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Australia Enable AI

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Australia Enable AI

  • Enable AI experiences – Take advantage of artificial intelligence-based tools to prioritize and automate routine tasks, detect major incidents, and surface insights.

  • ServiceNow product tiers – ServiceNow structures its products and packages in three tiers — Foundation, Advanced, and Prime. Each tier incorporates AI and builds progressively on the previous one with additional AI capabilities, agents, and governance tools.

  • ServiceNow AI implementation – Getting ready to implement ServiceNow Otto is more than just installing plugins—it’s about laying the groundwork for a seamless, intelligent experience across your workflows. Whether you’re enabling conversational catalogs, automating content generation, or enhancing user interactions, a few key steps will ensure your data is ready, your applications are prepared, and your organization’s AI policy is in alignment with your implementation.

    • ServiceNow AI organization and tools – The ServiceNow AI experience includes generative AI skills, agentic AI, and conversational user engagement layer. A good AI experience depends on quality data and a coherent AI policy that functions as its guiding star. AI Search capabilities and other tools help connect the pieces.

    • AI governance – As organizations increasingly adopt AI to drive efficiency, innovation, and customer experience, AI governance becomes essential to ensure responsible use, regulatory compliance, and alignment with enterprise goals. ServiceNow provides a comprehensive governance framework through key roles and applications that work together to manage AI across its life cycle.

    • Data readiness – High-quality data that is complete, accurate, and contextually relevant is the foundation for delivering precise, meaningful, and trustworthy AI responses.

    • Application readiness – ServiceNow Otto uses the ServiceNow AI Platform to deliver AI solutions. Ensure that your instance is ready to use AI capabilities by preparing Platform applications, such as Service Catalog and AI Search.

    • Knowledge Base readiness – The knowledge base enables ServiceNow Otto to deliver intelligent, accurate, and context-aware responses across AI Search, Q&A Genius Results, and other AI-powered experiences.

    • Service Catalog readiness – A well-structured Service Catalog is essential to supporting AI capabilities. As the backbone of many self-service workflows, the catalog enables ServiceNow Otto to interpret user requests accurately, present the right options, and reduce complexity for users.

    • AI Search readiness – ServiceNow Otto for AI Search is built directly on the foundation of ServiceNow AI Search. AI Search provides the underlying infrastructure that enables ServiceNow Otto to retrieve and rank enterprise content—such as knowledge articles, records, and documentation—based on relevance and access permissions.

    • ServiceNow Otto for Virtual Agent readiness – ServiceNow Otto for Virtual Agent has AI-driven capabilities that interpret natural language, guide users through complex tasks, and deliver high-confidence answers without relying on rigid keyword matching or manual configurations.

    • Solving installation and configuration issues – Use this checklist to address issues or gaps in your AI configuration.

  • AI assets – The AI experience in the ServiceNow AI Platform includes generative AI skills, AI agents, and AI agentic workflows. These components work alone or in combination to help achieve efficiencies and results on your instance.

    • AI agents library – All ServiceNow AI agents are listed. Depending on your license, not all AI agents may appear in your instance.

    • Accounts Payable Operations AI agents – The following AI agents are available for Accounts Payable Operations.

      • Inquiry resolution provider AI agent – This AI agent assists with invoice inquiries by providing resolution details based on the invoice and its related data. It is intended for use by Accounts Payable Operations agents handling invoice inquiries.

      • Recommend invoice owner AI agent – This AI agent intelligently analyzes exception work notes to predict the most likely business owner for an invoice. It collaborates with the Accounts Payable (AP) specialist — the person responsible for processing the invoice — by suggesting the predicted business owner or creating follow-up tasks to streamline ownership validation and resolution.

      • Email parser agent – This AI agent identifies and classifies actionable requests, and routes them to the appropriate workflows to create invoice cases.

      • Zero touch service desk agent – This AI agent investigates supplier invoice inquiry cases and generates a response for the supplier contact. Fulfillers can review its work in the Agentic Processes panel on the case.

    • AIOps and Leap AI agents – The following AI agents are available for AIOps and AIOps Leap.

      • Alert automation AI agent – This agent interprets free-text user requests related to alert automation and guides users to the appropriate automation experience. It detects whether the user intends to enrich alerts, group alerts, respond to alerts, or any combination of these actions.

      • Alert impact verification AI agent – This agent is responsible for eligibility validation for a given alert, to perform impact analysis on it.

      • Artifact creation agent AI agent – Assists in creating and viewing artifacts such as problem records, knowledge base articles, and playbooks for an automation opportunity. The agent checks for an automation opportunity number and invokes the artifact creation topic tool to generate the required artifacts.

      • Business and technology management service impact analysis AI agent – This agent calculates the business and technology management services impacted by a given alert.

      • Manage alerts AI agent – Responsible for generating reports for a given alert and storing a structured summary with key insights and recommended next steps in the alert record.

      • Service instance impact analysis AI agent – This agent performs alert impact analysis on service instances for a given alert.

      • User impact analysis AI Agent – This agent investigates incidents and cases related to the alerts and affected services.

      • Artifact creation agent AI agent – The Artifact creation agent helps users create and view artifacts for an automation opportunity, such as a problem record, a knowledge base article, or a playbook.

      • LEAP Ansible Agent AI agent – The LEAP Ansible agent finds the Ansible job template that best matches the problem behind an automation opportunity. It recommends the template to the user and saves it to the discovered job catalog.

      • LEAP Ansible Execution Agent AI agent – The LEAP Ansible Execution agent runs the Ansible playbooks mapped to each resolution step of an incident. It launches jobs through Ansible Automation Platform, prompts the user to complete unmapped steps manually, and reports job status on request.

      • LEAP KB Creation Agent AI agent – The LEAP KB Creation agent creates draft knowledge base articles from the resolution steps of a LEAP automation opportunity.

      • LEAP Problem Creation Agent AI agent – The LEAP Problem Creation agent creates problem records from the data of a LEAP automation opportunity.

    • AI Search AI agents – The following AI agents are available for AI Search.

      • AI Search Configuration AI agent – Responsible for managing the end-to-end process of formally configuring AI Search for ServiceNow internal tables. This agent is invoked whenever a user mentions ServiceNow internal tables in the context of AI Search.

    • Asset Management AI agents – The following AI agents are available for Asset Management.

      • Asset next best action AI agent – This AI agent validates task numbers, identifies task types, and routes workflows to the correct specialized agent for troubleshooting or repair. It processes one task at a time for accurate handling of hardware asset repair order tasks.

      • Asset sourcing AI agent – This AI agent helps source a requested item by generating and executing sourcing plans through consumption, transfer, and purchase. The agent creates plans autonomously or with user guidance, coordinates with external agents for transfer and purchase orders, and validates sourcing status throughout the process.

      • Evaluate asset AI agent – This AI agent helps troubleshoot asset-related issues using the Web Search tool. It guides users to select a resolution and completes the troubleshooting task on confirmation.

      • Purchase order creation AI agent – This AI agent is used to create a purchase order.

      • Repair asset AI agent – This AI agent assists users in resolving issues with assets that are in a defective or pending repair state. It specializes in identifying and suggesting appropriate repair steps by leveraging the Web Search tool. The agent guides users through the repair process, helps them select a suitable resolution, and automatically updates and completes the repair task upon confirmation.

      • Transfer order creation AI agent – This AI agent is used for creating a transfer order to source a requested item.

    • Care Team Operations AI agents – The following AI agents are available for Care Team Operations.

      • Care Team Operations case creation AI agent – This AI agent streamlines case creation by analyzing user conversations to accurately determine the service definition for each reported issue. It intelligently identifies multiple issues raised in a single conversation and creates separate cases with correct case type and service definition.

      • Care Team Operations case creation AI voice agent – This agent helps healthcare staff report and submit Care Team Operations (CTO) requests. Trigger this agent when a user mentions Care Team Operations, CTO, or asks to submit a care team request.

      • Care Team Operations case intake AI agent – This workflow guides users through submitting operational issues by prompting for a clear problem description and ensuring required details—such as location, requesting unit, asset tag, and priority—are captured. It intelligently detects and separates multiple requests in a single user utterance, summarizes each request with its details, and confirms accuracy with the user before proceeding.

    • CMDB AI agents – The following AI agents are available for CMDB.

    • Contract Management Pro AI agents – The following AI agents are available for Contract Management Pro.

      • Contract repository AI agent – This AI agent retrieves contract repository details for a specified record, including vendor information, contract model, and document attachment IDs. It also calculates the average lead time for similar contracts when vendor, supplier, or account data exists.

      • Search contracts AI agent – This AI agent helps legal professionals, procurement teams, sales executives, and compliance officers find and understand legal contracts through conversational search and Q&A. It classifies each query, retrieves matching contract metadata or content, and links directly to results, while declining requests to draft, modify, or legally interpret contracts.

    • Core Business Suite AI agents – The following AI agents are available for Core Business Suite.

      • CBS Bulk Upload AI Agent – The CBS Bulk Upload AI agent handles bulk record creation and updates for the Space, Supplier, and Supplier Contact tables.

    • Customer Service Management AI agents – The following AI agents are available for Customer Service Management.

      • Case action AI agent – This agent can preform actions for the customer, including case actions (Create a case task, Update work notes, etc.) and more.

      • Case creation AI agent – The Case creation AI agent is designed to automate and manage the end-to-end process of case creation. This agent should be invoked when a case needs to be created.

      • Case status AI voice agent – This AI agent helps users quickly retrieve and understand the status of their cases through a conversational voice interface.

      • Complaint case research plan AI agent – Analyzes “Under Review” complaint cases to identify previously attempted troubleshooting steps and recommends new solutions from similar closed cases and knowledge articles. Prioritizes relevant suggestions, presents them with source attribution, updates work notes, and creates case task plans based on successful resolution patterns. Filters redundant recommendations and ensures proper workflow integration within case management systems.

      • Complaint case triage AI agent – This agent is designed to analyze complaint cases after their creation, automatically deriving key classification and prioritization fields. It serves case handlers and support teams by providing reliable, data-driven insights to enhance case management efficiency.

      • Context validator AI agent – The context validator AI agent is designed to validate the record, analyze the sentiment, and get the list of intents from the input record. This agent should be invoked when the record content needs to be validated.

      • Create case AI voice agent – This voice agent enables users to log cases using voice input. It guides the user through a structured conversation to capture and validate all mandatory case details, creates the case record, and provides confirmation upon successful submission.

      • Customer insight AI agent – This agent is responsible for answering any questions related to the record. The information it can retrieve includes customer information (name, contact info, details, location, history, cases, and orders) and case information (next steps, actions, agent, notes, documents, tasks, and resolutions).

      • Document verification AI agent – The document verification AI agent verifies the documents on a record to determine if they match the documents listed for the case type. Additionally, they determine if any documents are missing or if document verification has failed. This agent should be invoked when documents need to be verified.

      • Duplicate identification AI agent – The duplicate identification AI agent identifies duplicate records.

      • Email response AI agent – The email response AI agent generates and sends an email response.

      • Entity extraction AI agent – The entity extraction AI agent extracts entities from emails and information from documents. It matches the entities with a list of approved fields.

      • Image to task plan template AI agent – This AI agent asks the user to upload an image, extracts the task dependencies from it, and returns the result as JSON.

      • Informational queries AI agent – The informational queries AI agent summarizes all informational answers for a record.

      • Issue identifier complaint case intake AI agent – This AI agent is used for product and location-based grievances. It helps with intakes, documentation, and processing.

      • RMA Assistant AI agent – This AI agent helps customers and human agents with RMA case creation and processing for product returns, repairs, and replacements.

      • Transactional queries AI agent – The transactional queries AI agent will update a record with any collected transactional values.

      • Triage cases planner AI agent – This AI agent triages cases. It takes appropriate actions based on the record’s sentiment and intents. It is intended for agents and end-users who need assistance in resolving queries efficiently, as well as assessing a record for possible case creation.

      • Triage cases wrap up AI agent – The triage cases wrap up AI agent finalizes the case triage process by synthesizing the execution and posting the summary to the record’s work notes.

      • Troubleshooting steps identification AI agent – An AI agent responsible for gathering required context, identifying troubleshooting steps by comparing with KBs, similar cases and standard operating manuals, and proposing additional troubleshooting steps.

    • Employee Experience AI agents – The following AI agents are available for Employee Experience.

    • Employee Service Management AI agents – The following AI agents are available for Employee Service Management.

      • Universal Request departmental ticket creator AI agent – Automates the routing of universal requests to appropriate departmental ticketing systems by analyzing department predictions and creating properly configured HR cases or IT incidents with all necessary field mappings and reference tracking.

    • Enterprise Architecture AI agents – The following AI agents are available for Enterprise Architecture.

      • Enterprise Architecture explorer for analysis and queries AI agent – The agent answers questions about Enterprise Architecture data based on Common Service Data Model (CSDM) 5.0 entities and their relationships. It covers business capabilities, business applications, services, infrastructure, integrations, value streams, AI systems, technology standards, and conformance.

      • Enterprise Architecture diagrams AI agent – The agent finds a business application by name or ID and generates a hierarchy diagram saved as a named architectural artifact. The agent also summarizes the generated diagram.

    • Field Service Management AI agents – The following AI agents are available for Field Service Management.

      • Create work order AI agent – This AI agent creates work orders using text descriptions and image.

      • Parts audit AI agent – This AI agent assists technicians in conducting thorough parts validation only for work order tasks. It streamlines the process of verifying and reporting parts usage, ensuring accuracy and accountability.

      • Shift and schedule management AI agent – This AI agent helps managers create, review, update, and delete shifts and schedules, and assign technicians to them. It suggests default shift details, finds technicians by skill or name, and asks the manager to confirm one complete plan before making changes. Managers can also open shifts for technician signup.

    • Financial Services Operations AI Agents – The following AI agents are available for Financial Services Operations.

      • ACH dispute return recommendation AI agent – The AI agent guides the human agent through a structured decision-making process to determine the best action for an ACH dispute return task. The ACH dispute return recommendation AI agent analyzes disputed transactions based on merchant analysis and Nacha eligibility. The agent recommends actions based on historical data, and applies predefined rules when data is limited.

      • Banking CSR customer insights AI agent – The AI agent answers a customer service representative’s questions about a banking customer, presenting account, transaction, and case information in a clear, structured format so the representative can quickly explore relevant customer data.

      • Banking CSR support AI agent – The AI agent acts as a copilot for a customer service representative on a live banking call or chat, retrieving account, transaction, balance, credit, loan, and policy information in real time and presenting it in a clear, conversational format so the representative can respond quickly.

      • Dispute communication AI agent – The agent drafts a customer email about a card dispute outcome. It chooses the message template based on the dispute’s final action, fills in the relevant transaction and merchant details, and adjusts the tone to match the template’s intended message.

      • Friendly fraud AI agent – The agent guides the human agent through resolving a friendly fraud dispute. It recommends an action based on published guidance and similar past overrides, then supports the agent’s decision with real-time context to keep resolutions accurate and compliant.

      • Insurance CSR customer insights AI agent – The agent acts as a copilot for a customer service representative (CSR), answering natural-language questions about a single customer’s insurance policies, coverages, claims, billing, and servicing cases, as well as general product and procedural guidance, and presents structured, context-aware answers with follow-up options.

      • Insurance CSR support AI agent – The agent gives a customer service representative (CSR) real-time answers during a live insurance call, either from a typed question or from the latest call transcript, retrieving the customer’s policy, coverage, claim, and billing data or knowledge-base guidance so the CSR can respond quickly without switching screens.

      • Merchant analysis for disputes AI agent – The agent evaluates the merchant behind a disputed transaction for credibility. It researches the merchant’s reputation and regulatory history, weighs the evidence against a set of rules, and recommends whether the merchant is credible, with a rationale the human agent can accept or override.

      • Nacha operating guidelines check AI agent – The agent determines whether a return code assigned to a disputed transaction is eligible for filing. It checks the case against NACHA operating guidelines’ time-frame and documentation requirements and presents its eligibility decision and reasoning to the human agent for confirmation.

    • Governance, Risk, and Compliance AI agents – The following AI agents are available for Governance, Risk, and Compliance.

      • Control objective change AI agent – This AI agent acts as a Compliance Manager that validates if the description and supplemental guidance of a given control objective need updates based on the associated citations. The AI agent recommends new values if changes are required, and updates the control objective record after getting confirmation from the user.

      • Issue action plan AI agent – This AI agent identifies the optimal steps for resolving an issue by referencing the past similar issue details. It presents a step-by-step summarized action plan to the user.

      • Remediation tasks AI agent – This AI agent identifies the remediation tasks for resolving an issue by referencing the action plan field on the issue record.

      • Report a GRC issue AI agent – This AI agent enriches the given issue description with additional information by looking at historical data and using GRC context. It analyzes, extracts key information (such as issue type, date of occurrence, and so on), and creates a GRC issue.

    • Hardware Asset Management AI agents – The following AI agents are available for Hardware Asset Management.

    • Impact AI agents – The following AI agents are available for Impact.

      • Code fix AI agent – This AI Agent automates the process of analyzing, suggesting, and implementing fixes for code violations or issues detected within code repositories or running scripts. It leverages LLM-driven suggestions while keeping the user in control of approval and refinement.

    • Integration Hub AI agents – The following AI agents are available for Integration Hub.

    • IT Operations Management AI agents – The following AI agents are available for IT Operations Management.

      • Analyze potential impact AI agent – This agent analyzes the potential business and operational impact of a proposed change by identifying affected servers and services. The agent reviews the change request, performs impact analysis, and documents findings in work notes to support informed decision-making and effective mitigation strategies.

      • AWS CloudWatch API AI agent – This AI agent investigates AWS CloudWatch alarm firings end to end by querying alarm configuration, metric time series, log groups, ML anomaly detectors, and Logs Insights. It then synthesizes findings into a structured root cause analysis report with metric analysis, log evidence, and correlated service impact.

      • AWS CloudWatch MCP server AI agent – This AI agent automates AWS CloudWatch alert investigations by analyzing alarm details, querying affected resources, and retrieving CloudWatch metrics and logs across multiple strategies. It provides clear summaries with root cause analysis and actionable recommendations for resolution.

      • Azure Monitor MCP AI agent – This AI agent investigates Azure Monitor alerts by querying Log Analytics workspaces with KQL, retrieving platform metrics, and checking activity logs and resource health through Azure MCP tools. It returns structured findings to the parent agent.

      • CSDM business application to infrastructure AI specialist – This AI specialist finds the best matching discovered service for each business application and creates the standard CSDM “Uses::Used by” relationship in the CI relationships table [cmdb_rel_ci].

      • Datadog APM MCP server AI agent – This AI agent queries Datadog APM observability data using the full suite of Datadog MCP server tools. It can answer questions about service health, distributed traces, triggered monitors, log analysis, incidents, SLO compliance, deployment events, and service dependencies.

      • Dynatrace MCP server AI agent – This agent investigates Dynatrace alerts by retrieving alert impact summaries and querying the Dynatrace API for problem details, entity enrichment, log analysis, and root cause theories.

      • Gemini Cloud Assist A2A investigation AI agent – This AI agent investigates Google Cloud alerts by driving a Gemini Cloud Assist investigation through Google’s A2A investigation endpoint. It starts an investigation, polls task status, and retrieves the structured investigation result with root-cause hypotheses and next steps.

      • Kentik analysis AI agent – This AI agent fetches the incident insights report for a Kentik alert.

      • LogicMonitor API AI agent – This AI agent investigates LogicMonitor alerts and monitored entities, assesses entity health, and recommends next steps.

      • Network SME AI agent – This AI agent investigates network issues and recommends next steps.

      • New Relic MCP server AI agent – This AI agent queries and interprets observability data from New Relic using the full suite of New Relic MCP server tools.

      • Prometheus API AI agent – This AI agent analyzes and investigates Prometheus alerts and answers general Prometheus questions, surfacing infrastructure metrics through instant and range queries.

      • Service map creation AI specialist – This AI specialist discovers service infrastructure from application service candidates using ML analysis, and then it maps and persists the full service topology in CMDB.

      • SolarWinds analysis AI agent – This AI agent fetches the details related to a SolarWinds alert by executing SWQL queries against SolarWinds.

      • Splunk MCP server AI agent – This AI agent investigates Splunk Cloud and compatible Splunk Enterprise alerts by querying Splunk platform data. It doesn’t cover Splunk Observability Cloud.

      • SRE investigate AI agent – This AI agent coordinates investigation activities across multiple observability platforms by orchestrating tool-specific agents. It correlates findings, identifies probable root causes, and synthesizes comprehensive investigation reports.

      • ThousandEyes MCP server AI agent – This AI agent investigates ThousandEyes tests by analyzing metrics, anomalies, network events, and outages. It returns probable causes and recommended next steps.

    • IT Service Management AI agents – The following AI agents are available for IT Service Management.

      • Additional incident context AI agent – This AI agent retrieves information beyond the incident itself and presents the output in a readable format. This agent is strictly read-only; it can only retrieve and present information. It cannot update, modify, or perform any write operations on any records (such as adding comments, changing fields, reassigning, resolving, or closing incidents).

      • Agent client collector (ACC) diagnostic AI agent – This agent assists users in resolving ACC errors by providing suggestions based on the error codes found in the ACC Error Reports. It aims to help users resolve errors efficiently and effectively.

      • Categorize ITSM incident AI agent – This AI agent assigns the appropriate category and subcategory to an incident.

      • Create change request AI agent (autonomous) – This AI agent creates structured change requests from conversational input by autonomously selecting the appropriate change model and template.

      • Change CI suggestion AI agent – This AI agent recommends a list of relevant configuration items (CIs) that can be added as Affected CIs to the current change request.

      • Change CI suggestion AI agent (latest) – This AI agent autonomously identifies and populates both the primary configuration item (CI) and affected configuration items on a change request without requiring multiple user interactions.

      • Change conflict assessor AI agent – This AI agent can assess conflicts for changes. This includes checking mandatory fields and trigger conflict detection, and updating worknotes.

      • Change outage assistant AI agent – This AI agent creates outage records for the change request based on the impacted CIs.

      • Change quality assessor AI agent – This AI agent provides suggestions to improve inputs for a change form.

      • Change request plans AI agent – This AI agent helps change owners and approvers by drafting high-quality, context-aware change request content.

      • Change request plans AI agent (autonomous) – This AI agent autonomously drafts change plan fields during the readiness phase. Field population is governed by a resolved change policy to ensure consistent behavior without requiring user input.

      • Change template suggestion AI agent (autonomous) – This AI agent identifies the most relevant change template and model for new change requests by analyzing request details and comparing them against available templates and historical data.

      • Classify service and CI AI agent – This AI agent assigns the appropriate service and offering to an incident. It can also assign the appropriate configuration item.

      • Create incident AI agent – This AI agent manages the end-to-end process of formally logging IT support requests, specifically for user requests like “create an incident,” “raise an incident,” “open a new incident,” “open an IT ticket,” or “raise an IT support ticket.”

      • Use the Create incident AI agent – This AI agent manages the end-to-end process of formally logging IT support requests, specifically for user requests like “create an incident,” “raise an incident,” “open a new incident,” “open an IT ticket,” or “raise an IT support ticket.”

      • Create incident AI voice agent – This AI agent creates new incidents when the user explicitly mentions they want to create a new incident or report a new issue.

      • DEX diagnosis AI agent – This AI agent specializes in structured root cause diagnosis of device and application performance issues through a step-by-step process. This agent aggregates device health metrics, event logs and historical incident data, then systematically evaluates each source to isolate relevant evidence.

      • DEX remediation trigger AI agent – This AI agent receives a resolution plan, checks for remedial actions, and executes the supported ones on end-user devices (Windows OS and MacOS endpoints) to resolve common device and application issues. Remedial actions are concrete, executable fix steps such as restarting services, clearing cache, or cleaning up temporary files.

      • DEX resolution plan AI agent – This AI agent generates a resolution plan for the given issue based strictly on the root cause provided. The agent ensures all suggested steps are evidence-based, actionable, and aligned with the diagnosed root cause. No resolution is proposed outside these defined sources.

      • Incident category configuration AI agent – This AI agent orchestrates the complete lifecycle of incident category and subcategory configuration for the Choices [sys_choice] table. It supports three interaction modes: guided conversation, file upload (CSV/XLSX), and industry-based recommendations.

      • Incident context AI agent – This AI agent helps users answer questions related to a given incident using information from the incident record and its related records. This agent is strictly read-only — it can only retrieve and present information.

      • Incident knowledge article AI agent – This AI agent can resolve incidents by finding existing similar knowledge articles and attaching them to the incident. It can also create a knowledge article for the incident.

      • Incident known error article AI agent – This AI agent can find similar known error articles for an incident and attach them to the incident. It can also resolve the incident.

      • Incident resolution details AI agent – This AI agent suggests resolution notes for an incident.

      • Incident routing configuration AI agent – This AI agent creates, modifies, and deactivates assignment rules for incident routing.

      • ITSM incident resolution plan investigation AI agent – This AI agent gets incident details, reviews similar incidents and knowledge articles, and then generates an incident resolution plan.

      • Link major incident or problem AI Agent – This AI agent automatically identifies and links the most relevant major incident or problem with an incident.

      • Manage ticket AI voice agent – This AI voice agent assists users with managing their active incidents and requested items.

      • Microsoft 365 group membership AI agent – The agent helps users add or remove members of Microsoft 365 distribution list groups. It finds the group, validates the people to add or remove, confirms the action with the user, and records the request in an incident.

      • Password reset AI voice agent – This AI voice agent helps users reset their password during phone-based interactions.

      • Post incident review AI agent – This AI agent generates a post-incident review report for the user to review and revise after a major incident report.

      • Request catalog item AI voice agent – This AI voice agent assists users in finding and delivering catalog items. If the item is not categorized as software, then the catalog link is sent through approved channels such as email or SMS. If the item is software, this agent will help create the request.

      • Schedule change request AI agent – This AI agent schedules a change request.

      • Standard change template proposal AI agent – Provided with a change request, this AI agent searches for similar changes and returns a list of similar changes to use for a proposal record.

      • Standard change template recommender AI agent – This AI agent helps users create and submit change requests. Invoke it whenever the user asks to log, create, submit, initiate, or help with a change record of any type (standard, routine, scheduled, maintenance, infrastructure, software, database, security, or any changes specific to IT service management).

      • Submit account unlock catalog AI voice agent – This AI voice agent helps users unlock accounts by invoking a tool call to submit an account-unlock catalog request.

      • Troubleshoot Outlook issue AI voice agent – This AI voice agent troubleshoots Microsoft Outlook issues. It also sends a troubleshooting article link to the user by email when requested.

    • Legal Service Delivery AI agents – The following AI agents are available for Legal Service Delivery.

    • Operational Sustainability Management AI agents – The following AI agents are available for Operational Sustainability Management.

      • Calculation creation AI agent – This AI agent creates a calculated metric definition record using the formula passed in the input.

      • Calculation operand AI agent – This AI agent identifies and returns relevant metric definitions and emission factors from existing sources. It replaces generic references with precise metric definitions and emission factor names in the input formula.

    • Operational Technology Manager AI agents – The following AI agents are available for Operational Technology Manager.

      • OT Excel import task AI agent – The OT Excel import task AI agent imports Operational Technology (OT) device data from a spreadsheet into the Configuration Management Database (CMDB). The agent validates the imported records, creates remediation tasks for invalid records, and imports the valid records.

    • Operational Technology Service Management AI agents – The following AI agents are available for Operational Technology Service Management.

    • Sales CRM AI agents – The following AI agents are available for Sales Customer Relationship Management (Sales CRM).

      • Sales Automation AI agents – The following AI agents are available for Sales Automation.

      • Telco order fulfilment template creation AI agent – This Sales Customer Relationship Management AI agent generates a task plan template using the provided specification and dependency JSON.

      • Lead outreach AI agent – This AI agent sends outreach and follow-up emails to new leads on behalf of sales agents. It selects the email template based on how many emails the lead has already received, and disqualifies the lead when three emails go unanswered.

      • Appointment management AI agent – This AI agent manages appointments with leads on behalf of sales agents. It reads lead emails, checks the lead owner’s availability, and either books an appointment or proposes two open time slots. It also cancels appointments when a lead asks and sends a confirmation email.

      • Lead disinterest AI agent – This AI agent handles lead disqualification and opt-out requests for sales agents. After the sales agent confirms, it cancels the lead’s upcoming appointments, closes the related tasks, and disqualifies the lead.

      • Inbound email AI agent – This AI agent analyzes the latest email that a lead sends in response to outreach and helps sales agents decide how to move forward. It classifies the response and then books a demo, disqualifies the lead, cancels the appointment, or drafts answers to the lead’s questions.

      • Opportunity AI agent – This AI agent helps sales agents read, create, update, and summarize opportunities and their related records, such as accounts, contacts, touchpoints, opportunity tasks, line items, and meetings. It previews changes to opportunities, accounts, contacts, and line items and makes them only after the user approves.

      • Quote AI agent – This AI agent helps sales agents create, update, and finalize quotes. It validates products against the catalog, adds and configures line items, applies discounts, and generates the quote document. When started from an opportunity, it reads the opportunity line items and notes to build the quote without manual input.

      • Order Management AI agents – The following AI agents are available for Order Management.

      • Move order voice AI agent – This Sales Customer Relationship Management voice agent helps users relocate a telecom service to a new address by validating the consumer, verifying serviceability at the target location, and creating a move order.

      • Order enrichment AI agent – This Sales Customer Relationship Management agent identifies enrichment tasks for order line items and their children using historic data, confirms the task list with the user, and creates them.

      • Order fallout AI Agent – This Sales Customer Relationship Management agent analyzes order task comments and work notes to detect issues matching known fallout types, creates fallout records for any matches, and summarizes the results.

      • Order fulfillment AI agent – This Sales Customer Relationship Management agent identifies fulfillment tasks for a domain order by checking historic tasks from similar orders, confirms the task list with the user, and creates them.

      • Invoice dispute AI voice agent – This AI voice agent helps customers resolve discrepancies between invoiced quantities and received quantities by creating dispute cases.

      • Invoice dispute intake assistant AI agent – This AI agent helps requesters identify the correct invoice, confirm dispute details, and submit invoice disputes related to quantity issues. It guides users step by step through invoice identification, validation, and dispute submission to ensure a smooth and accurate experience. The agent also analyzes the sentiment of the user throughout the conversation.

      • Invoice dispute support assistant AI agent – This agent helps users efficiently identify, review, and answer questions related to invoice disputes. It validates information, maintains data integrity, and answers questions regarding the invoice case.

      • Manage order operations AI agent – This AI agent helps customers request faster delivery of an order.

      • Order capture AI agent – This AI agent helps order-management agents investigate, understand, and update customer orders in any form, whether direct (“apply 10% discount to all lines”), corrective (“this address is wrong, fix it”), bulk (“set quantity to 50 for every item”), diagnostic (“why is this order delayed?”), or follow-up (“undo the last change”).

      • Order exception AI voice agent – This AI voice agent helps customers request faster delivery of an order.

      • CPQ AI agents – The following AI agents are available for Configure, Price, Quote (CPQ).

      • PRM AI agents – The following AI agents are available for Partner Relationship Management (PRM).

    • Service Exchange AI agents – The following AI agents are available for Service Exchange.

      • Registration error monitor AI agent – This AI agent checks a registration number and connection against the Service Exchange error table, and then provides a detailed summary of any reported Service Exchange errors. This summary helps users or other AI agents make informed decisions and take appropriate action to resolve registration issues.

      • Registration initiator AI agent – This AI agent initiates the registration process and manages its early stages, including creating the registration record.

      • Registration monitor AI agent – This AI agent monitors registration task state changes, validates system configurations, and posts audit results to work notes.

    • Security Incident Response AI agents – The following AI agents are available for Security Incident Response.

      • EDR AI agent – This Security Incident Response agent retrieves host details or isolates a host associated with a security incident.

      • Exchange online integration handling AI agent – This Security Incident Response agent searches for and deletes emails related to a security incident.

      • Observable analysis AI agent – This Security Incident Response agent performs threat lookups, observable enrichment, sighting searches, and block requests against observables in a security incident.

      • Security incident activities handling AI agent – This Security Incident Response agent manages security incident core activities, ensuring efficient and effective handling of incidents, problems, change requests, and security incident response tasks. It is intended for use by security incident management teams.

      • Security incident resolution AI agent – This Security Incident Response agent walks the user through resolving a security incident by generating and executing a resolution plan.

      • Security incident retrieval AI agent – This Security Incident Response agent searches for security incidents using filters and optionally summarizes individual incidents.

      • Security incident shift handover AI agent – This Security Incident Response agent adds a security incident to a shift handover report, walking the user through each section for review.

      • Security incident wrap up generator AI agent – This Security Incident Response agent can close a security incident by performing all pre-closure validations. It can also generate close notes, generate post incident analysis, post work note, and update the state of the security incident to close it.

      • Security metrics analysis AI agent – This Security Incident Response agent calculates and analyzes security incident response metrics for an individual analyst or a team over a specified time range.

    • ServiceNow AI Platform AI agents – The following AI agents are available for ServiceNow AI Platform.

      • AI Analytics Q and A AI agent – This AI agent answers natural language questions about AI Analytics. It covers KPI definitions, dashboard widgets, source tables, data flows, deflection log states and scenarios, the deflection and metric frameworks, and ServiceNow platform scripts.

      • AICT security analyzer AI agent – This AI agent generates context for a detected AI security event by aggregating asset, prompt, cross-threat, and correlation data. It then computes a confidence score from detection strength, event risk, and authorization signals.

      • AICT AI asset description auto-populator AI agent – This AI agent generates and applies descriptions to AI assets using vendor, model, and manufacturer metadata.

      • AICT deactivate dormant asset AI agent – This AI agent analyzes and retires dormant AI assets by running comprehensive analysis and creating offboarding change requests.

      • AICT life cycle task reminder AI agent – This AI agent sends automated email reminders to task owners for overdue and dormant life cycle tasks on high-priority AI assets.

      • AICT outcome ranker AI agent – This AI agent ranks AI insight outcomes by relevance to each user persona based on their role and responsibilities.

      • Approval assistance AI agent – This AI agent handles all queries related to approval records for the current user.

      • Using the approval assistance AI agent – This AI agent handles all queries related to approval records for the current user.

      • Decomposition AI agent – This AI agent takes resolution steps as input, and then analyzes and breaks down each resolution step into a clear, structured set of smaller, actionable substeps. It then creates records based on user preference. This enables precise execution, improves task clarity, and supports automation and auditability of resolution workflows.

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      • Error analysis and remediation AI agent – This AI agent is helps admins and operators investigate Error Framework error codes, analyze root causes, and execute preconfigured remediation actions.

      • Explain SLA AI agent – This agent explains Service Level Agreement (SLA) details to the user based on the provided task number. It retrieves the matching SLA using timer configurations, along with SLA breakdown details, task SLA information, and an SLA timeline summary.

      • Image processor AI agent – This AI agent lets users upload image attachments and create task records by analyzing them. I

      • Issue readiness AI agent – This AI agent scans the record fields and context of a ticket so it can determine if it is ready for a fulfiller to work on it.

      • Using the issue readiness AI agent – This AI agent scans the record fields and context of a ticket so it can determine if it is ready for a fulfiller to work on it.

      • Issue trend analysis AI agent – This AI agent analyzes grouped task data from cases, incidents, problems, or other tables to identify recurring issues and root causes.

      • Model provider configuration AI agent – This AI Agent helps administrators understand whether their configured third-party LLM providers use OEM (managed by ServiceNow) or bring-your-own-key integration.

      • Next action recommendation AI agent – This AI agent identifies the optimal steps for resolving tasks by reviewing similar record details, related list record details, relevant knowledge articles, and web search results.

      • Prioritize work AI agent – This AI agent retrieves user-assigned tasks, scoring them by priority using metadata and Service Level Agreement (SLA) data.

      • Problems investigation AI agent – This AI agent specializes in root cause identification, impact assessment, and resolution planning. It analyzes problem details, associated incidents, similar incidents, change requests, and affected configuration items to provide a structured and comprehensive analysis.

      • Record field value prediction AI agent – This AI agent can predict fields for an incoming record.

      • Record management AI agent – This AI agent can fetch, update, create a record, link a knowledge base (KB) article, and copy an attachment to a record with details provided by the user.

      • Request status AI agent – This AI agent answers inquiries about existing tickets that were created by or are currently opened by the logged-in user. It should be triggered only when the user explicitly references an existing ticket (for example, by asking for ticket status, updates, comments, or history).

      • Using the request status AI agent – This AI agent answers inquiries about existing tickets that were created by or are currently opened by the logged-in user. It should be triggered only when the user explicitly references an existing ticket (for example, by asking for ticket status, updates, comments, or history).

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      • ServiceNow record navigator AI agent – This AI agent surfaces ServiceNow records as clickable navigation views. It handles retrieval-style queries (“show me open P1 incidents”) and navigation-style commands (“take me to changes assigned to David”).

      • Smart documents AI agent – This Document Management AI agent help users interact with document content by providing summaries, FAQs, and answers to their specific questions. It only works within the ServiceNow Document Viewer.

      • Survey analysis AI agent – This AI agent extracts and validates date ranges from user input, including natural language expressions. It analyzes survey data (such as questions, responses, and triggered records) to extract key metrics, identify trends, and deliver actionable insights for informed decision-making.

      • Survey filling data collection AI agent – This AI agent collects data related to the case, incident, or request and the survey questions raised to collect feedback on it.

      • Survey response suggestion AI agent – This AI agent suggests answers to questions in the survey by using details from case, incident, or request.

      • Troubleshooting AI agent – This AI agent triages an Automated Test Framework (ATF) test failure.

      • Update work plan AI agent – This AI agent receives structured plan data from the orchestrator, constructs a valid JSON payload, and updates the finalized work plan in the database.

      • Web automation AI agent – This AI agent executes automated actions across any web page, enabling form filling, clicking, data extraction, and navigation tasks.

      • Web research and recommendation AI agent – This AI agent resolves issues by analyzing the problem and generating resolution steps using web search tools.

      • Work allocator AI Agent – This AI agent automatically fetches backlog tickets, evaluates agent workloads and performance metrics, and assigns tickets to agents in a way that optimizes distribution and operational efficiency.

      • Content Understanding AI agents – The following AI agents are available for Content Understanding.

      • Content insights AI agent – This AI agent processes and analyazes content in the Content Understanding application.

      • Knowledge Management AI agents – The following AI agents are available for Knowledge Management.

      • KB content consolidation AI agent – This AI agent detects duplicate articles in Knowledge Management.

      • KB content creation AI agent – This AI agent generates a knowledge article from a task record.

      • MID Server AI agents – The following AI agents are available for MID Server.

      • MID common AI agent – This AI agent helps users and support personnel to identify, analyze, and resolve MID Server issues.

      • MID keystore AI agent – This AI agent helps IT administrators diagnose and resolve MID Server keystore problems and manage MID Server health.

      • MID network AI agent – This AI agent helps administrators and support personnel diagnose and resolve MID Server connectivity issues in a ServiceNow instance.

      • MID upgrade AI agent – This AI agent helps admins diagnose and resolve ‘Upgrade Failed’ issues on MID Servers by automating the identification, script deployment, error collection, and resolution workflow.

      • Notifications AI agents – The following AI agents are available for Notifications.

      • Email generator AI agent – This AI agent composes and sends emails (including new messages or replies) based on the provided context.

      • Intent executor AI agent – This AI agent executes admin-defined actions, such as workflows or creating reply responses based on matched intent.

      • Intent identification AI agent – This AI agent analyzes and identifies the input email body intent. A list of admin-configured intents are compared with the input email body for a potential match.

      • Notification AI agent – This AI agent creates or edits notification records through natural‑language interaction.

      • Notification content AI agent – This AI agent prepares email notification templates and layouts. It begins by analyzing user input to understand the notification’s content requirements and design expectations. Before generating any content, the agent searches for similar existing templates and presents the results to the user.

      • Setup Hub AI agents – The following AI agents are available for Setup Hub.

      • Group and roles assignment AI agent – This ServiceNow Otto for Setup AI agent provisions access by creating groups, adding users to a group, assigning roles to a group, and adding users to a role.

      • IA operational data workflow AI agent – This ServiceNow Otto for Setup AI agent automates the configuration of data imports by handling the creation of data sources, import sets, and transform maps.

      • Implementation plan executor AI agent – This ServiceNow Otto for Setup AI agent is responsible for executing the implementation plan, carrying out each defined step, and ensuring the plan is completed as designed.

      • Implementation plan generator AI agent – This ServiceNow Otto for Setup AI agent collects user input by asking configured questions, analyzing the full conversation to derive a precise technical intent, and then generating a step‑by‑step implementation plan.

      • Implementation plan manager AI agent – The implementation plan manager agent orchestrates an implementation by coordinating the plan generator and plan executor agents. It helps to ensure that your technical intent is converted into a clear plan and executed safely and correctly.

      • Initialize IA orchestrator AI agent – This ServiceNow Otto for Setup agent automatically triggers the appropriate Virtual Agent topic to streamline setup and onboarding for users on your ServiceNow instance.

      • SLA management AI Agent – This ServiceNow Otto for Setup agent enables administrators to create and edit SLA definitions through a conversational interface. The agent guides you through SLA configuration, including setting target type (Resolution/Response), duration, table, schedule, start/pause/stop conditions, and flow associations.

      • SSO configuration AI agent – This ServiceNow Otto for Setup AI agent guides administrators through secure Single Sign-On (SSO) setup for ServiceNow using SAML or OpenID Connect (OIDC). The agent collects and validates identity provider details, creates authentication configurations, and guides users through review, testing, and activation.

      • Update set AI agent – This ServiceNow Otto for Setup AI agent helps administrators and developers upload, review, and commit XML update sets efficiently.

    • ServiceNow Otto for Creator AI agents – The following AI agents are available for ServiceNow Otto for Creator.

      • Build Agent – This AI agent enables developers to create, edit, and deploy full-stack ServiceNow applications to update sets that encompass both user interface and back-end components.

      • Catalog AI agent – This ServiceNow Otto for Creator agent gathers, validates, and tracks all required inputs for a given Service Catalog item.

      • Module creator AI agent – This ServiceNow Otto for Creator agent Converts natural language requests into structured JSON definitions for ServiceNow application navigator modules.

      • Playbook activity context AI agent – This ServiceNow Otto for Creator agent helps users complete ServiceNow forms by fetching form fields, automatically matching pre-filled field values, and prompting for any missing required information. The agent saves the summary of what it has done.

      • Playbook data gathering and processing AI agent – This ServiceNow Otto for Creator agent is a assistant that consolidates data gathering, processing, and slot-filling capabilities. It handles end-to-end playbook execution using a broad toolkit of retrieval, reasoning, and action-oriented functions. The agent also helps with tasks related to processing of files attached to instance records.

      • Release lifecycle documentation AI agent – This ServiceNow Otto for Creator agent generates release notes and update set descriptions in natural, human-friendly language for business stakeholders, within a maximum 4000 character limit.

      • Theme Builder AI agent – This ServiceNow Otto for Creator agent assists in creating and managing themes for user interfaces. It is intended for use by UI/UX designers, developers, and anyone responsible for maintaining the visual consistency of their organization’s digital properties.

      • UI builder AI agent – This agent helps users edit pages and answer questions within the ServiceNow UI Builder editor.

    • ServiceNow Vault AI agents – The following AI agents are available for ServiceNow Vault.

      • Access Observer configuration manager AI agent – This ServiceNow Vault agent helps users complete tasks related to Access Observer configuration manager.

      • Access validation for privacy advanced configurations AI agent – This ServiceNow Vault agent checks a user’s access to manage privacy policy advanced configuration.

      • Create Data Privacy advanced configuration AI agent – This ServiceNow Vault agent helps users complete tasks related to creating data privacy advanced configuration agents.

      • Custom app recommendations AI agent – This ServiceNow Vault agent lists the custom apps on the instance, and shows recommendations along with the available protections for the selected apps.

      • Data Discovery policy and job manager AI agent – This ServiceNow Vault agent checks whether a given list of tables supports data discovery scanning. The agent then creates data discovery job policies using these tables, and creates discovery job policies and schedules and data discovery jobs using the created policy.

      • Data Discovery recommendation AI agent – This ServiceNow Vault agent provides job info, findings, attributes, and related details for a list of tables provided. The agent also returns scan recommendations you can use to create policy and schedule the discovery job by another agent.

      • Data Discovery util AI agent – This ServiceNow Vault agent assists data stewards in scheduling and managing data discovery jobs by verifying required roles, entitlements, and subscriptions. The agent categorizes discovered data patterns into compliance groups to support regulatory and security requirements. It also help user to add tags to data patterns on demand.

      • Data Discovery workflow lookup AI agent – This ServiceNow Vault agent receives provide recommendation for different tables associated with workflows, and identifies what kind of sensitive data can present in those tables such as PII,PFI,PCI,PHI,FCI.

      • Data pattern list AI agent – This ServiceNow Vault agent helps users associate data patterns to an data privacy policy configuration. It retrieves the list of available data patterns, captures the user’s selections, extracts the related sys_ids, assigns data patterns to data privacy policy configuration and validates it.

      • DP record selector agent AI agent – This ServiceNow Vault agent fetches data using tools or topics to display list of choices related to the action.

      • Edit Data Privacy advanced configuration mapping AI agent – This ServiceNow Vault agent helps users complete tasks related to edit data privacy advanced configuration mapping agent.

      • Edit privacy advanced configuration AI agent – This ServiceNow Vault agent helps users complete tasks related to edit privacy advanced configuration agent.

      • Summarize Access Observer logs AI agent – This ServiceNow Vault agent summarizes access observer logs and provides detailed breakdowns by caller type, users, and roles for specific table and column combinations.

      • Vault crypto module manager AI agent – This ServiceNow Vault agent manages the Vault crypto module configuration and access policies. The agent handles encrypted field configurations for fields, and manages module access policies for roles.

    • Software Asset Management AI agents – The following AI agents are available for Software Asset Management.

    • Sourcing and Procurement Operations AI agents – The following AI agents are available for Sourcing and Procurement Operations.

      • Procurement inquiry analysis AI agent – This Sourcing and Procurement Operations agent is an educational assistant for procurement and sourcing terminology questions. The agent scans policy manuals and help articles to deliver plain-language explanations of purchase orders, requisitions, sourcing requests, and procurement cases. It can connect users to a live agent if knowledge articles don’t have sufficient information.

      • Procurement product recommendation AI agent – This Sourcing and Procurement Operations agent can browse the catalog and create orders. The agent matches user requirements against available items, then routes to submission forms based on vendor status and dollar thresholds. Accepts and processes uploaded quote documents or SOW (Statement of Work) files to extract line items and pricing.

      • Procurement request tracking AI agent – This Sourcing and Procurement Operations agent provides a summary of a procurement request. It can provide current status and next steps for the request.

      • Spend categorization AI agent – This Sourcing and Procurement Operations agent predicts and populates product and spend categories in purchase requisition lines, reducing manual effort and improving data accuracy.

      • Contract Optimization Opportunity Finder agent – This Sourcing and Procurement Operations agent analyzes supplier contracts to identify and create savings opportunities by analyzing price escalations, renewal caps, and payment terms. It can also answer follow-up questions about spend data, contracts, savings opportunities, pipeline projects, and savings calculations.

      • Spend Optimization Opportunity Finder agent – This Sourcing and Procurement Operations agent compares on-contract and off-contract purchase order pricing to identify savings opportunities, quantifying the potential savings from redirecting off-contract spend to existing supplier contracts. It can also answer follow-up questions about spend data, contracts, savings opportunities, pipeline projects, and savings calculations.

      • Supplier Optimization Opportunity Finder agent – This Sourcing and Procurement Operations agent analyzes spend data to identify supplier optimization opportunities across non-preferred supplier spend, unconsolidated spend, spend fragmentation, and supplier performance and risk. It can also answer follow-up questions about spend data, contracts, savings opportunities, pipeline projects, and savings calculations.

    • Strategic Portfolio Management AI agents – The following AI agents are available for Strategic Portfolio Management.

      • Project eligibility analysis AI agent – This Strategic Portfolio Management agent performs checks for project and project task related requests. The agent analyzes requests to check impact on a project due to project task. It checks if the project summary email skill is enabled for the instance and whether the skill was enabled for a given project.

      • Project summarization AI agent – This Strategic Portfolio Management agent assists project managers to get a summary of the project. The agent helps in keeping their projects on track, by providing actionable and proactive updates on critical changes to projects. It also generates the project summaries.

      • Validate the work item and generate stories AI agent – This Strategic Portfolio Management agent transforms Enterprise Agile Planning work items (epics and features) into a set of well-formed, actionable user stories. It evaluates work items to verify that it contains sufficient information, and guides users through a structured validation process.

    • Supplier Lifecycle Operations AI agents – The following AI agents are available for Supplier Lifecycle Operations.

      • Supplier approval AI agent – This Supplier Lifecycle Operations agent performs the supplier activation or supplier rejection tasks during supplier approval process.

      • Supplier data steward AI agent – This Supplier Lifecycle Operations agent manages supplier onboarding by verifying details, detecting duplicates, presenting scorecards for review, creating records, and updating case status.

      • Supplier data validator AI agent – This Supplier Lifecycle Operations agent takes a supplier case number and initiates the supplier banking task validation process. The validation process compares the bank account proof against the banking details provided by the supplier contact.

      • Supplier document strategy generator agent – This Supplier Lifecycle Operations agent assists with supplier onboarding by retrieving the supplier’s region and industry, then identifying customized onboarding tasks that meet compliance and regulatory requirements.

    • Telecommunications, Media and Technology AI agents – The following AI agents are available for Telecommunications, Media and Technology.

      • Billing account information collector AI agent – This Telecommunications, Media, and Technology (TMT) agent retrieves billing case and account details and communicates them to the user in a structured format.

      • Billing invoice data collector AI agent – This Telecommunications, Media, and Technology (TMT) agent provides detailed information about invoices, validates amounts, and identifies high-amount line items for billing inquiries. The agent retrieves invoice history, communicates invoice and line item details to the user, and flags potential causes of high charges.

      • Billing plan recommender AI agent – This Telecommunications, Media, and Technology (TMT) agent generates recommended service plans and updates the billing inquiry case work notes with the results. The agent helps to ensure a seamless recommendation process without requiring additional user input beyond initial confirmation.

    • Unified Security Exposure Management AI agents – The following AI agents are available for Unified Security Exposure Management.

      • VR data retrieval agent AI agent – This Unified Security Exposure Management agent answers questions about vulnerability response data by constructing and executing table queries.

      • Remediation compliance analysis AI agent – This Unified Security Exposure Management agent provides insights around remediation target compliance. It provides SLA or compliance statistics about remediation targets. The agent can break down these numbers by attributes such as severity, assignment group, or configuration class item class.

      • Vulnerability exposure analysis AI agent – This Unified Security Exposure Management agent assesses vulnerability on business services or configuration items.

    • Workflow Data Fabric AI agents – The following AI agents are available for Workflow Data Fabric.

      • Automation finder AI agent – This Workflow Data Fabric agent fetches automation records from tables on your instance. The agent discovers relevant automations for a specified asset, estimates cost and time savings, saves the discovered automations, and presents a value insights summary to the user.

      • Automation table discovery AI agent – This Workflow Data Fabric agent collects asset, time, and scope inputs from the user and returns a structured JSON payload identifying the target execution tables for automation discovery.

      • Workflow data fabric flow generation AI agent – This Workflow Data Fabric agent transforms a user’s natural-language integration intent into a generated Workflow Studio flow.

      • Workflow data fabric product knowledge AI agent – This Workflow Data Fabric agent retrieves and summarizes knowledge articles. The agent passes the user’s query verbatim to the search tool, extracts factual content from matching articles, and produces a consolidated summary with references to source documents.

      • Workflow data fabric product recommendation AI agent – This Workflow Data Fabric agent evaluates integration requirements, data movement needs, communication flows, and automation objectives expressed in the user’s prompt and determines the most suitable workflow data fabric product.

      • Workflow data fabric spoke asset discovery AI Agent – This Workflow Data Fabric agent finds specific flows, subflows, and actions inside a named spoke that fulfill a user’s stated automation goal. This agent is triggered only when a user has identified a spoke and wants to explore what reusable assets it contains for their use case.

      • Workflow data fabric spoke install advisor AI Agent – This Workflow Data Fabric agent helps you search for available IntegrationHub spokes that aren’t installed on the instance. This agent takes the your search query, retrieves matching non-installed spokes from the instance catalog, and returns a prioritized list of candidates with installation links.

    • Zero Copy Connector for ERP AI agents – The following AI agents are available for Zero Copy Connector for ERP.

      • ERP action invoker AI agent – This AI agent gathers user inputs for a specific operation in Zero Copy Connector for ERP.

      • ERP data fetcher AI agent – This Zero Copy Connector for ERP AI agent retrieves information about relevant models and model operations. The AI agent currently supports Read operations.

      • ERP output formatter AI agent – The Zero Copy Connector for ERP AI agent formats and refines the output from the ERP action invoker AI agent.

      • OData service recommender AI agent – The OData service recommender AI agent identifies relevant OData services and endpoints. The AI agent can create a model for the identified service if a model does not already exist.

    • AI assets on by default – Starting with the Zurich Patch 4 release, some AI agents, generative AI skills, and agentic workflows are turned on by default in the ServiceNow AI Platform.

    • Skills – ServiceNow AI Platform products contain generative AI skills that are tailored to meet the needs of users in different workflows.

    • Skills in the Platform workflow – Most generative AI products include skills in the Platform workflow, such as product navigation. Some products include skills for the conversational user and platform experience, and knowledge article recommendations.

      • Article optimization – Article optimization skill in ServiceNow AI Platform provides recommendations for improving the quality and health of knowledge articles, providing actionable feedback to authors and managers. The recommendations for knowledge articles is set to available after you activate the Article Optimization skills in AI Admin Hub.

      • Catalog item form slot-fill – With the catalog item form slot-fill skill, generative AI can automatically complete catalog item forms based on what users search for.

      • Conversational Help – This skill uses Generative AI application capabilities to provide answers to the questions on the ServiceNow Otto panel.

      • Extract information from documents – The extract information from documents skill allows you to use ServiceNow Otto predictions to extract information from document and image files.

      • Knowledge content recommendation – Knowledge generative AI skills on the ServiceNow AI Platform provides recommendations for editing a knowledge article. Once activated, this skill is available on the ServiceNow Otto context menu.

      • Navigation – Use the navigation skill in to take you where you want to go on the ServiceNow AI Platform.

      • ServiceNow Otto in Standard Ticket Page – The ServiceNow Otto in Standard Ticket Page skill allows you to display summaries generated by ServiceNow Otto, providing overviews of recent activities and ticket details.

      • Potential knowledge gaps – The Potential Knowledge Gap helps in finding missing or incomplete knowledge coverage by analyzing service interactions. Gap recommendations is set to available after you activate the Knowledge Gaps skills in AI Admin Hub.

      • Requester Approval Checklist – The Requester Approval Checklist skill in the ServiceNow AI Platform generates a structured checklist by mapping real-time request data against your organization’s knowledge articles.

      • Smart Documents – Accelerate document insights with instant text and voice summaries, interactive Q&A, and FAQs and redaction using ServiceNow Otto in Document Management.

      • Q&A Genius Results – The Q&A Genius Results skill enables users to get answers to their questions from knowledge articles, external sources, and uploaded files directly in the ServiceNow Otto panel.

    • AI agents library – All ServiceNow AI agents are listed. Depending on your license, not all AI agents may appear in your instance.

    • Accounts Payable Operations AI agents – The following AI agents are available for Accounts Payable Operations.

      • Inquiry resolution provider AI agent – This AI agent assists with invoice inquiries by providing resolution details based on the invoice and its related data. It is intended for use by Accounts Payable Operations agents handling invoice inquiries.

      • Recommend invoice owner AI agent – This AI agent intelligently analyzes exception work notes to predict the most likely business owner for an invoice. It collaborates with the Accounts Payable (AP) specialist — the person responsible for processing the invoice — by suggesting the predicted business owner or creating follow-up tasks to streamline ownership validation and resolution.

      • Email parser agent – This AI agent identifies and classifies actionable requests, and routes them to the appropriate workflows to create invoice cases.

      • Zero touch service desk agent – This AI agent investigates supplier invoice inquiry cases and generates a response for the supplier contact. Fulfillers can review its work in the Agentic Processes panel on the case.

    • AIOps and Leap AI agents – The following AI agents are available for AIOps and AIOps Leap.

      • Alert automation AI agent – This agent interprets free-text user requests related to alert automation and guides users to the appropriate automation experience. It detects whether the user intends to enrich alerts, group alerts, respond to alerts, or any combination of these actions.

      • Alert impact verification AI agent – This agent is responsible for eligibility validation for a given alert, to perform impact analysis on it.

      • Artifact creation agent AI agent – Assists in creating and viewing artifacts such as problem records, knowledge base articles, and playbooks for an automation opportunity. The agent checks for an automation opportunity number and invokes the artifact creation topic tool to generate the required artifacts.

      • Business and technology management service impact analysis AI agent – This agent calculates the business and technology management services impacted by a given alert.

      • Manage alerts AI agent – Responsible for generating reports for a given alert and storing a structured summary with key insights and recommended next steps in the alert record.

      • Service instance impact analysis AI agent – This agent performs alert impact analysis on service instances for a given alert.

      • User impact analysis AI Agent – This agent investigates incidents and cases related to the alerts and affected services.

      • Artifact creation agent AI agent – The Artifact creation agent helps users create and view artifacts for an automation opportunity, such as a problem record, a knowledge base article, or a playbook.

      • LEAP Ansible Agent AI agent – The LEAP Ansible agent finds the Ansible job template that best matches the problem behind an automation opportunity. It recommends the template to the user and saves it to the discovered job catalog.

      • LEAP Ansible Execution Agent AI agent – The LEAP Ansible Execution agent runs the Ansible playbooks mapped to each resolution step of an incident. It launches jobs through Ansible Automation Platform, prompts the user to complete unmapped steps manually, and reports job status on request.

      • LEAP KB Creation Agent AI agent – The LEAP KB Creation agent creates draft knowledge base articles from the resolution steps of a LEAP automation opportunity.

      • LEAP Problem Creation Agent AI agent – The LEAP Problem Creation agent creates problem records from the data of a LEAP automation opportunity.

    • AI Search AI agents – The following AI agents are available for AI Search.

      • AI Search Configuration AI agent – Responsible for managing the end-to-end process of formally configuring AI Search for ServiceNow internal tables. This agent is invoked whenever a user mentions ServiceNow internal tables in the context of AI Search.

    • Asset Management AI agents – The following AI agents are available for Asset Management.

      • Asset next best action AI agent – This AI agent validates task numbers, identifies task types, and routes workflows to the correct specialized agent for troubleshooting or repair. It processes one task at a time for accurate handling of hardware asset repair order tasks.

      • Asset sourcing AI agent – This AI agent helps source a requested item by generating and executing sourcing plans through consumption, transfer, and purchase. The agent creates plans autonomously or with user guidance, coordinates with external agents for transfer and purchase orders, and validates sourcing status throughout the process.

      • Evaluate asset AI agent – This AI agent helps troubleshoot asset-related issues using the Web Search tool. It guides users to select a resolution and completes the troubleshooting task on confirmation.

      • Purchase order creation AI agent – This AI agent is used to create a purchase order.

      • Repair asset AI agent – This AI agent assists users in resolving issues with assets that are in a defective or pending repair state. It specializes in identifying and suggesting appropriate repair steps by leveraging the Web Search tool. The agent guides users through the repair process, helps them select a suitable resolution, and automatically updates and completes the repair task upon confirmation.

      • Transfer order creation AI agent – This AI agent is used for creating a transfer order to source a requested item.

    • Care Team Operations AI agents – The following AI agents are available for Care Team Operations.

      • Care Team Operations case creation AI agent – This AI agent streamlines case creation by analyzing user conversations to accurately determine the service definition for each reported issue. It intelligently identifies multiple issues raised in a single conversation and creates separate cases with correct case type and service definition.

      • Care Team Operations case creation AI voice agent – This agent helps healthcare staff report and submit Care Team Operations (CTO) requests. Trigger this agent when a user mentions Care Team Operations, CTO, or asks to submit a care team request.

      • Care Team Operations case intake AI agent – This workflow guides users through submitting operational issues by prompting for a clear problem description and ensuring required details—such as location, requesting unit, asset tag, and priority—are captured. It intelligently detects and separates multiple requests in a single user utterance, summarizes each request with its details, and confirms accuracy with the user before proceeding.

    • CMDB AI agents – The following AI agents are available for CMDB.

    • Contract Management Pro AI agents – The following AI agents are available for Contract Management Pro.

      • Contract repository AI agent – This AI agent retrieves contract repository details for a specified record, including vendor information, contract model, and document attachment IDs. It also calculates the average lead time for similar contracts when vendor, supplier, or account data exists.

      • Search contracts AI agent – This AI agent helps legal professionals, procurement teams, sales executives, and compliance officers find and understand legal contracts through conversational search and Q&A. It classifies each query, retrieves matching contract metadata or content, and links directly to results, while declining requests to draft, modify, or legally interpret contracts.

    • Core Business Suite AI agents – The following AI agents are available for Core Business Suite.

      • CBS Bulk Upload AI Agent – The CBS Bulk Upload AI agent handles bulk record creation and updates for the Space, Supplier, and Supplier Contact tables.

    • Customer Service Management AI agents – The following AI agents are available for Customer Service Management.

      • Case action AI agent – This agent can preform actions for the customer, including case actions (Create a case task, Update work notes, etc.) and more.

      • Case creation AI agent – The Case creation AI agent is designed to automate and manage the end-to-end process of case creation. This agent should be invoked when a case needs to be created.

      • Case status AI voice agent – This AI agent helps users quickly retrieve and understand the status of their cases through a conversational voice interface.

      • Complaint case research plan AI agent – Analyzes “Under Review” complaint cases to identify previously attempted troubleshooting steps and recommends new solutions from similar closed cases and knowledge articles. Prioritizes relevant suggestions, presents them with source attribution, updates work notes, and creates case task plans based on successful resolution patterns. Filters redundant recommendations and ensures proper workflow integration within case management systems.

      • Complaint case triage AI agent – This agent is designed to analyze complaint cases after their creation, automatically deriving key classification and prioritization fields. It serves case handlers and support teams by providing reliable, data-driven insights to enhance case management efficiency.

      • Context validator AI agent – The context validator AI agent is designed to validate the record, analyze the sentiment, and get the list of intents from the input record. This agent should be invoked when the record content needs to be validated.

      • Create case AI voice agent – This voice agent enables users to log cases using voice input. It guides the user through a structured conversation to capture and validate all mandatory case details, creates the case record, and provides confirmation upon successful submission.

      • Customer insight AI agent – This agent is responsible for answering any questions related to the record. The information it can retrieve includes customer information (name, contact info, details, location, history, cases, and orders) and case information (next steps, actions, agent, notes, documents, tasks, and resolutions).

      • Document verification AI agent – The document verification AI agent verifies the documents on a record to determine if they match the documents listed for the case type. Additionally, they determine if any documents are missing or if document verification has failed. This agent should be invoked when documents need to be verified.

      • Duplicate identification AI agent – The duplicate identification AI agent identifies duplicate records.

      • Email response AI agent – The email response AI agent generates and sends an email response.

      • Entity extraction AI agent – The entity extraction AI agent extracts entities from emails and information from documents. It matches the entities with a list of approved fields.

      • Image to task plan template AI agent – This AI agent asks the user to upload an image, extracts the task dependencies from it, and returns the result as JSON.

      • Informational queries AI agent – The informational queries AI agent summarizes all informational answers for a record.

      • Issue identifier complaint case intake AI agent – This AI agent is used for product and location-based grievances. It helps with intakes, documentation, and processing.

      • RMA Assistant AI agent – This AI agent helps customers and human agents with RMA case creation and processing for product returns, repairs, and replacements.

      • Transactional queries AI agent – The transactional queries AI agent will update a record with any collected transactional values.

      • Triage cases planner AI agent – This AI agent triages cases. It takes appropriate actions based on the record’s sentiment and intents. It is intended for agents and end-users who need assistance in resolving queries efficiently, as well as assessing a record for possible case creation.

      • Triage cases wrap up AI agent – The triage cases wrap up AI agent finalizes the case triage process by synthesizing the execution and posting the summary to the record’s work notes.

      • Troubleshooting steps identification AI agent – An AI agent responsible for gathering required context, identifying troubleshooting steps by comparing with KBs, similar cases and standard operating manuals, and proposing additional troubleshooting steps.

    • Employee Experience AI agents – The following AI agents are available for Employee Experience.

    • Employee Service Management AI agents – The following AI agents are available for Employee Service Management.

      • Universal Request departmental ticket creator AI agent – Automates the routing of universal requests to appropriate departmental ticketing systems by analyzing department predictions and creating properly configured HR cases or IT incidents with all necessary field mappings and reference tracking.

    • Enterprise Architecture AI agents – The following AI agents are available for Enterprise Architecture.

      • Enterprise Architecture explorer for analysis and queries AI agent – The agent answers questions about Enterprise Architecture data based on Common Service Data Model (CSDM) 5.0 entities and their relationships. It covers business capabilities, business applications, services, infrastructure, integrations, value streams, AI systems, technology standards, and conformance.

      • Enterprise Architecture diagrams AI agent – The agent finds a business application by name or ID and generates a hierarchy diagram saved as a named architectural artifact. The agent also summarizes the generated diagram.

    • Field Service Management AI agents – The following AI agents are available for Field Service Management.

      • Create work order AI agent – This AI agent creates work orders using text descriptions and image.

      • Parts audit AI agent – This AI agent assists technicians in conducting thorough parts validation only for work order tasks. It streamlines the process of verifying and reporting parts usage, ensuring accuracy and accountability.

      • Shift and schedule management AI agent – This AI agent helps managers create, review, update, and delete shifts and schedules, and assign technicians to them. It suggests default shift details, finds technicians by skill or name, and asks the manager to confirm one complete plan before making changes. Managers can also open shifts for technician signup.

    • Financial Services Operations AI Agents – The following AI agents are available for Financial Services Operations.

      • ACH dispute return recommendation AI agent – The AI agent guides the human agent through a structured decision-making process to determine the best action for an ACH dispute return task. The ACH dispute return recommendation AI agent analyzes disputed transactions based on merchant analysis and Nacha eligibility. The agent recommends actions based on historical data, and applies predefined rules when data is limited.

      • Banking CSR customer insights AI agent – The AI agent answers a customer service representative’s questions about a banking customer, presenting account, transaction, and case information in a clear, structured format so the representative can quickly explore relevant customer data.

      • Banking CSR support AI agent – The AI agent acts as a copilot for a customer service representative on a live banking call or chat, retrieving account, transaction, balance, credit, loan, and policy information in real time and presenting it in a clear, conversational format so the representative can respond quickly.

      • Dispute communication AI agent – The agent drafts a customer email about a card dispute outcome. It chooses the message template based on the dispute’s final action, fills in the relevant transaction and merchant details, and adjusts the tone to match the template’s intended message.

      • Friendly fraud AI agent – The agent guides the human agent through resolving a friendly fraud dispute. It recommends an action based on published guidance and similar past overrides, then supports the agent’s decision with real-time context to keep resolutions accurate and compliant.

      • Insurance CSR customer insights AI agent – The agent acts as a copilot for a customer service representative (CSR), answering natural-language questions about a single customer’s insurance policies, coverages, claims, billing, and servicing cases, as well as general product and procedural guidance, and presents structured, context-aware answers with follow-up options.

      • Insurance CSR support AI agent – The agent gives a customer service representative (CSR) real-time answers during a live insurance call, either from a typed question or from the latest call transcript, retrieving the customer’s policy, coverage, claim, and billing data or knowledge-base guidance so the CSR can respond quickly without switching screens.

      • Merchant analysis for disputes AI agent – The agent evaluates the merchant behind a disputed transaction for credibility. It researches the merchant’s reputation and regulatory history, weighs the evidence against a set of rules, and recommends whether the merchant is credible, with a rationale the human agent can accept or override.

      • Nacha operating guidelines check AI agent – The agent determines whether a return code assigned to a disputed transaction is eligible for filing. It checks the case against NACHA operating guidelines’ time-frame and documentation requirements and presents its eligibility decision and reasoning to the human agent for confirmation.

    • Governance, Risk, and Compliance AI agents – The following AI agents are available for Governance, Risk, and Compliance.

      • Control objective change AI agent – This AI agent acts as a Compliance Manager that validates if the description and supplemental guidance of a given control objective need updates based on the associated citations. The AI agent recommends new values if changes are required, and updates the control objective record after getting confirmation from the user.

      • Issue action plan AI agent – This AI agent identifies the optimal steps for resolving an issue by referencing the past similar issue details. It presents a step-by-step summarized action plan to the user.

      • Remediation tasks AI agent – This AI agent identifies the remediation tasks for resolving an issue by referencing the action plan field on the issue record.

      • Report a GRC issue AI agent – This AI agent enriches the given issue description with additional information by looking at historical data and using GRC context. It analyzes, extracts key information (such as issue type, date of occurrence, and so on), and creates a GRC issue.

    • Hardware Asset Management AI agents – The following AI agents are available for Hardware Asset Management.

    • Impact AI agents – The following AI agents are available for Impact.

      • Code fix AI agent – This AI Agent automates the process of analyzing, suggesting, and implementing fixes for code violations or issues detected within code repositories or running scripts. It leverages LLM-driven suggestions while keeping the user in control of approval and refinement.

    • Integration Hub AI agents – The following AI agents are available for Integration Hub.

    • IT Operations Management AI agents – The following AI agents are available for IT Operations Management.

      • Analyze potential impact AI agent – This agent analyzes the potential business and operational impact of a proposed change by identifying affected servers and services. The agent reviews the change request, performs impact analysis, and documents findings in work notes to support informed decision-making and effective mitigation strategies.

      • AWS CloudWatch API AI agent – This AI agent investigates AWS CloudWatch alarm firings end to end by querying alarm configuration, metric time series, log groups, ML anomaly detectors, and Logs Insights. It then synthesizes findings into a structured root cause analysis report with metric analysis, log evidence, and correlated service impact.

      • AWS CloudWatch MCP server AI agent – This AI agent automates AWS CloudWatch alert investigations by analyzing alarm details, querying affected resources, and retrieving CloudWatch metrics and logs across multiple strategies. It provides clear summaries with root cause analysis and actionable recommendations for resolution.

      • Azure Monitor MCP AI agent – This AI agent investigates Azure Monitor alerts by querying Log Analytics workspaces with KQL, retrieving platform metrics, and checking activity logs and resource health through Azure MCP tools. It returns structured findings to the parent agent.

      • CSDM business application to infrastructure AI specialist – This AI specialist finds the best matching discovered service for each business application and creates the standard CSDM “Uses::Used by” relationship in the CI relationships table [cmdb_rel_ci].

      • Datadog APM MCP server AI agent – This AI agent queries Datadog APM observability data using the full suite of Datadog MCP server tools. It can answer questions about service health, distributed traces, triggered monitors, log analysis, incidents, SLO compliance, deployment events, and service dependencies.

      • Dynatrace MCP server AI agent – This agent investigates Dynatrace alerts by retrieving alert impact summaries and querying the Dynatrace API for problem details, entity enrichment, log analysis, and root cause theories.

      • Gemini Cloud Assist A2A investigation AI agent – This AI agent investigates Google Cloud alerts by driving a Gemini Cloud Assist investigation through Google’s A2A investigation endpoint. It starts an investigation, polls task status, and retrieves the structured investigation result with root-cause hypotheses and next steps.

      • Kentik analysis AI agent – This AI agent fetches the incident insights report for a Kentik alert.

      • LogicMonitor API AI agent – This AI agent investigates LogicMonitor alerts and monitored entities, assesses entity health, and recommends next steps.

      • Network SME AI agent – This AI agent investigates network issues and recommends next steps.

      • New Relic MCP server AI agent – This AI agent queries and interprets observability data from New Relic using the full suite of New Relic MCP server tools.

      • Prometheus API AI agent – This AI agent analyzes and investigates Prometheus alerts and answers general Prometheus questions, surfacing infrastructure metrics through instant and range queries.

      • Service map creation AI specialist – This AI specialist discovers service infrastructure from application service candidates using ML analysis, and then it maps and persists the full service topology in CMDB.

      • SolarWinds analysis AI agent – This AI agent fetches the details related to a SolarWinds alert by executing SWQL queries against SolarWinds.

      • Splunk MCP server AI agent – This AI agent investigates Splunk Cloud and compatible Splunk Enterprise alerts by querying Splunk platform data. It doesn’t cover Splunk Observability Cloud.

      • SRE investigate AI agent – This AI agent coordinates investigation activities across multiple observability platforms by orchestrating tool-specific agents. It correlates findings, identifies probable root causes, and synthesizes comprehensive investigation reports.

      • ThousandEyes MCP server AI agent – This AI agent investigates ThousandEyes tests by analyzing metrics, anomalies, network events, and outages. It returns probable causes and recommended next steps.

    • IT Service Management AI agents – The following AI agents are available for IT Service Management.

      • Additional incident context AI agent – This AI agent retrieves information beyond the incident itself and presents the output in a readable format. This agent is strictly read-only; it can only retrieve and present information. It cannot update, modify, or perform any write operations on any records (such as adding comments, changing fields, reassigning, resolving, or closing incidents).

      • Agent client collector (ACC) diagnostic AI agent – This agent assists users in resolving ACC errors by providing suggestions based on the error codes found in the ACC Error Reports. It aims to help users resolve errors efficiently and effectively.

      • Categorize ITSM incident AI agent – This AI agent assigns the appropriate category and subcategory to an incident.

      • Create change request AI agent (autonomous) – This AI agent creates structured change requests from conversational input by autonomously selecting the appropriate change model and template.

      • Change CI suggestion AI agent – This AI agent recommends a list of relevant configuration items (CIs) that can be added as Affected CIs to the current change request.

      • Change CI suggestion AI agent (latest) – This AI agent autonomously identifies and populates both the primary configuration item (CI) and affected configuration items on a change request without requiring multiple user interactions.

      • Change conflict assessor AI agent – This AI agent can assess conflicts for changes. This includes checking mandatory fields and trigger conflict detection, and updating worknotes.

      • Change outage assistant AI agent – This AI agent creates outage records for the change request based on the impacted CIs.

      • Change quality assessor AI agent – This AI agent provides suggestions to improve inputs for a change form.

      • Change request plans AI agent – This AI agent helps change owners and approvers by drafting high-quality, context-aware change request content.

      • Change request plans AI agent (autonomous) – This AI agent autonomously drafts change plan fields during the readiness phase. Field population is governed by a resolved change policy to ensure consistent behavior without requiring user input.

      • Change template suggestion AI agent (autonomous) – This AI agent identifies the most relevant change template and model for new change requests by analyzing request details and comparing them against available templates and historical data.

      • Classify service and CI AI agent – This AI agent assigns the appropriate service and offering to an incident. It can also assign the appropriate configuration item.

      • Create incident AI agent – This AI agent manages the end-to-end process of formally logging IT support requests, specifically for user requests like “create an incident,” “raise an incident,” “open a new incident,” “open an IT ticket,” or “raise an IT support ticket.”

      • Use the Create incident AI agent – This AI agent manages the end-to-end process of formally logging IT support requests, specifically for user requests like “create an incident,” “raise an incident,” “open a new incident,” “open an IT ticket,” or “raise an IT support ticket.”

      • Create incident AI voice agent – This AI agent creates new incidents when the user explicitly mentions they want to create a new incident or report a new issue.

      • DEX diagnosis AI agent – This AI agent specializes in structured root cause diagnosis of device and application performance issues through a step-by-step process. This agent aggregates device health metrics, event logs and historical incident data, then systematically evaluates each source to isolate relevant evidence.

      • DEX remediation trigger AI agent – This AI agent receives a resolution plan, checks for remedial actions, and executes the supported ones on end-user devices (Windows OS and MacOS endpoints) to resolve common device and application issues. Remedial actions are concrete, executable fix steps such as restarting services, clearing cache, or cleaning up temporary files.

      • DEX resolution plan AI agent – This AI agent generates a resolution plan for the given issue based strictly on the root cause provided. The agent ensures all suggested steps are evidence-based, actionable, and aligned with the diagnosed root cause. No resolution is proposed outside these defined sources.

      • Incident category configuration AI agent – This AI agent orchestrates the complete lifecycle of incident category and subcategory configuration for the Choices [sys_choice] table. It supports three interaction modes: guided conversation, file upload (CSV/XLSX), and industry-based recommendations.

      • Incident context AI agent – This AI agent helps users answer questions related to a given incident using information from the incident record and its related records. This agent is strictly read-only — it can only retrieve and present information.

      • Incident knowledge article AI agent – This AI agent can resolve incidents by finding existing similar knowledge articles and attaching them to the incident. It can also create a knowledge article for the incident.

      • Incident known error article AI agent – This AI agent can find similar known error articles for an incident and attach them to the incident. It can also resolve the incident.

      • Incident resolution details AI agent – This AI agent suggests resolution notes for an incident.

      • Incident routing configuration AI agent – This AI agent creates, modifies, and deactivates assignment rules for incident routing.

      • ITSM incident resolution plan investigation AI agent – This AI agent gets incident details, reviews similar incidents and knowledge articles, and then generates an incident resolution plan.

      • Link major incident or problem AI Agent – This AI agent automatically identifies and links the most relevant major incident or problem with an incident.

      • Manage ticket AI voice agent – This AI voice agent assists users with managing their active incidents and requested items.

      • Microsoft 365 group membership AI agent – The agent helps users add or remove members of Microsoft 365 distribution list groups. It finds the group, validates the people to add or remove, confirms the action with the user, and records the request in an incident.

      • Password reset AI voice agent – This AI voice agent helps users reset their password during phone-based interactions.

      • Post incident review AI agent – This AI agent generates a post-incident review report for the user to review and revise after a major incident report.

      • Request catalog item AI voice agent – This AI voice agent assists users in finding and delivering catalog items. If the item is not categorized as software, then the catalog link is sent through approved channels such as email or SMS. If the item is software, this agent will help create the request.

      • Schedule change request AI agent – This AI agent schedules a change request.

      • Standard change template proposal AI agent – Provided with a change request, this AI agent searches for similar changes and returns a list of similar changes to use for a proposal record.

      • Standard change template recommender AI agent – This AI agent helps users create and submit change requests. Invoke it whenever the user asks to log, create, submit, initiate, or help with a change record of any type (standard, routine, scheduled, maintenance, infrastructure, software, database, security, or any changes specific to IT service management).

      • Submit account unlock catalog AI voice agent – This AI voice agent helps users unlock accounts by invoking a tool call to submit an account-unlock catalog request.

      • Troubleshoot Outlook issue AI voice agent – This AI voice agent troubleshoots Microsoft Outlook issues. It also sends a troubleshooting article link to the user by email when requested.

    • Legal Service Delivery AI agents – The following AI agents are available for Legal Service Delivery.

    • Operational Sustainability Management AI agents – The following AI agents are available for Operational Sustainability Management.

      • Calculation creation AI agent – This AI agent creates a calculated metric definition record using the formula passed in the input.

      • Calculation operand AI agent – This AI agent identifies and returns relevant metric definitions and emission factors from existing sources. It replaces generic references with precise metric definitions and emission factor names in the input formula.

    • Operational Technology Manager AI agents – The following AI agents are available for Operational Technology Manager.

      • OT Excel import task AI agent – The OT Excel import task AI agent imports Operational Technology (OT) device data from a spreadsheet into the Configuration Management Database (CMDB). The agent validates the imported records, creates remediation tasks for invalid records, and imports the valid records.

    • Operational Technology Service Management AI agents – The following AI agents are available for Operational Technology Service Management.

    • Sales CRM AI agents – The following AI agents are available for Sales Customer Relationship Management (Sales CRM).

      • Sales Automation AI agents – The following AI agents are available for Sales Automation.

      • Telco order fulfilment template creation AI agent – This Sales Customer Relationship Management AI agent generates a task plan template using the provided specification and dependency JSON.

      • Lead outreach AI agent – This AI agent sends outreach and follow-up emails to new leads on behalf of sales agents. It selects the email template based on how many emails the lead has already received, and disqualifies the lead when three emails go unanswered.

      • Appointment management AI agent – This AI agent manages appointments with leads on behalf of sales agents. It reads lead emails, checks the lead owner’s availability, and either books an appointment or proposes two open time slots. It also cancels appointments when a lead asks and sends a confirmation email.

      • Lead disinterest AI agent – This AI agent handles lead disqualification and opt-out requests for sales agents. After the sales agent confirms, it cancels the lead’s upcoming appointments, closes the related tasks, and disqualifies the lead.

      • Inbound email AI agent – This AI agent analyzes the latest email that a lead sends in response to outreach and helps sales agents decide how to move forward. It classifies the response and then books a demo, disqualifies the lead, cancels the appointment, or drafts answers to the lead’s questions.

      • Opportunity AI agent – This AI agent helps sales agents read, create, update, and summarize opportunities and their related records, such as accounts, contacts, touchpoints, opportunity tasks, line items, and meetings. It previews changes to opportunities, accounts, contacts, and line items and makes them only after the user approves.

      • Quote AI agent – This AI agent helps sales agents create, update, and finalize quotes. It validates products against the catalog, adds and configures line items, applies discounts, and generates the quote document. When started from an opportunity, it reads the opportunity line items and notes to build the quote without manual input.

      • Order Management AI agents – The following AI agents are available for Order Management.

      • Move order voice AI agent – This Sales Customer Relationship Management voice agent helps users relocate a telecom service to a new address by validating the consumer, verifying serviceability at the target location, and creating a move order.

      • Order enrichment AI agent – This Sales Customer Relationship Management agent identifies enrichment tasks for order line items and their children using historic data, confirms the task list with the user, and creates them.

      • Order fallout AI Agent – This Sales Customer Relationship Management agent analyzes order task comments and work notes to detect issues matching known fallout types, creates fallout records for any matches, and summarizes the results.

      • Order fulfillment AI agent – This Sales Customer Relationship Management agent identifies fulfillment tasks for a domain order by checking historic tasks from similar orders, confirms the task list with the user, and creates them.

      • Invoice dispute AI voice agent – This AI voice agent helps customers resolve discrepancies between invoiced quantities and received quantities by creating dispute cases.

      • Invoice dispute intake assistant AI agent – This AI agent helps requesters identify the correct invoice, confirm dispute details, and submit invoice disputes related to quantity issues. It guides users step by step through invoice identification, validation, and dispute submission to ensure a smooth and accurate experience. The agent also analyzes the sentiment of the user throughout the conversation.

      • Invoice dispute support assistant AI agent – This agent helps users efficiently identify, review, and answer questions related to invoice disputes. It validates information, maintains data integrity, and answers questions regarding the invoice case.

      • Manage order operations AI agent – This AI agent helps customers request faster delivery of an order.

      • Order capture AI agent – This AI agent helps order-management agents investigate, understand, and update customer orders in any form, whether direct (“apply 10% discount to all lines”), corrective (“this address is wrong, fix it”), bulk (“set quantity to 50 for every item”), diagnostic (“why is this order delayed?”), or follow-up (“undo the last change”).

      • Order exception AI voice agent – This AI voice agent helps customers request faster delivery of an order.

      • CPQ AI agents – The following AI agents are available for Configure, Price, Quote (CPQ).

      • PRM AI agents – The following AI agents are available for Partner Relationship Management (PRM).

    • Service Exchange AI agents – The following AI agents are available for Service Exchange.

      • Registration error monitor AI agent – This AI agent checks a registration number and connection against the Service Exchange error table, and then provides a detailed summary of any reported Service Exchange errors. This summary helps users or other AI agents make informed decisions and take appropriate action to resolve registration issues.

      • Registration initiator AI agent – This AI agent initiates the registration process and manages its early stages, including creating the registration record.

      • Registration monitor AI agent – This AI agent monitors registration task state changes, validates system configurations, and posts audit results to work notes.

    • Security Incident Response AI agents – The following AI agents are available for Security Incident Response.

      • EDR AI agent – This Security Incident Response agent retrieves host details or isolates a host associated with a security incident.

      • Exchange online integration handling AI agent – This Security Incident Response agent searches for and deletes emails related to a security incident.

      • Observable analysis AI agent – This Security Incident Response agent performs threat lookups, observable enrichment, sighting searches, and block requests against observables in a security incident.

      • Security incident activities handling AI agent – This Security Incident Response agent manages security incident core activities, ensuring efficient and effective handling of incidents, problems, change requests, and security incident response tasks. It is intended for use by security incident management teams.

      • Security incident resolution AI agent – This Security Incident Response agent walks the user through resolving a security incident by generating and executing a resolution plan.

      • Security incident retrieval AI agent – This Security Incident Response agent searches for security incidents using filters and optionally summarizes individual incidents.

      • Security incident shift handover AI agent – This Security Incident Response agent adds a security incident to a shift handover report, walking the user through each section for review.

      • Security incident wrap up generator AI agent – This Security Incident Response agent can close a security incident by performing all pre-closure validations. It can also generate close notes, generate post incident analysis, post work note, and update the state of the security incident to close it.

      • Security metrics analysis AI agent – This Security Incident Response agent calculates and analyzes security incident response metrics for an individual analyst or a team over a specified time range.

    • ServiceNow AI Platform AI agents – The following AI agents are available for ServiceNow AI Platform.

      • AI Analytics Q and A AI agent – This AI agent answers natural language questions about AI Analytics. It covers KPI definitions, dashboard widgets, source tables, data flows, deflection log states and scenarios, the deflection and metric frameworks, and ServiceNow platform scripts.

      • AICT security analyzer AI agent – This AI agent generates context for a detected AI security event by aggregating asset, prompt, cross-threat, and correlation data. It then computes a confidence score from detection strength, event risk, and authorization signals.

      • AICT AI asset description auto-populator AI agent – This AI agent generates and applies descriptions to AI assets using vendor, model, and manufacturer metadata.

      • AICT deactivate dormant asset AI agent – This AI agent analyzes and retires dormant AI assets by running comprehensive analysis and creating offboarding change requests.

      • AICT life cycle task reminder AI agent – This AI agent sends automated email reminders to task owners for overdue and dormant life cycle tasks on high-priority AI assets.

      • AICT outcome ranker AI agent – This AI agent ranks AI insight outcomes by relevance to each user persona based on their role and responsibilities.

      • Approval assistance AI agent – This AI agent handles all queries related to approval records for the current user.

      • Using the approval assistance AI agent – This AI agent handles all queries related to approval records for the current user.

      • Decomposition AI agent – This AI agent takes resolution steps as input, and then analyzes and breaks down each resolution step into a clear, structured set of smaller, actionable substeps. It then creates records based on user preference. This enables precise execution, improves task clarity, and supports automation and auditability of resolution workflows.

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      • Error analysis and remediation AI agent – This AI agent is helps admins and operators investigate Error Framework error codes, analyze root causes, and execute preconfigured remediation actions.

      • Explain SLA AI agent – This agent explains Service Level Agreement (SLA) details to the user based on the provided task number. It retrieves the matching SLA using timer configurations, along with SLA breakdown details, task SLA information, and an SLA timeline summary.

      • Image processor AI agent – This AI agent lets users upload image attachments and create task records by analyzing them. I

      • Issue readiness AI agent – This AI agent scans the record fields and context of a ticket so it can determine if it is ready for a fulfiller to work on it.

      • Using the issue readiness AI agent – This AI agent scans the record fields and context of a ticket so it can determine if it is ready for a fulfiller to work on it.

      • Issue trend analysis AI agent – This AI agent analyzes grouped task data from cases, incidents, problems, or other tables to identify recurring issues and root causes.

      • Model provider configuration AI agent – This AI Agent helps administrators understand whether their configured third-party LLM providers use OEM (managed by ServiceNow) or bring-your-own-key integration.

      • Next action recommendation AI agent – This AI agent identifies the optimal steps for resolving tasks by reviewing similar record details, related list record details, relevant knowledge articles, and web search results.

      • Prioritize work AI agent – This AI agent retrieves user-assigned tasks, scoring them by priority using metadata and Service Level Agreement (SLA) data.

      • Problems investigation AI agent – This AI agent specializes in root cause identification, impact assessment, and resolution planning. It analyzes problem details, associated incidents, similar incidents, change requests, and affected configuration items to provide a structured and comprehensive analysis.

      • Record field value prediction AI agent – This AI agent can predict fields for an incoming record.

      • Record management AI agent – This AI agent can fetch, update, create a record, link a knowledge base (KB) article, and copy an attachment to a record with details provided by the user.

      • Request status AI agent – This AI agent answers inquiries about existing tickets that were created by or are currently opened by the logged-in user. It should be triggered only when the user explicitly references an existing ticket (for example, by asking for ticket status, updates, comments, or history).

      • Using the request status AI agent – This AI agent answers inquiries about existing tickets that were created by or are currently opened by the logged-in user. It should be triggered only when the user explicitly references an existing ticket (for example, by asking for ticket status, updates, comments, or history).

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      • ServiceNow record navigator AI agent – This AI agent surfaces ServiceNow records as clickable navigation views. It handles retrieval-style queries (“show me open P1 incidents”) and navigation-style commands (“take me to changes assigned to David”).

      • Smart documents AI agent – This Document Management AI agent help users interact with document content by providing summaries, FAQs, and answers to their specific questions. It only works within the ServiceNow Document Viewer.

      • Survey analysis AI agent – This AI agent extracts and validates date ranges from user input, including natural language expressions. It analyzes survey data (such as questions, responses, and triggered records) to extract key metrics, identify trends, and deliver actionable insights for informed decision-making.

      • Survey filling data collection AI agent – This AI agent collects data related to the case, incident, or request and the survey questions raised to collect feedback on it.

      • Survey response suggestion AI agent – This AI agent suggests answers to questions in the survey by using details from case, incident, or request.

      • Troubleshooting AI agent – This AI agent triages an Automated Test Framework (ATF) test failure.

      • Update work plan AI agent – This AI agent receives structured plan data from the orchestrator, constructs a valid JSON payload, and updates the finalized work plan in the database.

      • Web automation AI agent – This AI agent executes automated actions across any web page, enabling form filling, clicking, data extraction, and navigation tasks.

      • Web research and recommendation AI agent – This AI agent resolves issues by analyzing the problem and generating resolution steps using web search tools.

      • Work allocator AI Agent – This AI agent automatically fetches backlog tickets, evaluates agent workloads and performance metrics, and assigns tickets to agents in a way that optimizes distribution and operational efficiency.

      • Content Understanding AI agents – The following AI agents are available for Content Understanding.

      • Content insights AI agent – This AI agent processes and analyazes content in the Content Understanding application.

      • Knowledge Management AI agents – The following AI agents are available for Knowledge Management.

      • KB content consolidation AI agent – This AI agent detects duplicate articles in Knowledge Management.

      • KB content creation AI agent – This AI agent generates a knowledge article from a task record.

      • MID Server AI agents – The following AI agents are available for MID Server.

      • MID common AI agent – This AI agent helps users and support personnel to identify, analyze, and resolve MID Server issues.

      • MID keystore AI agent – This AI agent helps IT administrators diagnose and resolve MID Server keystore problems and manage MID Server health.

      • MID network AI agent – This AI agent helps administrators and support personnel diagnose and resolve MID Server connectivity issues in a ServiceNow instance.

      • MID upgrade AI agent – This AI agent helps admins diagnose and resolve ‘Upgrade Failed’ issues on MID Servers by automating the identification, script deployment, error collection, and resolution workflow.

      • Notifications AI agents – The following AI agents are available for Notifications.

      • Email generator AI agent – This AI agent composes and sends emails (including new messages or replies) based on the provided context.

      • Intent executor AI agent – This AI agent executes admin-defined actions, such as workflows or creating reply responses based on matched intent.

      • Intent identification AI agent – This AI agent analyzes and identifies the input email body intent. A list of admin-configured intents are compared with the input email body for a potential match.

      • Notification AI agent – This AI agent creates or edits notification records through natural‑language interaction.

      • Notification content AI agent – This AI agent prepares email notification templates and layouts. It begins by analyzing user input to understand the notification’s content requirements and design expectations. Before generating any content, the agent searches for similar existing templates and presents the results to the user.

      • Setup Hub AI agents – The following AI agents are available for Setup Hub.

      • Group and roles assignment AI agent – This ServiceNow Otto for Setup AI agent provisions access by creating groups, adding users to a group, assigning roles to a group, and adding users to a role.

      • IA operational data workflow AI agent – This ServiceNow Otto for Setup AI agent automates the configuration of data imports by handling the creation of data sources, import sets, and transform maps.

      • Implementation plan executor AI agent – This ServiceNow Otto for Setup AI agent is responsible for executing the implementation plan, carrying out each defined step, and ensuring the plan is completed as designed.

      • Implementation plan generator AI agent – This ServiceNow Otto for Setup AI agent collects user input by asking configured questions, analyzing the full conversation to derive a precise technical intent, and then generating a step‑by‑step implementation plan.

      • Implementation plan manager AI agent – The implementation plan manager agent orchestrates an implementation by coordinating the plan generator and plan executor agents. It helps to ensure that your technical intent is converted into a clear plan and executed safely and correctly.

      • Initialize IA orchestrator AI agent – This ServiceNow Otto for Setup agent automatically triggers the appropriate Virtual Agent topic to streamline setup and onboarding for users on your ServiceNow instance.

      • SLA management AI Agent – This ServiceNow Otto for Setup agent enables administrators to create and edit SLA definitions through a conversational interface. The agent guides you through SLA configuration, including setting target type (Resolution/Response), duration, table, schedule, start/pause/stop conditions, and flow associations.

      • SSO configuration AI agent – This ServiceNow Otto for Setup AI agent guides administrators through secure Single Sign-On (SSO) setup for ServiceNow using SAML or OpenID Connect (OIDC). The agent collects and validates identity provider details, creates authentication configurations, and guides users through review, testing, and activation.

      • Update set AI agent – This ServiceNow Otto for Setup AI agent helps administrators and developers upload, review, and commit XML update sets efficiently.

    • ServiceNow Otto for Creator AI agents – The following AI agents are available for ServiceNow Otto for Creator.

      • Build Agent – This AI agent enables developers to create, edit, and deploy full-stack ServiceNow applications to update sets that encompass both user interface and back-end components.

      • Catalog AI agent – This ServiceNow Otto for Creator agent gathers, validates, and tracks all required inputs for a given Service Catalog item.

      • Module creator AI agent – This ServiceNow Otto for Creator agent Converts natural language requests into structured JSON definitions for ServiceNow application navigator modules.

      • Playbook activity context AI agent – This ServiceNow Otto for Creator agent helps users complete ServiceNow forms by fetching form fields, automatically matching pre-filled field values, and prompting for any missing required information. The agent saves the summary of what it has done.

      • Playbook data gathering and processing AI agent – This ServiceNow Otto for Creator agent is a assistant that consolidates data gathering, processing, and slot-filling capabilities. It handles end-to-end playbook execution using a broad toolkit of retrieval, reasoning, and action-oriented functions. The agent also helps with tasks related to processing of files attached to instance records.

      • Release lifecycle documentation AI agent – This ServiceNow Otto for Creator agent generates release notes and update set descriptions in natural, human-friendly language for business stakeholders, within a maximum 4000 character limit.

      • Theme Builder AI agent – This ServiceNow Otto for Creator agent assists in creating and managing themes for user interfaces. It is intended for use by UI/UX designers, developers, and anyone responsible for maintaining the visual consistency of their organization’s digital properties.

      • UI builder AI agent – This agent helps users edit pages and answer questions within the ServiceNow UI Builder editor.

    • ServiceNow Vault AI agents – The following AI agents are available for ServiceNow Vault.

      • Access Observer configuration manager AI agent – This ServiceNow Vault agent helps users complete tasks related to Access Observer configuration manager.

      • Access validation for privacy advanced configurations AI agent – This ServiceNow Vault agent checks a user’s access to manage privacy policy advanced configuration.

      • Create Data Privacy advanced configuration AI agent – This ServiceNow Vault agent helps users complete tasks related to creating data privacy advanced configuration agents.

      • Custom app recommendations AI agent – This ServiceNow Vault agent lists the custom apps on the instance, and shows recommendations along with the available protections for the selected apps.

      • Data Discovery policy and job manager AI agent – This ServiceNow Vault agent checks whether a given list of tables supports data discovery scanning. The agent then creates data discovery job policies using these tables, and creates discovery job policies and schedules and data discovery jobs using the created policy.

      • Data Discovery recommendation AI agent – This ServiceNow Vault agent provides job info, findings, attributes, and related details for a list of tables provided. The agent also returns scan recommendations you can use to create policy and schedule the discovery job by another agent.

      • Data Discovery util AI agent – This ServiceNow Vault agent assists data stewards in scheduling and managing data discovery jobs by verifying required roles, entitlements, and subscriptions. The agent categorizes discovered data patterns into compliance groups to support regulatory and security requirements. It also help user to add tags to data patterns on demand.

      • Data Discovery workflow lookup AI agent – This ServiceNow Vault agent receives provide recommendation for different tables associated with workflows, and identifies what kind of sensitive data can present in those tables such as PII,PFI,PCI,PHI,FCI.

      • Data pattern list AI agent – This ServiceNow Vault agent helps users associate data patterns to an data privacy policy configuration. It retrieves the list of available data patterns, captures the user’s selections, extracts the related sys_ids, assigns data patterns to data privacy policy configuration and validates it.

      • DP record selector agent AI agent – This ServiceNow Vault agent fetches data using tools or topics to display list of choices related to the action.

      • Edit Data Privacy advanced configuration mapping AI agent – This ServiceNow Vault agent helps users complete tasks related to edit data privacy advanced configuration mapping agent.

      • Edit privacy advanced configuration AI agent – This ServiceNow Vault agent helps users complete tasks related to edit privacy advanced configuration agent.

      • Summarize Access Observer logs AI agent – This ServiceNow Vault agent summarizes access observer logs and provides detailed breakdowns by caller type, users, and roles for specific table and column combinations.

      • Vault crypto module manager AI agent – This ServiceNow Vault agent manages the Vault crypto module configuration and access policies. The agent handles encrypted field configurations for fields, and manages module access policies for roles.

    • Software Asset Management AI agents – The following AI agents are available for Software Asset Management.

    • Sourcing and Procurement Operations AI agents – The following AI agents are available for Sourcing and Procurement Operations.

      • Procurement inquiry analysis AI agent – This Sourcing and Procurement Operations agent is an educational assistant for procurement and sourcing terminology questions. The agent scans policy manuals and help articles to deliver plain-language explanations of purchase orders, requisitions, sourcing requests, and procurement cases. It can connect users to a live agent if knowledge articles don’t have sufficient information.

      • Procurement product recommendation AI agent – This Sourcing and Procurement Operations agent can browse the catalog and create orders. The agent matches user requirements against available items, then routes to submission forms based on vendor status and dollar thresholds. Accepts and processes uploaded quote documents or SOW (Statement of Work) files to extract line items and pricing.

      • Procurement request tracking AI agent – This Sourcing and Procurement Operations agent provides a summary of a procurement request. It can provide current status and next steps for the request.

      • Spend categorization AI agent – This Sourcing and Procurement Operations agent predicts and populates product and spend categories in purchase requisition lines, reducing manual effort and improving data accuracy.

      • Contract Optimization Opportunity Finder agent – This Sourcing and Procurement Operations agent analyzes supplier contracts to identify and create savings opportunities by analyzing price escalations, renewal caps, and payment terms. It can also answer follow-up questions about spend data, contracts, savings opportunities, pipeline projects, and savings calculations.

      • Spend Optimization Opportunity Finder agent – This Sourcing and Procurement Operations agent compares on-contract and off-contract purchase order pricing to identify savings opportunities, quantifying the potential savings from redirecting off-contract spend to existing supplier contracts. It can also answer follow-up questions about spend data, contracts, savings opportunities, pipeline projects, and savings calculations.

      • Supplier Optimization Opportunity Finder agent – This Sourcing and Procurement Operations agent analyzes spend data to identify supplier optimization opportunities across non-preferred supplier spend, unconsolidated spend, spend fragmentation, and supplier performance and risk. It can also answer follow-up questions about spend data, contracts, savings opportunities, pipeline projects, and savings calculations.

    • Strategic Portfolio Management AI agents – The following AI agents are available for Strategic Portfolio Management.

      • Project eligibility analysis AI agent – This Strategic Portfolio Management agent performs checks for project and project task related requests. The agent analyzes requests to check impact on a project due to project task. It checks if the project summary email skill is enabled for the instance and whether the skill was enabled for a given project.

      • Project summarization AI agent – This Strategic Portfolio Management agent assists project managers to get a summary of the project. The agent helps in keeping their projects on track, by providing actionable and proactive updates on critical changes to projects. It also generates the project summaries.

      • Validate the work item and generate stories AI agent – This Strategic Portfolio Management agent transforms Enterprise Agile Planning work items (epics and features) into a set of well-formed, actionable user stories. It evaluates work items to verify that it contains sufficient information, and guides users through a structured validation process.

    • Supplier Lifecycle Operations AI agents – The following AI agents are available for Supplier Lifecycle Operations.

      • Supplier approval AI agent – This Supplier Lifecycle Operations agent performs the supplier activation or supplier rejection tasks during supplier approval process.

      • Supplier data steward AI agent – This Supplier Lifecycle Operations agent manages supplier onboarding by verifying details, detecting duplicates, presenting scorecards for review, creating records, and updating case status.

      • Supplier data validator AI agent – This Supplier Lifecycle Operations agent takes a supplier case number and initiates the supplier banking task validation process. The validation process compares the bank account proof against the banking details provided by the supplier contact.

      • Supplier document strategy generator agent – This Supplier Lifecycle Operations agent assists with supplier onboarding by retrieving the supplier’s region and industry, then identifying customized onboarding tasks that meet compliance and regulatory requirements.

    • Telecommunications, Media and Technology AI agents – The following AI agents are available for Telecommunications, Media and Technology.

      • Billing account information collector AI agent – This Telecommunications, Media, and Technology (TMT) agent retrieves billing case and account details and communicates them to the user in a structured format.

      • Billing invoice data collector AI agent – This Telecommunications, Media, and Technology (TMT) agent provides detailed information about invoices, validates amounts, and identifies high-amount line items for billing inquiries. The agent retrieves invoice history, communicates invoice and line item details to the user, and flags potential causes of high charges.

      • Billing plan recommender AI agent – This Telecommunications, Media, and Technology (TMT) agent generates recommended service plans and updates the billing inquiry case work notes with the results. The agent helps to ensure a seamless recommendation process without requiring additional user input beyond initial confirmation.

    • Unified Security Exposure Management AI agents – The following AI agents are available for Unified Security Exposure Management.

      • VR data retrieval agent AI agent – This Unified Security Exposure Management agent answers questions about vulnerability response data by constructing and executing table queries.

      • Remediation compliance analysis AI agent – This Unified Security Exposure Management agent provides insights around remediation target compliance. It provides SLA or compliance statistics about remediation targets. The agent can break down these numbers by attributes such as severity, assignment group, or configuration class item class.

      • Vulnerability exposure analysis AI agent – This Unified Security Exposure Management agent assesses vulnerability on business services or configuration items.

    • Workflow Data Fabric AI agents – The following AI agents are available for Workflow Data Fabric.

      • Automation finder AI agent – This Workflow Data Fabric agent fetches automation records from tables on your instance. The agent discovers relevant automations for a specified asset, estimates cost and time savings, saves the discovered automations, and presents a value insights summary to the user.

      • Automation table discovery AI agent – This Workflow Data Fabric agent collects asset, time, and scope inputs from the user and returns a structured JSON payload identifying the target execution tables for automation discovery.

      • Workflow data fabric flow generation AI agent – This Workflow Data Fabric agent transforms a user’s natural-language integration intent into a generated Workflow Studio flow.

      • Workflow data fabric product knowledge AI agent – This Workflow Data Fabric agent retrieves and summarizes knowledge articles. The agent passes the user’s query verbatim to the search tool, extracts factual content from matching articles, and produces a consolidated summary with references to source documents.

      • Workflow data fabric product recommendation AI agent – This Workflow Data Fabric agent evaluates integration requirements, data movement needs, communication flows, and automation objectives expressed in the user’s prompt and determines the most suitable workflow data fabric product.

      • Workflow data fabric spoke asset discovery AI Agent – This Workflow Data Fabric agent finds specific flows, subflows, and actions inside a named spoke that fulfill a user’s stated automation goal. This agent is triggered only when a user has identified a spoke and wants to explore what reusable assets it contains for their use case.

      • Workflow data fabric spoke install advisor AI Agent – This Workflow Data Fabric agent helps you search for available IntegrationHub spokes that aren’t installed on the instance. This agent takes the your search query, retrieves matching non-installed spokes from the instance catalog, and returns a prioritized list of candidates with installation links.

    • Zero Copy Connector for ERP AI agents – The following AI agents are available for Zero Copy Connector for ERP.

      • ERP action invoker AI agent – This AI agent gathers user inputs for a specific operation in Zero Copy Connector for ERP.

      • ERP data fetcher AI agent – This Zero Copy Connector for ERP AI agent retrieves information about relevant models and model operations. The AI agent currently supports Read operations.

      • ERP output formatter AI agent – The Zero Copy Connector for ERP AI agent formats and refines the output from the ERP action invoker AI agent.

      • OData service recommender AI agent – The OData service recommender AI agent identifies relevant OData services and endpoints. The AI agent can create a model for the identified service if a model does not already exist.

    • Agentic workflows – Agentic workflows are AI‑driven workflow orchestrations that use one or more AI agents to achieve a specific outcome. They go beyond single‑step automation by dynamically planning, executing, and adapting actions based on context, data, and intermediate results.

    • In-product agentic AI – Dedicated spaces in workspaces and in the Core UI enable you to use agentic workflows directly in record forms. You can view agentic workflow progress and previous executions, and you can answer follow-up questions for ones that require human supervision.

      • Enable the in-product experience – Enable the AI Workflows panel and UI actions for agentic workflows on forms in the Core UI and workspaces to track agentic AI executions.

      • Interactive View in the Core UI – Add the Interactive View Experience component to a page variant to display agentic AI processes on a record.

    • Platform agentic workflows – You can use the available Platform agentic workflows to achieve business outcomes with self-executing autonomous AI agents.

      • Analyze task trends – Use the Platform Analyze task trends agentic workflow to detect recurring task patterns so that you can understand the root cause and get recommendations to prevent them from happening in future.

      • Classify tasks – Use the Platform Classify tasks agentic workflow to gather relevant information about tasks automatically and make decisions about priorities and assignments.

      • Generate my work plan – Use the Platform Generate my work plan agentic workflow to create personalized work plans for currently assigned work.

      • Generate resolution plan – Use the Platform Generate resolution plan agentic workflow to fetch task record details, generate resolution summary steps, and update comments or work notes.

      • Help optimize team productivity – Use the Platform Help optimize team productivity agentic workflow to gather relevant information about tasks automatically and make decisions about priorities and assignments.

      • Identify escalation signals – Use the Platform Identify escalation signals agentic workflow to identify tickets that require attention before they are escalated, such as tickets near the end of their SLA or ones without recent updates.

      • Identify ways to improve service – Use the Platform Identify ways to improve service agentic workflow to analyze feedback, performance metrics, and historical trends that identify areas for service improvement.

      • Investigate problems – Use the Platform Investigate problems agentic workflow to perform root cause and risk assessments so that you can create an actionable resolution plan for a problem.

      • Process images for new tasks – Use the Platform Process images for new tasks agentic workflow to convert images to actionable tasks.

      • Process task closure – The Process task closure agentic workflow reviews open tickets against priority, sentiment, age, and status to identify which are ready to close, then guides a human agent through reviewing and closing each eligible ticket with resolution-note support.

      • Propose survey responses – Use the Platform Propose survey responses agentic workflow to assist requesters in completing surveys.

    • Platform AI agents – Explore different AI agents available in platform ServiceNow Otto applications.

    • Domain Separation AI agent – The Domain Visibility AI agent enables Domain Admins to manage user domain visibility using guided, conversational workflows in the ServiceNow Otto panel.

  • Large language models on the ServiceNow AI Platform – ServiceNow AI Platform utilises large language models (LLM) for generative AI and agentic AI functionality. You can choose the ServiceNow LLM or supported third-party models. LLMs bring about the Generative AI application capabilities by understanding and generating human language, and processing vast amount of data.

    • Model provider updates – Review these updates to learn which model providers and models are available for your skills and agents. Review the model cards for information about how each model is intended to be used.

    • Writing instructions for large language models – When using Now Assist products and skills, you may have the option to give specific instructions or other guidance to the LLM. Writing generative AI instructions is different from conducting a keyword search. Use the following general guidelines when crafting your instructions.

    • Long term stable models – Long term stable (LTS) models support regulated industries, such as financial institutions, with stronger AI lifecycle management, governance, transparency, and compliance tools.

    • Discrepancies when using different AI search tools – Different AI search tools may return different answers for the same or similar searches. This difference in results is expected. It occurs because each large language model (LLM) uses a different approach to find results and generate answers that match your search.

    • Providers and Models – You can bring your own large language model (LLM) provider and API to use with Now Assist.

  • AI Admin Hub – ServiceNow ServiceNow Otto uses generative AI to enhance user productivity and efficiency through conversation and proactive experiences.

    • Exploring AI Admin Hub – Learn how ServiceNow Otto Admin brings generative AI capabilities to the ServiceNow AI Platform. With ServiceNow Otto Admin, you can improve the productivity and efficiency in your organization, deliver better self-service, recommend actions and provide answers, and empower your users to search more effectively.

    • Overview tab in AI Admin Hub – The AI Admin Hub console provides quick and effortless access to the important information that you need to set up, configure, and monitor ServiceNow Otto applications and features.

    • ServiceNow Otto Experiences – Explore ServiceNow Otto panel and ServiceNow Otto context menu under the ServiceNow Otto Experiences tab of ServiceNow Otto Admin.

      • ServiceNow Otto panel – With the ServiceNow Otto panel, you can get assistance from generative AI experiences to solve customer issues faster. Use this conversational interface to summarize a chat, case, or incident, get help, or generate resolution notes so that you can get the context of this information more quickly.

      • Standard chat – With the ServiceNow Otto panel standard chat, you can get assistance from generative AI experiences to solve customer issues faster. Use this conversational interface to summarize a chat, case, or incident, get help, or generate resolution notes so that you can get the context of this information more quickly.

      • Enhanced chat – ServiceNow Otto panel enhanced chat is a conversational support experience within a dynamic window that also includes the ability to have multiple active conversations and superior search capabilities. Use ServiceNow Otto panel enhanced chat to improve your productivity and efficiency by leveraging generative AI to perform tasks such as summarize a chat, case, or incident, request help, generate resolution notes, among others.

      • Premium chat – ServiceNow Otto panel premium chat is an AI chat experience built into your ServiceNow environment. Ask questions, get answers from your organization’s knowledge, and take action on records. It supports file uploads, web search, and multi-step agentic tasks, so you can handle more complex requests without leaving the panel.

      • ServiceNow Otto context menu – The ServiceNow Otto context menu uses generative AI to help agents summarize, create, and edit written content, thus streamlining their writing tasks.

      • Email recommendations using the ServiceNow Otto context menu – Use the ServiceNow Otto context menu to compose or respond to emails with recommendations from Now Assist with generative AI template suggestions. The ServiceNow Otto context menu enables users to generate email response recommendations in new, forward, reply, or reply all scenarios.

      • Summarize records with the ServiceNow Otto context menu – Use the ServiceNow Otto context menu to generate a record summary for the page, using Generative AI application assisted summarization capabilities in workspaces and UI16. The ServiceNow Otto context menu can generate a new summary, expand or collapse the summary card, share the summary to work notes, regenerate, or copy the summary.

      • Improve Docs content in Strategic Portfolio Management with ServiceNow Otto context menu – Use the ServiceNow Otto context menu to start conversations and raise queries, using generative AI capabilities and custom built in skills in Strategic Portfolio Management.

      • Generate KB article with ServiceNow Otto context menu – Use the ServiceNow Otto context menu to generate Knowledge Base articles in Knowledge Management.

      • ServiceNow Otto context menu usage dashboard – Use the ServiceNow Otto context menu dashboard to monitor the use of ServiceNow Otto context menu across the different applications.

      • Use ServiceNow Otto context menu for custom skill deployment – Use the ServiceNow Otto context menu to deploy the custom skills created using ServiceNow Otto skill kit.

    • Analyzing AI performance – Use the AI Analytics dashboard to monitor the usage and performance of generative AI features and capabilities.

      • Exploring AI Analytics – Learn how AI Analytics enables users with the AI Analytics Viewer or AI Analytics Admin role to monitor the usage, adoption, and performance of generative AI features and capabilities.

      • Configuring AI Analytics – Configure the AI Analytics dashboard to view usage, value, and performance indicators for generative AI features on your instance.

      • Installing AI Analytics – You can install the AI Analytics application (sn_na_analytics) with any of the ServiceNow Otto products if you have the admin role.

      • Map a skill to a dashboard – Map an AI skill to a dashboard to view skill performance indicators and skill details.

      • Using AI Analytics – The AI Analytics dashboard provides indicators and breakdowns that help monitor the performance of generative AI features, capabilities, and skills active on your instance.

      • Usage and adoption – The Usage and adoption dashboard page contains key usage and performance indicators that help you evaluate AI adoption in your organization.

      • Skills performance – Use the Skills performance dashboard page to view usage and performance indicators of active AI skills.

        • Skill details – Use the Skill details dashboard page to view usage and performance indicators of a skill.

        • Custom skill details – Use the Custom skill details dashboard page to view usage and performance indicators of custom skills.

      • AI Guardian analytics – Monitor the performance of guardrails enabled through AI Guardian.

      • User search analyzer – Gain insights into user search queries and AI-powered search results.

      • ServiceNow Otto context menu analytics – Monitor the usage and performance of the ServiceNow Otto context menu.

      • AI Analytics reference – AI Analytics reference topics include information about user roles and details of the indicators on the dashboard.

      • AI Analytics roles – AI Analytics requires the following roles to view and manage the dashboard functionality.

      • AI Analytics dashboard indicator details – Indicator details help you understand the data and calculations behind an indicator that is presented in the form of a visualization on the dashboard.

    • AI Admin Hub Settings – Configure general settings for all Now Assist applications from the Settings tab in the AI Admin Hub console.

      • Configure multilingual service for ServiceNow Otto applications – Turn on multilingual service for user-entered text with native translation or Dynamic Translation in ServiceNow Otto applications.

      • Multilingual service for Now Assist – Now Assist applications use the multilingual capabilities of large language models (LLMs) to translate user-generated content.

      • Microsoft Azure OEM for Dynamic Translation in Now Assist – Support multi-language input in Now Assist applications with the Microsoft Azure OEM translator service included in Generative AI Controller.

      • Data sharing and processes – Data sharing and processes topic description.

      • Configure Now Assist privacy policies – Configure privacy policies to anonymize data in Now Assist.

      • Opt out of data sharing for Now Assist – Data sharing improves ServiceNow AI products. You can opt out of data sharing from the Now Assist Admin console Settings page.

      • Configure Now Assist data overflow processing – Configure where Now Assist data is processed during periods of high traffic.

      • Assign the data steward role – Select a data steward to make decisions about data sharing with ServiceNow in Now Assist applications.

      • Manage AI models – Access and select the LLM (large language model) provider used for various AI skills. The selection impacts all the skills within the capability.

      • Manage model providers – Edit or customize the model provider for a skill or skill group at the instance level from the list of supported third-party model providers, including the default Now LLM Service. You can also review the model policy set by your organization, and view the change history here.

      • Manage Integration – Choose the preferred integration type for configuring the available model providers. There are two ways to configure a model provider in AI Admin Hub. You can either select Original Equipment Manufacturer (OEM) or Bring Your Own Key (BYOK).

      • Manage version – Manage the version of the model providers across skills and instance levels. You can change and update versions for the out-of-box and custom skills.

      • Review Now Assist account – Review your Now Assist license details on the Account page of the AI Admin Hub console to make sure that you’re up to date on what’s available to you.

      • Usage alerts – Define threshold-based rules at the skill levels to monitor and manage assist consumption and skill execution counts when limits are reached. The solution aims to prevent resource exhaustion, and provide clear, actionable insights through an alerts feed and a rule management interface.

      • Create alert rule – Create alert rules to track the usage of generative AI skills in AI Admin Hub and notify you in the instance and via email, when the set thresholds are reached.

    • Configuring AI skills – Use the AI Admin Hub console to activate the various Now Assist applications and skills that you’re entitled to.

    • Install plugins for ServiceNow Otto – Install plugins for ServiceNow Otto to enable generative AI on your instance.

      • Now Assist suite – The Application Manager uses Now Assist suite versions to verify compatibility between multiple generative AI applications in one instance.

      • Example – The Application Manager uses Now Assist suite versions to verify compatibility between multiple generative AI applications in one instance.

    • Activate the ServiceNow Otto panel standard chat – Activate the ServiceNow Otto panel standard chat to enable your agents to use AI skills, such as task summarization or navigation, in a side panel on the user interface.

    • Activate ServiceNow Otto panel enhanced chat – Activate the ServiceNow Otto panel enhanced chat to enable your agents to use ServiceNow Otto skills, such as task summarization or navigation, in a side panel on the user interface.

    • Activate ServiceNow Otto panel assistants – You must activate ServiceNow Otto panel assistants before using them.

    • Enable voice input for ServiceNow Otto panel – Give users the option to use their voice when interacting with the ServiceNow Otto panel to make the panel more accessible. Voice input enables you to use the panel without needing to use a keyboard.

    • Configure disambiguation – Configure the disambiguation property that controls when the assistant asks clarifying questions before responding to a ServiceNow Otto for Virtual Agent or ServiceNow Otto panel user request.

    • Configure response feedback – Configure the response feedback options that appear when users select thumbs up or thumbs down on a ServiceNow Otto for Virtual Agent or ServiceNow Otto panel response.

    • Configure processing messages – For ServiceNow Otto panel premium chat, configure processing messages to control the status updates that appear in the chat interface while ServiceNow Otto works on a request. You can customize the text of processing messages to better reflect your organization’s terminology or the specific actions your AI agents perform.

    • Using AI Admin Hub – Use AI Admin Hub to explore the various Now Assist plugins, skills, and associated Generative AI application features you are entitled to.

    • AI skill details – Explore details of AI skills, security and usage data.

    • Activate an AI skill – Configure the triggers, settings, and display locations for AI skills to enable generative AI capabilities across the ServiceNow AI Platform.

    • Edit an AI skill – Edit the configuration of an AI skill to choose the inputs or triggers and the display location of the skill output.

    • Make a copy of an AI skill – Create a copy of an AI skill using the Make a copy function in AI Admin Hub. With a copy of the skill, you can experiment with skill settings and tailor the skill to fit your business needs.

    • Archive an AI skill – The ‘Archive’ option enables you to archive copies and custom AI skills.

    • Analyzing generative AI usage – Use the analytics and monitoring tools in the AI Admin Hub overview page to review the summaries, skill usage information, and issues that need your attention.

    • AI Admin Hub reference – Reference topics include information about user roles, data usage, and domain separation for AI Admin Hub.

    • Default and target model version – Model version is the large language model version a skill uses to route requests to process users’ queries. Default model version is where all the requests route to by default. This is pre-set by ServiceNow. A target model version is chosen to route your requests to a different version at run-time, rather than using the default version.

    • Domain separation in the AI Admin Hub console – If any conkeyrefs are broken, re-add them from the doc/source/reuse/domain-separation/domain-separation-overview.dita file.In the short description, edit the first sentence to state whether domain separation is supported or not and add the application name. Keep the conkeyref at the end that describes domain separation.Domain separation is supported for the AI Admin Hub console. Domain separation enables you to separate data, processes, and administrative tasks into logical groupings called domains. You can control several aspects of this separation, including which users can see and access data.

    • Fetch end points in Now Assist Conversational Help skills – The Now Assist Conversational Help skills architecture solves latency by fetching answers hosted at the nearest location, which is best suited to the user.

    • AI Admin Hub roles – Certain roles are required to use AI Admin Hub functionality. The base admin (sys_admin) role does not automatically grant or include the following roles, so assign these directly to users who require them.

      • Now Assist Admin [sn_nowassist_admin.nsa_admin] – Certain roles are required to use AI Admin Hub functionality. The base admin (sys_admin) role does not automatically grant or include the following roles, so assign these directly to users who require them.

      • AI Admin Hub console user [sn_nowassist_admin.user] – Certain roles are required to use AI Admin Hub functionality. The base admin (sys_admin) role does not automatically grant or include the following roles, so assign these directly to users who require them.

      • ServiceNow Otto panel user [now_assist_panel_user] – Certain roles are required to use AI Admin Hub functionality. The base admin (sys_admin) role does not automatically grant or include the following roles, so assign these directly to users who require them.

    • User data usage policy – Your data is safe and secure with ServiceNow user data usage policy for generative AI. You can also mask sensitive data or opt-out of sharing data for model improvements.

    • Troubleshoot an AI skill – Run diagnostics for a skill on the AI Admin Hub console to get information about the status of your skill configuration. The availability of diagnostics depends on the skill.

    • ServiceNow Otto panel system properties – Use system properties to customize ServiceNow Otto panel. Some properties are available on a system properties form, but some lesser-used properties are available only from the System Property [sys_properties] table.

    • Generative AI Controller – Use Generative AI Controller to integrate your ServiceNow applications with third-party large language models (LLMs) and manage how generative AI requests are governed, protected, and logged.

    • Exploring Generative AI Controller – Learn how Generative AI Controller works as the control and visibility layer for all generative AI requests across your ServiceNow applications, what it controls, and how it integrates with third-party LLM providers.

      • Domain separation and Generative AI Controller – If any conkeyrefs are broken, re-add them from the doc/source/reuse/domain-separation/domain-separation-overview.dita file.In the short description, edit the first sentence to state whether domain separation is supported or not and add the application name. Keep the conkeyref at the end that describes domain separation.Domain separation is supported for Generative AI Controller. Domain separation enables you to separate data, processes, and administrative tasks into logical groupings called domains. You can control several aspects of this separation, including which users can see and access data.

    • Configuring Generative AI Controller – Configure Generative AI Controller providers and capabilities.

      • Installing Generative AI Controller – The Generative AI Controller application (sn.generative.ai) is automatically installed with ServiceNow AI applications.

      • Configuring API credentials for generative AI capabilities – Configure the credentials and connections for your preferred generative AI service provider to integrate third-party generative AI on the ServiceNow AI Platform.

      • Amazon Bedrock – Configure your API credentials to use AWS Bedrock as your LLM provider for Generative AI Controller capabilities.

      • Azure OpenAI – Configure your API credentials to use Azure OpenAI as your LLM provider for Generative AI Controller capabilities.

      • Google AI Studio – Configure your API credentials to use Google AI Studio as your LLM provider for Generative AI Controller capabilities.

      • Google Vertex AI – Configure your API credentials to use Google Vertex AI as your LLM provider for Generative AI Controller capabilities.

      • IBM watsonx – Configure your API credentials to use IBM watsonx Granite models as your LLM provider for Generative AI Controller capabilities.

      • OpenAI – Configure your API credentials to use OpenAI as your LLM provider for Generative AI Controller capabilities.

      • Generic LLM – Use a generic LLM connector to connect an external AI provider to Generative AI Controller to use generative AI capabilities in your instance.

      • Configure a generic large language model (LLM) connector – Connect an external LLM to the ServiceNow AI Platform by using a generic LLM connector. With a connector, you can write your own prompts to send to the LLM and create your own generative AI capabilities.

      • Set a provider for a generative AI capability – Configure LLM providers for Generative AI Controller at the backend level. For most use cases, configure capabilities using AI Admin Hub.

      • Select a model for Amazon Bedrock – Choose which large language model (LLM) to use with Amazon Bedrock for custom skills.

      • Configure small talk filters – Redirect users to different Virtual Agent topics if small talk, such as greetings, expressions of gratitude, complaints, or requests to close, are detected in conversations.

      • Configure AI search answers capability for web search – AI web search is a OneExtend capability that enables end users to perform web searches and receive AI-powered answers. The capability supports multiple AI providers and integrates with virtual assistants and workflows.

      • Enable recursive summarization for large inputs – Use recursive summarization to break down the requests to the large language models (LLMs) into smaller pieces so that you can maintain the context for generative AI capabilities.

      • Enable Dynamic Translation for capabilities in Generative AI Controller – Use Dynamic Translation to add multiple language support for generative AI capabilities to support users who speak languages other than English.

      • Disable Dynamic Translation for LLM Virtual Agent conversations – Enable dynamic translation of chat messages into English before they are sent to the large language model in generative AI topics to support users who speak other languages.

      • Configure rate limiting for providers – Configure rate limiting to control the traffic flow to the one_extend request by restricting the number of requests that can be made within a certain time frame.

      • Bring your own key for third-party AI provider integration – The bring-your-own-key (BYOK) feature enables you to use your own API credentials from supported cloud AI providers, such as Azure OpenAI, Amazon Bedrock, and Google Gemini, to run generative AI skills and AI agents.

      • Configure a custom resource path for BYOK models – Enter a custom resource path in your bring-your-own-key (BYOK) model configuration so that Generative AI Controller can connect to AI service providers that use a different web address than the default.

    • Reference for Generative AI Controller – Reference topics provide information about Generative AI Controller tables, and properties.

  • AI Agent Studio – Build and deploy autonomous AI agents that automate complex workflows. AI Agent Studio is your command center for creating, configuring, and deploying autonomous AI agents that learn your business processes and act on them. Configure agents that learn your processes and handle routine work at scale—while you stay in control and focus on strategic priorities.

    • Explore AI Agent Studio – Create, manage, and test AI agents and agentic workflows in one centralized space to build self-executing solutions that help you achieve your business goals.

    • Configure AI Agent Studio – Configure AI agents, agentic workflows, and tools so that AI agents can plan and execute tasks using your record data and knowledge base content.

    • Install AI Agent Studio – Install Now Assist AI agents on your ServiceNow instance to enable the agentic AI experience.

    • Set up AI Agent Studio – The default (base system) AI agents provide preconfigured agentic workflows that address common business challenges across ServiceNow applications. Before activating the default AI agents, you must verify that your instance meets the prerequisites and complete the required configuration steps.

    • AI Agent Studio settings – Configure general settings for AI Agent Studio and agentic AI assets.

      • Enable AI Guardian for AI agents – Identify and block offensive messages that are sent by human agents automatically by enabling AI Guardian in AI agents. With this capability, you can help reduce your agentic workflow or test from being exposed to harmful content.

      • Set up long-term memory – Make AI agents remember your preference or facts from previous interactions and use memories for more focused conversations.

      • Long-term memory categories for Now Assist AI Agents – Long-term memory (LTM) categories define the types of semantic information that a Now Assist AI agent can learn and retain about users over time. You can add new categories and map them to specific agents to personalize agent responses based on accumulated user context.

      • Create a long-term – Add a long-term memory category to add it an AI agent while setting up long term memory

      • Map a long-term memory category to an AI agent – Map the created long-term memory category to the agents that should use it. Categories must be mapped individually to each agent; they are not applied globally by default.

      • Select the model provider – Choose the large language model (LLM) service provider for Now Assist AI agents in AI Agent Studio.

    • Multiple conversations – Multiple active conversations enable live agents to maintain separate conversations for different records. You can preserve the context of multiple conversations and enable multiple AI agents to interact at the same time through the Now Assist panel.

    • Citations in Now Assist AI agents – Citations appear on the ServiceNow Otto panel when AI agents and agentic workflows provide summaries for similar incidents and knowledge articles.

      • Hide citations – Disable citations for specific agentic workflows or AI agents in AI Agent Studio where citations aren’t required or involve confidential information.

    • Follow-up conversations – AI agents continue with follow-up conversations after the AI agent execution is complete.

    • Usage, failure, and latency notifications – Add or change recipients to email notifications triggered by unexpected or undesired behavior in AI agent and agentic workflow executions.

    • Implement access control in AI Agent Studio – Implement security in AI Agent Studio through access control lists (ACLs), user identities, and role filtering with the access control-based security measures in the agentic system.

    • Create an AI agent – Create an AI agent in AI Agent Studio to solve problems for your users and coordinate with other AI agents in agentic workflows.

    • Create an AI agent from an automation opportunity – Use automation opportunities identified on your instance to create AI agents in AI Agent Studio.

    • Define expected behavior – In the guided setup for an AI agent, write a clear description defining your AI agent, its role, and the instructions it should perform to accomplish tasks.

    • Add tools and information – Add a tool to an AI agent to enable different functionalities and help your AI agents achieve their objectives.

      • AI skill – Add a generative AI skill to an AI agent to expand its capabilities with reusable operations and custom functionality.

      • Catalog item – Add a catalog item to an AI agent in AI Agent Studio so that your users can access conversational catalog items.

      • Conversational topic – Add a Virtual Agent topic to an AI agent in AI Agent Studio so that you can use conversations to get additional information from the user. For example, a conversational topic could be used to let a user select a date range for surveys.

      • Deep research – Add a deep research tool to an AI agent so it can search multiple sources and provide answers with sources listed.

      • Desktop action – Add a desktop action as a tool to an AI agent so that AI agents can automate tasks in desktop and web-based applications.

      • Parameter mapping for defined desktop actions – Add a desktop action as a tool to an AI agent so that AI agents can automate tasks in desktop and web-based applications.

      • File upload – Upload files for analysis by an AI agent in AI Agent Studio to grant your AI agent access to specialized knowledge.

      • Flow action – Add a flow action to an AI agent in AI Agent Studio. Define the flow action to use it as a reusable operation in automating ServiceNow AI Platform features without having to write code.

      • Knowledge graph – Add a Knowledge Graph to an AI agent in AI Agent Studio that uses the structured and unstructured data from different ServiceNow records to enhance the performance of AI agents.

      • Record operation – Add a record operation tool to an AI agent to enable it to perform record operations on tables in your instance.

      • Script – Add a script tool to an AI agent to enable it to execute custom scripts to perform specialized tasks.

      • Search retrieval – Add a search retrieval tool to an AI agent to enable it to retrieve and incorporate relevant information from defined sources.

      • Subflow – Add a subflow tool to an AI agent in AI Agent Studio. Subflows are reusable sequences of processing steps that can be called from within a flow.

      • Web search – Add a web search tool to an AI agent to enable it to search the web and retrieve relevant information.

    • Define access – Define security controls for an AI agent to determine which users can access it and what permissions they have.

    • Add a trigger – In the guided setup for an AI agent, add triggers to run the AI agent automatically when certain conditions are met.

    • Select channels and messages – In the guided setup for an AI agent, select whether to use it in the ServiceNow Otto panel or an Otto assistant.

    • Modify an AI agent – Modify an AI agent in AI Agent Studio to refine its performance, align with business goals, or enable it to perform new tasks.

    • Integrate external AI agents – Integrate external AI agents in AI Agent Studio to connect the ServiceNow AI Platform with third-party agentic AI providers as primary agents.

    • Agent2Agent Protocol – Agent2Agent (A2A) is an open standard that enables cross-platform AI agent communication by associating each agent with an Agent Card containing provider information.

    • Create an external AI agent – Create external AI agents in AI Agent Studio to connect the ServiceNow AI Platform with third-party agentic AI providers.

    • ServiceNow AI agents as secondary agents – Secondary agents are specialized AI agents that handle specific workflow tasks delegated by a primary agent or user. You can connect your ServiceNow agent to other agentic AI model providers using the Agent2Agent protocol.

    • Set up A2A authentication – Configure ServiceNow AI agents as secondary agents over the Agent2Agent (A2A) protocol by setting up authentication and enabling external agent interoperability.

    • A2A API Key credential behavior – Starting in Now Assist AI Agents 7.1.x, the header included in each A2A request is driven by the API Key credential record and it is not injected automatically by the flow action.

    • Modify an external AI agent – Modify an external AI agent in AI Agent Studio to refine its performance, align with business goals, or enable it to perform new tasks.

    • Create an agentic workflow – Create an agentic workflow in AI Agent Studio to coordinate multiple AI agents and solve complex problems that require collaboration across specialized agents.

    • Define expected behavior – In the guided setup for an agentic workflow, write a clear description defining your agentic workflow and the instructions it should perform to orchestrate AI agents and accomplish complex tasks.

    • Add AI agents – Add AI agents to an agentic workflow so they can work together to accomplish complex tasks.

    • Define access – Define security controls for an agentic workflow to determine which users can access it and what permissions they have.

    • Add a trigger – In the guided setup for an agentic workflow, add triggers to run the agentic workflow automatically when certain conditions are met.

    • Select channels – In the guided setup for an agentic workflow, configure where users can engage with the agentic workflow.

    • Modify an agentic workflow – Modify an agentic workflow in AI Agent Studio to refine its performance, align with business goals, or enable it to perform new tasks.

    • Manually test an agentic AI asset – Manually test an AI agent or agentic workflow to verify it functions as defined and achieves the desired objectives.

    • Create test scenarios – Create different scenarios to evaluate your agentic AI asset’s performance across multiple executions.

    • Evaluate an agentic AI asset – Evaluate your agentic AI asset’s performance against defined scenarios to identify issues and opportunities for improvement.

    • Duplicate an agentic AI asset – Duplicate an agentic AI asset to create an identical copy that you can fine-tune with distinct instructions and tools.

    • Deactivate an agentic AI asset – Deactivate an AI agent or agentic workflow to stop it from running.

    • Reference – Find more information about user roles, tables, and the different properties that are installed in ServiceNow Otto AI agents.

    • Domain Separation AI agent – The Domain Visibility AI agent enables Domain Admins to manage user domain visibility using guided, conversational workflows in the ServiceNow Otto panel.

    • Troubleshoot manual testing – When testing an agentic AI asset in AI Agent Studio, there are a few common errors that can be addressed before you test again.

    • Troubleshoot AI agents – Use this troubleshooting guide to resolve issues with AI agents’ roles, testing procedures, silent failure diagnosis and reference resources.

    • Troubleshoot external AI agents – Use this troubleshooting guide to resolve issues with External AI agents’ roles, testing procedures, failure diagnosis and reference resources.

    • MCP Client – The ServiceNow Model Context Protocol Client (MCP Client) allows you to access the Model Context Protocol tools hosted externally and published via an MCP Server in the ServiceNow AI Agent Studio.

    • Explore MCP Client – The Model Context Protocol (MCP) is a standardized client-server protocol that enables AI applications to discover and interact seamlessly with external tools, data sources, and services. MCP facilitates communication between an AI host application (like AI Agent Studio), an MCP Client embedded in the host, and one or more MCP Servers that expose specific capabilities such as tools.

    • Configuring Model Context Protocol Client – Configure the MCP Client to execute agentic workflows with AI agents and mapped tools.

      • Install Model Context Protocol Client – Install the MCP Client application on your ServiceNow instance to enable using the tools from the MCP Server Console in AI agents.

      • Adding an MCP Server Console in AI Agent Studio – An MCP Server Console hosts the APIs and tools required by an AI application. It receives and processes calls from MCP Clients to govern ingress traffic and promote secure access to tools. Adding an MCP Server Console in the AI Agent Studio helps you to leverage the Model Context Protocol as a tool in the ServiceNow agentic AI system.

      • Add MCP server with OAuth 2.1 – Add an MCP server with OAuth 2.1 in the AI Agent Studio.

      • MCP Server with API Key – Add an MCP Server with an API Key in the AI Agent Studio.

      • MCP Server Others – Add an MCP Server by selecting a Connection and Credential Alias record.

      • Define security controls for MCP Servers – Define security controls for an MCP Servers to determine which users can access it and what permissions they have.

      • View tools on the MCP server – You can view the list of tools a connected MCP server exposes on the server’s details view in the AI Agent Studio.

      • Add Model Context Protocol tool – Add an MCP tool to an AI agent in the AI Agent Studio so that your users can access the MCP server.

      • Model Context Protocol Client reference – Find more information about user roles, tables, and the different properties that are installed with the Model Context Protocol Client application.

  • AI Agent Studio (legacy) – The ServiceNow AI agents are entities that mimic human-like intelligence by using large language models (LLMs). AI agents can perform tasks that range from simple automated responses to complex problem solving. By using AI agents, you can reduce the workloads of your live agents and help increase their productivity.

    • Explore – Learn how the AI agents enhance live agent productivity by mimicking human-like intelligence to manage tasks ranging from automated responses to complex problem solving.

    • AI Agent Studio overview – Create, manage, or test AI agents and agentic workflows so that you can create self-executing workflows to help you achieve your business goals.

    • Understand AI agents – The AI Agent Studio is designed to securely use your data, workflows, and integrations directly within the ServiceNow AI Platform. AI agents can dynamically adjust their actions based on the progress and changing conditions of incidents or cases to help verify that they stay focused on achieving their objectives.

    • AI agents best practices – AI agents can dynamically adjust the actions that are based on the progress and changing conditions of incidents or cases to help ensure that they stay focused on achieving their objectives.

      • AI agents capabilities – Lists the features supported by AI agents, including configuration options and functional capabilities.

      • Writing prompts for agentic AI – By following some general guidelines for creating AI agents and agentic workflows, you can create clear and effective instructions that help maximize their efficiency and effectiveness.

      • Example AI agent – Use the example AI agent with clear name, description, AI agent role, and list of steps fields to use as a guide when creating your own AI agents.

      • Example agentic workflow – Use the example agentic workflow with clear name, description, and list of steps fields to use as a guide when creating your own agentic workflows.

    • Group Action Framework – Group Action Framework (GAF) is an intelligence feature on the ServiceNow AI Platform that groups related records and applies actions to them using LLMs.

    • Version control – Version control enables you to track changes made to instructions sent to the LLM for AI agents and agentic workflows.

    • Security for AI agents – Implement security in AI Agent Studio through access control lists (ACLs), user identities, and role filtering with the access control-based security measures in the agentic system.

    • AI agent learning – Enhance AI agent learning through episodic memory, enabling agents to improve by learning from past successful interactions.

    • Configure – Configure AI agents to execute agentic workflows with AI agents and mapped tools.

    • Install ServiceNow Otto AI Agents – Install ServiceNow Otto AI Agents on your ServiceNow instance to enable the agentic AI experience.

    • Set up AI agents – The default (base system) AI agents provide preconfigured agentic workflows that address common business challenges across ServiceNow applications. Before activating the default AI agents, you must verify that your instance meets the prerequisites and complete the required configuration steps.

    • Enable AI Guardian – Identify and block offensive messages that are sent by human agents automatically by enabling AI Guardian in AI agents. With this capability, you can help reduce your agentic workflow or test from being exposed to harmful content.

    • Select the model provider – Choose the large language model (LLM) service provider for AI agents in AI Agent Studio.

    • Set up long-term memory – Make AI agents remember your preference or facts from previous interactions and use memories for more focused conversations.

      • Long-term memory categories for AI Agents – Long-term memory (LTM) categories define the types of semantic information that an AI agent can learn and retain about users over time. You can add new categories and map them to specific agents to personalize agent responses based on accumulated user context.

      • Create a long-term – Add a long-term memory category to add it an AI agent while setting up long term memory

      • Map a long-term memory category to an AI agent – Map the created long-term memory category to the agents that should use it. Categories must be mapped individually to each agent; they are not applied globally by default.

    • GAF – Set up Group Action Framework (GAF) to improve the response quality, recall speed, and consistency of AI agents.

      • Set up AI Search for GAF – Configure AI Search to enable Group Action Framework (GAF) to improve quality and consistency of agentic AI and generative AI on the ServiceNow AI Platform.

    • Multiple conversations – Multiple active conversations enable live agents to maintain separate conversations for different records. You can preserve the context of multiple conversations and enable multiple AI agents to interact at the same time through the ServiceNow Otto panel.

    • Citations in AI agents – You can see citations on the ServiceNow Otto panel when you execute AI agents and agentic workflows in AI agents that provide summaries to get similar incidents and relevant knowledge articles.

      • Hide citations – Disable citations for specific agentic workflows or AI agents in AI Agent Studio where citations aren’t required or involve confidential information.

    • Follow-up conversations – AI agents continue with follow-up conversations after the AI agent execution is complete.

    • Usage, failure, and latency notifications – Add or change recipients to email notifications triggered by unexpected or undesired behavior in AI agent and agentic workflow executions.

    • Create an AI agent – Create an AI agent in AI Agent Studio to solve problems for your users and coordinate with other AI agents while executing the agentic workflows.

    • Find AI agents – Find available AI agents in AI Agent Studio to explore your options when creating agentic workflows.

    • Define the specialty – In the guided setup for an AI agent, write a clear description defining your agent and its role. You can also configure supported LLMs, enable third-party access, and manage long-term memory.

    • Add tools and information – Add a tool to an AI agent to enable different functionalities and help your AI agents achieve their objectives.

      • AI skill – Add a generative AI skill to an AI agent in AI Agent Studio. You can customize the skills to meet the needs of your users in different workflows.

      • Catalog item – Add a Service Catalog to an AI agent in AI Agent Studio so that your users can access conversational catalog items.

      • Conversational topic – Add a Virtual Agent topic to an AI agent in AI Agent Studio so that you can use conversations to get additional information from the user. For example, a conversational topic could be used to let a user select a date range for surveys.

      • Defined desktop action – Add a desktop action as a tool to an AI agent in AI Agent Studio so that AI agents can execute defined path desktop actions for repetitive tasks in desktop and web environment.

      • File upload – Upload files for analysis by an AI agent in AI Agent Studio to grant your AI agent access to specialized knowledge.

      • Flow action – Add a flow action to an AI agent in AI Agent Studio. Define the flow action to use it as a reusable operation in automating the ServiceNow AI Platform features without having to write code.

      • Knowledge Graph – Add a Knowledge Graph to an AI agent in AI Agent Studio that uses the structured and unstructured data from different ServiceNow records to enhance the performance of AI agents.

      • Record operation – Add a record operation to an AI agent in AI Agent Studio to create, update, look up, or delete records.

      • Script – Create a script to add it to an AI agent in AI Agent Studio. With scripts, you can use the scriptable APIs and back-end integration to support the AI agent.

      • Search retrieval – Add a search retrieval to an AI agent in AI Agent Studio. Leveraging the Retrieval-Augmented Generation (RAG) enables an AI agent to retrieve and incorporate relevant information from an external source.

      • Subflow – Add a subflow tool to an AI agent in AI Agent Studio. Subflows are reusable sequences of processing steps that can be called from within a flow.

      • Web search – Add a web search to an AI agent in AI Agent Studio using a third-party search API such as Microsoft Bing or Google.

    • Define security controls – In the guided setup for an AI agent, define security controls for who can access the AI agent and what data the AI agent has access to.

    • Add a trigger – In the guided setup for an AI agent, add triggers to run the AI agent automatically when certain conditions are met.

      • Kill Switch – The kill switch feature detects and stops runaway AI agent triggers that execute repeatedly against the same records, preventing unnecessary consumption of Now Assist interactions.

    • Select channels and status – In the guided setup for an AI agent, activate the AI agent to use in an assistant in ServiceNow Otto for Virtual Agent, and set the processing messages.

    • Duplicate an AI agent – Duplicate an existing AI agent in AI Agent Studio so that you can save time by not having to manually configure or create AI agents.

    • Modify an AI agent – Modify an AI agent in AI Agent Studio to suit your changing business needs.

    • Test execution manually – Analyze the execution of an AI agent so you can see that it functions the way that you defined it.

    • Test user access – Run a manual test to verify that only the users you want to access the AI agent can do so.

    • Delete an AI agent – Delete an AI agent from AI Agent Studio if you no longer need it.

    • Create an external agent – Create external AI agents in AI Agent Studio to connect the ServiceNow AI Platform with third-party agentic AI providers as primary agents.

      • Integrating external AI agents – Integrate and configure external agents with the ServiceNow agentic AI system using Agent2Agent (A2A) protocol integration to use in agentic workflows created in the AI Agent Studio.

      • ServiceNow AI agents as secondary agents – Integrate ServiceNow AI agents into other agentic AI systems, such as Google Cloud or Azure OpenAI.

      • External agents with Agent2Agent – Create external AI agents in AI Agent Studio to connect the ServiceNow AI Platform with third-party agentic AI providers.

      • A2A API Key credential behavior – Starting in Now Assist AI Agents 7.1.x, the header included in each A2A request is driven by the API Key credential record and it is not injected automatically by the flow action.

    • Create an agentic workflow – Create an agentic workflow in AI Agent Studio so that AI agents can coordinate to solve complex problems.

    • Define key requirements – In the guided setup for an agentic workflow, write a clear description of what the agentic workflow is, add AI agents, and select unsupported AI model providers.

    • Define security controls – In the guided setup for an agentic workflow, define security controls for who can access the agentic workflow and what data the agentic workflow has access to.

    • Add a trigger – In the guided setup for an agentic workflow, add triggers to run the agentic workflow automatically when certain conditions are met.

    • Select channels and access – In the guided setup for an agentic workflow, activate the agentic workflow to use in the ServiceNow Otto panel or UI actions in the Core UI and workspaces.

    • Duplicate an agentic workflow – Duplicate an existing agentic workflow in AI Agent Studio to save time by not having to manually configure or create agentic workflows.

    • Activate an agentic workflow template – Activate an agentic workflow that come installed with ServiceNow applications in AI Agent Studio.

    • Modify an agentic workflow – Make changes to existing agentic workflows in AI Agent Studio to adjust them to suit your business needs.

    • Test execution manually – Test your agentic workflow in AI Agent Studio to analyze how it functions while it executes the instructions that you defined.

    • Test user access – Run a manual test to verify that only the users you want to access the agentic workflow can do so.

    • Delete an agentic workflow – Delete an agentic workflow from AI Agent Studio.

    • Deploy AI voice agents – AI voice agents are generative AI-powered agents that bring natural, conversational experiences to phone-based support. As part of the broader AI agent ecosystem, they connect with telephony providers to replace rigid menu trees with seamless and human-like interactions that can help make support faster and more intuitive.

    • Install AI voice agents – Install the required plugins to enable AI voice agents on your ServiceNow instance.

    • Create an AI voice assistant – Create an AI voice assistant to enable natural, conversational voice interactions between users and AI voice agents.

    • Create an AI voice agent – Create an AI voice agent in the AI Agent Studio to resolve cases, incidents, or tasks through the phone channel.

    • Integrating voice assistant with CCaaS provider – Enable users to get voice-based support from ServiceNow AI voice agents by integrating ServiceNow voice assistant with supported third-party Contact Center as a Service providers (CCaaS).

    • Integrate voice assistant with mobile app launcher – Configure your voice assistant to be accessible through the voice launcher functions in mobile app.

    • Test AI voice agents – Test your AI voice agents associated with a voice assistant from the Assistant Designer. You can make browser-based voice calls and view turn-by-turn analysis of AI voice agents invoked, tools executed, and latency data during the conversation.

    • AI voice agent analytics – The Voice dashboard page in the Analytics tab of Assistant Designer helps you monitor the usage and performance metrics for AI voice agents.

    • AI voice agent reference – Reference information for AI voice agents.

    • Evaluate agentic AI assets – Agentic evaluations enable you to test agentic AI assets against defined datasets to verify quality before deployment. Run results score performance, identify issues, and provide opportunities for optimization.

    • Explore – Automated evaluations test your agentic AI assets and help determine when they’re ready for production. Learn more about how evaluations work, who they’re designed for, and the benefits they deliver.

      • Guidelines for evaluations – Learn about agentic evaluation runs and different recommendations for evaluating your agentic AI assets against datasets to check for completion, performance, and tool execution.

    • Evaluate – Find guidance for every stage of the agentic evaluation lifecycle, from initial setup to reevaluation.

      • Getting started – Learn what you need to run your first agentic evaluation.

      • FAQs – Find answers to common questions about setting up and running evaluations.

      • Grounding document for AI voice agents – Guidelines for writing effective grounding documents to generate scenarios for automated testing of AI voice agents and assistants.

      • Execute a run – Evaluate agentic AI assets against datasets to monitor performance and compare benchmarks.

      • Execute a run for an AI voice agentic asset – Evaluate AI voice agentic assists against datasets to monitor performance and compare benchmarks.

        • Generate conversations – Create execution log data for AI voice agentic assets By creating new conversations from typical scenarios you configure.

      • Track and monitor – Monitor the status of an active evaluation run to catch errors early and confirm when results are ready to review.

      • Review results – Assess your agent’s overall performance after a run completes, including per-metric scores and issue counts. Use the results as your starting point for diagnosing quality issues and opportunities for improvement before deployment.

      • Review issues – Identify and prioritize specific quality failures detected during an evaluation run, organized by severity. Use issue severity and metric relevance to decide which failures to address before deployment.

      • Analyze traces – Investigate the full record of an agentic interaction to diagnose the root cause of a quality failure. Trace each step the agent took, including tool calls and outputs, to pinpoint where things went wrong.

      • Apply optimizations and reevaluate – Review and accept system-generated recommendations to improve agent quality based on detected issues. Apply optimizations before triggering a re-evaluation to confirm that changes resolved the failures.

      • Troubleshoot – Find solutions to common evaluation errors, including run failures, data ingestion issues, and unexpected results.

      • Create a custom metric – Create a custom metric for evaluating AI agents and agentic workflows to test the outputs against expected responses.

    • Reference – Find technical reference material for roles, metrics, and output formats of agentic evaluations.

      • Increase maximum inbound content length for scripted REST APIs – Increase the maximum allowed inbound content length for scripted REST APIs when automated agentic evaluation runs fail due to inbound payload size limitations.

      • Parser tool – Use the outputs of the agentic evaluation parser tool in your scripts for custom metrics to customize the criteria for effective AI agents and agentic workflows.

      • Run results page – Learn about agentic evaluation runs and the meaning behind different evaluation scores from the agentic evaluation results page.

    • Examples of using AI agents – Review different ways that you can leverage the Now Assist AI agents application in agentic workflows across the platform.

    • Resolve an incident – Help your live agents resolve an incident faster with Now Assist AI agents by using the Now Assist panel.

    • Review and update tickets – Review the status of your tickets and take standard ticket actions, such as adding comments, using the Ticket Status AI agent in Now Assist in Virtual Agent.

    • Reference – Find more information about user roles, tables, and the different properties that are installed in ServiceNow Otto AI agents.

    • Domain separation – If any conkeyrefs are broken, re-add them from the doc/source/reuse/domain-separation/domain-separation-overview.dita file.In the short description, edit the first sentence to state whether domain separation is supported or not and add the application name. Keep the conkeyref at the end that describes domain separation.Domain separation is supported for AI agents in the AI Agent Studio. Domain separation enables you to separate data, processes, and administrative tasks into logical groupings called domains. You can control several aspects of this separation, including which users can see and access data.

    • AI Agent Analytics dashboard – Track the AI agent use and efficiency gain on your instance through the AI Agent Analytics dashboard. The dashboard can reveal trends in how AI agents are used to improve the time to resolution and the number of tasks closed.

    • Run results page – Learn about agentic evaluation runs and the meaning behind different evaluation scores from the agentic evaluation results page.

    • Troubleshooting Group Action Framework – Troubleshoot common errors associated with configuring GAF, such as failed action strategy job trigger or the scheduled job failing to execute.

    • Troubleshoot AI agents – Use this troubleshooting guide to resolve issues with AI agents’ roles, testing procedures, silent failure diagnosis and reference resources.

    • Model Context Protocol Client Legacy – The ServiceNow Model Context Protocol Client (MCP Client) enables you to access the Model Context Protocol tools that are hosted externally and published using an MCP Server in the ServiceNow AI Agent Studio.

    • Exploring Model Context Protocol Client – The Model Context Protocol (MCP) is a standardized client-server protocol that enables AI applications to discover and interact seamlessly with external tools, data sources, and services. MCP facilitates communication between an AI host application (like AI Agent Studio), an MCP Client embedded in the host, and one or more MCP Servers that expose specific capabilities such as tools.

    • Configuring Model Context Protocol Client – Configure the MCP Client to execute agentic workflows with AI agents and mapped tools.

      • Install Model Context Protocol Client – Install the MCP Client application on your ServiceNow instance to enable using the tools from the MCP Server in AI agents.

      • Adding an MCP Server in AI Agent Studio – An MCP Server hosts the APIs and tools required by an AI application, enabling it to receive and process calls from MCP Clients to govern ingress traffic and to promote secure and efficient access to tools. Adding an MCP Server in the AI Agent Studio helps you to leverage the Model Context Protocol as a tool in an AI agent.

      • Add MCP server with OAuth 2.1 – Add an MCP server with OAuth 2.1 in the AI Agent Studio.

      • MCP Server with API Key – Add an MCP Server with an API Key in the AI Agent Studio.

      • MCP Server Others – Add an MCP Server by selecting a Connection and Credential Alias record.

      • Add Model Context Protocol tool – Add an MCP tool to an AI agent in the AI Agent Studio so that your users can access the MCP server.

      • Test an AI agent – Analyze the performance of an AI agent with a Model Context Protocol tool added to it to verify that it functions the way that you defined it.

      • Model Context Protocol Client reference – Find more information about user roles, tables, and the different properties that are installed with the Model Context Protocol Client application.

  • Autonomous Workforce – AI specialists in your Autonomous Workforce are designed to handle high-volume, routine requests end to end, giving your team room to focus on the tasks only people can solve.

    • Explore – Learn more about Autonomous Workforce and review the benefits AI specialists can provide for different users in your organization.

    • General guidelines – AI specialists, like other agentic AI, benefit from strategic planning for testing, deploying, and monitoring performance. Learn guiding principles to help your AI specialists achieve their highest chances of success.

    • Domain separation – Domain separation is unsupported for Autonomous Workforce.

    • Multilingual support – AI specialists can respond to users in multiple languages.

    • Configure – Configure the name, profile icon, capabilities, assignment groups, tasks, and language proficiencies of AI specialists in AI Agent Studio.

    • Configure in AI Agent Studio – Configure your AI specialists in your autonomous workforce in AI Agent Studio.

      • Profile – Modify the profile of an AI specialist in AI Agent Studio to set its name, description, roles, and assignment groups.

      • Tasks – Select tasks that an AI specialist can perform in the new AI Agent Studio.

      • Preview an AI specialist – Run a preview of your AI specialist in the new AI Agent Studio on a single record to preview how it works and verify it matches your intentions.

    • Configure in the legacy AI Agent Studio – Configure your AI specialists in your autonomous workforce in the legacy AI Agent Studio.

      • Profile – Modify the profile of an AI specialist in the legacy AI Agent Studio to set its name, description, roles, and assignment groups.

      • Tasks – Select tasks that an AI specialist can perform in the legacy AI Agent Studio.

      • Preview an AI specialist – Run a preview of your AI specialist in the legacy AI Agent Studio on a single record to preview how it works and verify it matches your intentions.

    • Assigning work – You can assign work to an AI specialist in one of three ways, depending on your current work assignment procedures.

      • Manually assign work – You can assign work to an AI specialist in one of three ways, depending on your current work assignment procedures.

      • Create an assignment rule – You can assign work to an AI specialist in one of three ways, depending on your current work assignment procedures.

      • Configure AWA – You can assign work to an AI specialist in one of three ways, depending on your current work assignment procedures.

    • Use – AI specialists are autonomous, hierarchical AI agents designed to serve as team members who can accomplish complex tasks. Onboard AI specialists to your team to use AI for role-based automation.

    • Use in AI Agent Studio – Onboard and view the performance and activity of AI specialists in AI Agent Studio.

      • View performance – Review the performance analytics of an AI specialist to track their task execution success. You can use the analytics to tune the AI specialist to suit your business needs.

      • View activity – View the task execution history of an AI specialist in the new AI Agent Studio to track where and when it plans and attempts executions. You can also give feedback on its performance.

      • Manage access – Control workspace access, user roles, and publishing settings for your AI specialist and its copies configured for specific teams.

      • Create a copy – Create a new copy of an AI specialist to configure different settings for different assignment groups

      • Deactivate an AI specialist – Deactivate an AI specialist in AI Agent Studio so that it no longer acts on records.

    • Use in the legacy AI Agent Studio – Onboard and view the performance and activity of AI specialists in the legacy AI Agent Studio.

      • View performance – Review the performance analytics of an AI specialist to track their task execution success. You can use the analytics to tune the AI specialist to suit your business needs.

      • View activity – View the task execution history of an AI specialist in AI Agent Studio to track where and when it plans and attempts executions. You can also give feedback on its performance.

      • Manage access – Control workspace access, user roles, and publishing settings for your AI specialist and its copies configured for specific teams.

      • Create a copy – Create a copy of an AI specialist to configure different settings for different assignment groups

      • Deactivate an AI specialist – Deactivate an AI specialist in the legacy AI Agent Studio so that it no longer acts on records and lists.

    • Reference – Reference for Autonomous Workforce provides additional information about the roles associated with AI specialists.

    • AI Specialist Manager role – Reference for Autonomous Workforce provides additional information about the roles associated with AI specialists.

  • AI Admin Center – Set up, manage, and optimize your AI solutions on the ServiceNow AI Platform from a single workspace.

  • AI Data Kit – Use ServiceNowAI Data Kit to add datasets to a data catalog. The curated data works with AI Skill Kit to enable AI skill development and evaluation.

    • Exploring AI Data Kit – The AI Data Kit plugin for Now Assist enables you to add datasets to a data catalog and create collections for use in ServiceNow SDK.

    • Configuring AI Data Kit – Set up AI Data Kit so that users in your organization can start working with datasets.

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    • Using AI Data Kit – Use AI Data Kit to add datasets to a data catalog to create collections for use in ServiceNow SDK.

    • Add a dataset – Import data from a ServiceNow table or a local file into AI Data Kit as a dataset. Datasets are the foundation of data collections, which you publish for use in custom skill evaluation in Now Assist Skill Kit.

    • Create a derived dataset – Create a smaller, derived dataset from an existing dataset using AI Data Kit. Use derived datasets to isolate specific records for focused ground truth labeling or evaluation without modifying the original dataset.

    • Generate synthetic data – Create synthetic data using the Standard data generator in AI Data Kit. Use synthetic data to imitate real-world records so you can run evaluations or create training for a test model without using production data.

      • Select the sample data – Add the available sample data to enhance the accuracy and relevancy of the generated data in the AI Data Kit application. The sample data can be curated, added into the data collection, or published.

      • Define columns to generate data – Provide detailed definitions to preview a test for each column that you want the data to generate the results for.

    • Add a ground truth to each dataset record – Add a ground truth to each record in a dataset. A ground truth is an expected correct output for a given record. During evaluation in Now Assist Skill Kit, your custom skill’s actual output is compared against the ground truth to measure accuracy and identify areas for improvement.

    • View data insights – You can view data insights to see the completeness and distribution of your generated data.

    • Find and cleanse sensitive data – You can scan your datasets for sensitive data, like personally identifiable information (PII). If you find sensitive data, then you can cleanse it from your datasets.

    • AI Data Kit reference – Reference topics for AI Data Kit.

    • AI Data Kit roles (sn_data_kit.analyst) – Users with this role can access the AI Data Kit Home page and edit and save ground truth data. Assign this role to analysts and AI practitioners who work with datasets and ground truth in AI Data Kit.

    • AI Data Kit roles (sn_data_kit.admin) – Users with this role can create, update, and publish datasets and data collections in AI Data Kit. This role includes all permissions granted by sn_data_kit.analyst.

  • AI Skill Kit – Use ServiceNow AI Skill Kit to create and publish custom prompts and skills for ServiceNow Otto. Creating custom skills and prompts gives you greater flexibility with ServiceNow Otto’s generative AI capabilities.

    • Exploring AI Skill Kit – Use the AI Skill Kit plugin for ServiceNow Otto to create and activate custom prompts and skills for Otto.

    • General guidelines for AI Skill Kit – Building a custom skill in AI Skill Kit follows five phases, from scoping the skill to deploying and monitoring it.

      • Skill scoping guidelines – Scope a custom skill before you build it to define its requirements and expected outcomes.

      • Dataset guidelines for AI Skill Kit – A representative, appropriately sized, and isolated dataset gives you a reliable measure of how your skill prompt performs.

      • Prompt development guidelines – Write a skill prompt that is specific, clear, and grounded in context so that the model is more likely to return the output that you expect.

      • Prompt evaluation guidelines – Evaluate a skill prompt during development and again before deployment, using batches of test data and metrics that suit your use case.

      • Deploying and monitoring the skill – After you evaluate the prompt, you can deploy the custom skill and monitor its effectiveness.

    • Example use case for AI Skill Kit – As an AI developer, you can create custom skills with AI Skill Kit. For this example, create a custom skill for child incident summarization.

    • Configuring AI Skill Kit – Configure prompts and skills for AI Skill Kit.

    • Configure a skill prompt – Configure a skill prompt to set its large language model (LLM), the randomness of its responses, and its token limits.

    • Configure deployment and skill settings – Configure where a skill appears in AI Admin Hub, review general information, set security controls, choose a provider, and add evaluation metrics.

    • Configure security controls for a skill – Define an access control list (ACL) and role restrictions for every skill. The ACL limits which user roles can access and run the skill, and role restrictions limit the roles that the skill runs with.

    • Using AI Skill Kit – Use AI Skill Kit to create and publish prompts and custom skills for Otto.

    • Create a skill – Create a custom skill for Otto. Custom skills extend the generative AI capabilities of Otto to fit your own use cases.

      • Clone and edit a ServiceNow skill – Clone an eligible skill provided in ServiceNow Otto applications in AI Skill Kit so that you can edit its prompt or change its AI service provider. Editing the prompt lets you control the format and content of the large language model (LLM) response.

    • Create a prompt – After you create a custom skill, create a prompt. The prompt defines the instructions that the skill sends to the LLM and the skill inputs that it uses.

      • Add a tool – Add and manage tools visually in the Tools editor, including decision branching, to execute different tools for your skill. Adding decision branches between tools enables you to define the conditions that must be met for a tool to run. If no conditions are met, the default branch’s step is executed.

      • Add a retriever – Add a retriever to your skill to augment your prompts with relevant context from AI Search results.

      • Add a web search – Add a web search as a tool in AI Skill Kit to include web search results in your skill prompts.

      • Add Predictive Intelligence – Add Predictive Intelligence as a tool in AI Skill Kit. Predictive Intelligence models predict values, estimate outcomes, and identify patterns that you can use to route work, populate forms, and estimate wait times.

      • Add Document Intelligence – Add Document Intelligence as a tool in AI Skill Kit to extract structured data from documents as part of your skill’s execution flow.

    • Use prompt assistance – Use prompt assistance to start your prompt from an example in the prompt library or from a prompt that ServiceNow Otto generates.

    • Test a prompt – After you create a prompt for your custom skill, test it before you finalize it to verify that it returns the expected results.

    • Evaluate a prompt – Use the AI Skill Kit evaluation tools to measure how your skill prompts perform against a dataset, using automated metrics and human feedback.

    • Finalize and publish a skill – Finalize at least one prompt and publish your custom skill so that a Otto admin can activate it.

    • Activate a skill – After you create and publish a custom skill, you must activate it in AI Admin Hub. Activating the skill enables you to trigger the skill within the UI.

    • Call a custom skill from a script – Call a custom skill from a UI action script so that you can run the skill and use its output in your instance logic.

    • AI Skill Kit reference – Look up the roles, general guidelines, and log tables that support custom skill development in AI Skill Kit.

    • Document Intelligence in AI Skill Kit – Use the Document Intelligence tool to leverage extraction, question answering, and summarization capabilities for a skill created with AI Skill Kit.

    • AI Skill Kit roles – Certain roles are required to use AI Skill Kit functionality.

    • Field of use for AI Skill Kit – AI Skill Kit is included in various ServiceNow Otto packages that cover your ability to build Otto skills.

    • Web search custom skill – The web search custom skill performs an internet search to answer a query. Web search runs when the LLM and AI Search can’t provide results or whenever web search mode is activated.

  • AI Control Tower – Gain visibility into your organization’s AI footprint, manage the lifecycle of AI investments, govern risk and compliance, and measure the business impact of AI with AI Control Tower.

    • Explore – Learn how AI Control Tower provides centralized visibility and governance across your organization’s AI portfolio, connecting strategic planning, asset discovery, monitoring, compliance, and value measurement in a single workspace.

    • AI portfolio overview – Act on the AI governance work that matters most, review the state of your AI portfolio, and track the value your AI assets deliver on the Home page in AI Control Tower.

    • Activity Center – Track and act on the governance work generated across AI Control Tower from a single workspace, including lifecycle tasks, security tasks, change and offboarding requests, and AI recommendations.

    • Recommendations and AI insights – Recommendations and AI insights direct your attention to the AI governance work that matters most, so you can resolve high-impact issues without searching for them.

    • ServiceNow Otto in AI Control Tower – Ask questions about your AI portfolio in plain language and get answers drawn from your own AI Control Tower records.

    • Configure – Set up AI Control Tower so your organization can begin managing AI assets, enforcing governance policies, and tracking AI value.

    • Activating AI Control Tower – Govern, secure, and measure the value of your AI assets across the enterprise by activating AI Control Tower.

    • Guided setup – Configure AI Control Tower in a logical sequence and track your progress from a single summary page, instead of navigating separate Settings pages.

      • Access guided setup – Configure AI Control Tower in a logical sequence and track your progress toward a complete setup.

      • Guided setup areas – Area-by-area listing of the setup items in guided setup, with cross-references to the topic each item relates to.

    • Configure users and roles – Add and manage role to a user group to grant its members the corresponding permissions in AI Control Tower.

    • Configuring integrations – Connect AI Control Tower to external AI platforms so you can discover AI assets, collect trace data for monitoring, and synchronize governance across multiple ServiceNow instances.

      • Configuring connectors – Discover AI assets running on external platforms and hyperscaler environments to populate your AI asset inventory, apply consistent governance, and report on usage in AI Control Tower.

      • Create an AI connection for discovery – Bring AI assets from an external platform into your AI asset inventory by creating an AI connection. AI Control Tower authenticates to the platform using your credentials and imports assets on a schedule, so they’re governed and monitored alongside AI systems built natively on ServiceNow.

      • Configuring trace connections – Collect trace data from AI agents running on supported cloud platforms and monitoring services to generate evaluation metrics, populate AI inventory, and support security monitoring in AI Control Tower.

      • Add an AWS CloudWatch trace connection – Monitor AI agents built with AWS Bedrock AgentCore by adding an AWS CloudWatch trace connection. AI Control Tower collects trace data through your AWS credentials and a MID Server, without requiring SDK instrumentation.

      • Add an Azure trace connection – Monitor AI agents running on Microsoft Azure by adding an Azure trace connection. AI Control Tower collects trace data through your Azure credentials and a MID Server, without requiring SDK instrumentation.

      • Add a GCP Cloud Trace connection – Monitor AI agents running on Google Cloud by adding a GCP Cloud Trace connection. AI Control Tower collects trace data through your Google Cloud credentials and a MID Server, without requiring SDK instrumentation.

      • Troubleshoot a trace connection – Identify why a trace connection is not collecting data by reviewing its execution log and error messages.

      • Edit a trace connection – Update the configuration of an established trace connection to change its credentials, collection frequency, MID Server, or other settings.

      • Deactivate a trace connection – Stop trace collection for an established connection without deleting it, so you can reactivate it later.

      • Multi-instance – The Multi-instance Framework enables a prod (manager) instance to manage multiple sub-prod (managed) instances and facilitate communication for AI Control Tower.

      • Request a Manager instance – Request a Manager instance from the Multi-instance page to configure multi-instance management.

      • Configuring security connections – Connect AI Control Tower to the platforms where your AI agents run and to your identity provider so that policies and AI agent containment using kill switch protocol can act on agents wherever they’re hosted.

      • Add an AWS Bedrock or AWS Bedrock Agent Core connection – Connect AWS Bedrock or AWS Bedrock Agent Core to AI Control Tower so policies and AI agent containment using kill switch protocol can reach and act on agents running on AWS.

      • Add an Azure AI Foundry connection – Connect Azure AI Foundry to AI Control Tower so that policies and AI agent containment using kill switch protocol can reach and act on agents running on Azure AI Foundry.

      • Add a Gemini Enterprise Agent Platform connection – Connect Gemini Enterprise Agent Platform to AI Control Tower using a service account and OAuth 2.0 JWT Bearer authentication, so policies and AI agent containment using kill switch protocol can reach and act on agents running on Gemini Enterprise Agent Platform.

      • Add an Okta connection – Connect Okta to AI Control Tower so that policies and AI agent containment with kill switch protocol prevent future tokens from being issued to a deactivated AI agent. If your AI agent is on ServiceNow or AWS Bedrock, the agent’s identity is federated through Okta.

      • Add an Agent Client Collector (ACC) connection – Block and unblock AI usage on managed devices by configuring Agent Client Collector (ACC) as a control enforcement point.

    • Configuring rules and templates – Configure how AI assets are classified, evaluated, valued, and secured across your organization through rules and templates.

    • AI Gateway – Use the AI Gateway settings page to control global transaction processing for all connected MCP servers and manage individual MCP server connections independently of the global gateway state.

    • AI Control Tower playbooks – Drive the lifecycle and approval workflows for AI assets using playbooks in AI Control Tower.

      • Updated playbooks for governing managed assets – Manage AI assets through structured lifecycle workflows using the updated playbooks that automate governance tasks, route approvals, and track compliance requirements across onboarding, maintenance, and retirement phases.

      • Migrate to updated AI asset onboarding playbook – Configure the new AI asset onboarding playbook to simplify AI asset management through structured lifecycle workflows. The migration also includes an opt-in to decide between moving to the new playbook or continuing in the existing playbook.

    • Configure ServiceNow AI settings – Control how AI features operate on your instance by configuring data sharing preferences, asset approval requirements, and allowed AI model providers.

      • Data controls – Data controls in AI Control Tower enable you to manage how ServiceNow Otto traffic and data are handled across ServiceNow and external datacenters.

      • Builder controls – Builder controls in AI Control Tower enable you to govern AI assets and workflows by requiring human approval before an asset is deployed or a playbook is triggered.

      • AI model providers – AI model providers enable you to select data routing and manage third-party LLMs (Large language models) and SLMs (Small language models).

    • Configuring pages – View and govern your AI portfolio using terms and visualizations tailored to your organization.

      • Configure a page card – Change how a card presents its data so that a page matches the terminology, level of detail, and reporting focus your organization works with.

    • Creating or extending pages with pro-code tools – Extend your AI Control Tower pages beyond the built-in configuration options with the ServiceNow Lux Lab for VS Code extension.

    • Plan your AI strategy – Define AI strategic priorities, set measurable goals, and track planning items and costs to align your AI investments with organizational objectives.

    • Plan AI strategy, prioritize, and execute – Track your AI portfolio from strategy to delivery. Plan connects goal alignment, intake management, and execution tracking in one place within AI Control Tower. Portfolio managers and AI COE leads get a current view of AI investments without leaving the application.

      • Explore – AI Plan in AI Control Tower gives portfolio managers and AI COE leads a single place within AI Control Tower to view goals, intake, and delivery work scoped to AI investments.

      • Configure – The AI Plan tab in AI Control Tower displays when the Strategic Planning application is installed. It requires no additional configuration once installed.

      • Use – Use the Strategize, Prioritize, and Execute pages in the Plan menu of AI Control Tower to manage AI work from goal alignment through delivery tracking.

      • Strategize AI plan – The Strategize tab gives AI stewards a view of the AI strategic direction for their organization. Goals, targets, and strategic priorities that guide AI investment and governance decisions are tracked in one place.

        • Edit a strategize record – Update the details of an existing goal, target, or strategic priority record to reflect changes in organizational direction, scope, or measurement criteria.

      • Prioritize AI plan intake – View and manage AI-related intake items, demands, product ideas, and product feedback, through analytics tiles, contextual filters, and filterable lists in Prioritize tab. All three lists support creation of new records from this tab.

        • Create an intake record – Use the Prioritize tab to add a demand, product idea, or feedback record. Selecting New in any list opens a full form. The form fields differ by record type.

        • Manage intake records – Update the details of an existing intake record to reflect changes in scope, priority, or other relevant information.

      • Monitor AI Plan execution – Monitor the delivery status of AI-related projects and epics from a single page in AI Control Tower, with widgets and lists that update dynamically when you apply filters.

      • Reference – Field descriptions for the Strategize, Prioritize, and Execute forms in the AI Control Tower.

      • Components installed with AI Control Tower for SPM – User roles are added with the installation of AI Control Tower and Strategic Planning application.

      • Demand form – Learn about the fields on the Demand form. Use this form to create or edit a demand from the Prioritize page in AI Control Tower.

      • Epic form – Learn about the fields on the Epic form. Use this form to create or edit an epic from the Execute page in AI Control Tower.

      • Feedback form – Learn about the fields of the feedback form. Use this form to create or edit the details of a feedback record in AI Control Tower.

      • Goal form – Use the Goal form to create goals for your organizational strategic priorities.

      • Project form – Learn about the fields on the Project form. Use this form to create or edit a project from the Execute page in AI Control Tower.

      • Product idea form – Learn about the fields of the Product idea form. Use this form to create or edit the details of a product idea in AI Control Tower.

      • Strategic Priority form – Use the Strategic Priority form to orient your organization strategic plan toward your goal.

      • Target form – Use the Target form to track and measure the progress of the goals.

    • Discover and manage AI assets – Get a complete picture of every AI system in your organization by building and maintaining a comprehensive AI asset inventory.

    • Discovering AI assets – Populate your AI asset inventory automatically as AI Control Tower discovers AI assets across external platforms, hyperscaler traces, and your ServiceNow instance.

    • Managing your AI asset inventory – Track every AI system, model, prompt, and dataset across your enterprise and control which assets participate in governance, monitoring, and risk workflows.

      • Creating AI assets manually – You can create AI assets manually to track and manage the life cycles of your AI systems, AI models, prompts, and datasets.

      • Create AI system assets – Create AI assets to track and manage the life cycles of your AI systems

      • Create AI model assets – Create AI assets to track and manage the life cycles of your AI models.

      • Create prompt assets – Create AI assets to track and manage the life cycles of your prompts.

      • Create dataset assets – Create AI assets to track and manage the life cycles of your datasets.

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      • Reviewing duplicate AI assets – AI asset deduplication in AI Control Tower identifies duplicate records in the AI asset inventory for AI Stewards to review and consolidate.

      • Actions performed on duplicates – Review the actions and their immediate results when performed on duplicate AI assets in the Review duplicates page.

      • AI Inventory Intelligence Agent – AI Inventory Intelligence Agent automatically analyzes the AI asset inventory, identifies assets with incomplete metadata, and generates enrichment recommendations to improve data quality and governance readiness.

      • Managed and unmanaged AI assets – Control whether an AI asset participates in governance workflows by changing the status in AI Control Tower.

      • Move assets to managed – Bring discovered or imported AI assets into your governance program by moving them to managed status, making them eligible for lifecycle review, evaluation scoring, risk classification, and value measurement.

      • Move assets to unmanaged – Remove AI assets from active governance workflows by moving them to unmanaged status, retaining them in the inventory for tracking without incurring subscription charges.

      • Managing AI assets in bulk – Classify large numbers of AI assets automatically instead of moving them to managed one at a time by creating automation rules.

      • Create an automation rule – Set the conditions for automatically marking discovered AI assets as managed by creating a custom automation rule.

      • Edit an automation rule – Change which discovered AI assets a rule marks as managed, or update its name or description, by editing an existing automation rule.

      • Evaluating AI systems – Monitor the quality and safety of select AI systems by enabling evaluations.

      • Enable evaluation for an AI system – Start generating quality and safety scores for one or more AI systems by enabling evaluation from the inventory.

      • Disable evaluation for an AI system – Stop generating evaluation scores for one or more AI systems by disabling evaluation from the inventory, while preserving all historical score data.

      • External Registries – External Registries is the mechanism that lets an AI Steward make a managed AI agent discoverable to external systems.

      • Publish AI agents – Marking AI agents as publishable from an AI asset record page.

      • Deactivate a managed asset – Deactivate a managed AI agent directly from its asset record using kill switch protocol, even if the agent has no associated security event.

    • Working with AI asset records – An asset record consolidates the state of a single AI asset and the actions you can take on it, so you can understand the asset’s situation and respond from one page.

      • Reviewing AI asset status – Check the governance posture, pending work, key details, and value delivered by a specific AI asset, and act on what needs attention without leaving the record.

      • Recommendations for your AI assets – Recommendations for AI assets are automatically generated for you to review and act on.

      • Managing AI asset details – Confirm what an AI asset is, who owns it, how it operates, and what other assets depend on it before approving a change, investigating a score, or responding to an audit question.

      • Update use and purpose – Update the use and purpose values for an AI asset when its operating characteristics or business outcome change so that risk classifications and governance controls continue to reflect how the asset behaves.

      • Use and purpose fields – Field and value reference for the Use and purpose section of an AI asset record. AI stewards and AI asset owners reference these values during onboarding, reviews, and audits.

      • Trace AI asset relationships – Identify the AI assets that depend on or feed into a specific AI asset to assess the impact of a planned change, investigate an unexpected evaluation score, or respond to an audit question about how data and models flow through your AI inventory.

      • Associate a business application with an AI system – Associate an existing business application with an AI system asset, independently of whether the Enterprise Architecture Workspace plugin is installed.

      • Managing your AI asset lifecycle – Get a structured view of an AI asset’s progression through its operational stages, from initial onboarding to retirement.

      • Create change requests for AI assets – Create a change request to modify the relationships between a deployed AI asset and its related assets.

      • Create offboarding requests for AI assets – Create an offboarding request to retire AI assets that are no longer needed.

      • Planning strategy and execution for an AI system – View all planning data for a specific AI system in one location. The Plan tab automatically filters goals, intake items, and delivery records to show only those linked to your selected AI system across Strategize, Prioritize, and Execute workflows.

      • Governing AI asset risk and compliance – Get a consolidated view of your AI asset’s regulatory risk classification, compliance posture, control effectiveness metrics, and governance artifacts from the Risk & compliance tab.

      • Monitoring an AI system – Assess the quality and safety performance of a specific AI system by reviewing its scores, metric breakdowns, trends, and evaluated sessions.

      • Review scores for an AI system – Determine whether a specific AI system is meeting quality and safety targets by reviewing its scores, identifying which metrics are affecting performance, and checking for regressions over time.

      • Configure metrics evaluated for an AI system – Add or remove metrics for a specific AI system to tailor evaluation coverage without changing your organization’s global metric configuration.

      • Viewing value metrics for an AI system – View productivity gains, net returns, usage volume, and cost metrics so you can assess the business impact of a specific AI system over time.

      • Viewing templates associated with an AI system – Learn how value is being measured for an AI system by viewing the value templates mapped to the asset and the productivity calculation each template uses.

      • Governing AI asset security – Get a snapshot of your AI asset’s security posture and visualize how your asset relates to other agentic assets in your enterprise in the AI agent map.

    • Working with MCP server records – The MCP servers tab on the AI Control Tower Inventory page provides a centralized view of MCP servers registered in your ServiceNow instance.

    • Govern AI assets – Manage AI assets across the life cycle using AI Risk and Compliance, with integrated Security and Privacy oversight for risk, compliance, and security.

    • Managing risk and compliance – Gain visibility into AI-related risks, compliance posture, and governance outcomes for your AI portfolio in AI Control Tower.

      • Explore – Risk and Compliance in AI Control Tower provides a summary of regulatory classification, compliance, and risk across your AI portfolio and individual AI assets.

      • Configure – Risk and Compliance information appears in AI Control Tower only when the required governance applications, frameworks, and data are available in your environment.

      • Reviewing AI governance posture and compliance status – The Risk & Compliance tab under Govern displays regulatory classification, risk posture, and compliance information for AI assets.

      • Reviewing regulatory classification and compliance status – Review regulatory risk classification and compliance posture to understand how AI assets align with configured governance expectations.

        • Regulatory risk classification – Regulatory risk classification places an AI asset in a risk rating, such as Low or High, based on the score from a risk assessment.

        • Compliance score calculation – The compliance score of an AI system measures how well its controls are met, based on the scores of its active related entities.

      • Reviewing AI risk posture – Aggregated risk posture displays inherent risk, residual risk, and control effectiveness for AI assets.

        • Risk rating calculation – The inherent, control effectiveness, and residual ratings of an AI system come from risk assessment scores that a risk rollup aggregates.

      • Governance insights – Scheduled detection jobs evaluate your AI portfolio and generate the Stalled Lifecycle Tasks and Significant Issues insights that appear as recommendations.

        • Governance insight detection – Thresholds, criteria, and refresh schedule that determine when an AI system appears in the Stalled Lifecycle Tasks or Significant Issues insight.

      • Act on AI asset governance insight recommendations – Resolve Stalled Lifecycle Tasks and Significant Issues recommendations to keep AI systems moving through their governance life cycle.

      • Reference – Review the scope of Risk and Compliance visibility in AI Control Tower, including key terminology used across these views.

      • Terminology – Key terms used across Risk and Compliance views in AI Control Tower, including compliance score and regulatory risk classification.

      • Governance records – Record types under the Governance section of an AI asset’s Risk & Compliance tab in AI Control Tower, used to support governance review.

      • Regulatory risk classification – Risk assessment methodologies, formulas, risk ratings, and records that determine the regulatory risk classification of an AI asset.

      • AI risk score calculation – Formulas, rating bands, lookup matrix, and rollup settings that determine the inherent, control effectiveness, and residual ratings of an AI system.

      • AI system compliance score calculation – Formula, calculation rules, scheduled jobs, and properties that determine the compliance score of an AI system.

      • Dashboard errors – Properties and scheduled jobs that resolve No data found errors in the risk score and compliance score dashboards on the AI asset record and landing pages.

    • Managing AI asset security – Review AI asset security metrics such as access issues, dormant and privileged AI agents, and map the relationships of your ServiceNow agents, agentic workflows, and tools.

      • Explore – The security area of AI Control Tower offers a dashboard-based view of your AI asset security metrics. In addition to tracking metrics, the security area contains the agent map, a tool that gives a node-graph visualization of the relationships between your AI agents, agentic workflows, and tools.

      • Basic concepts – Learn more about the AI asset security score, agent map, AI asset security events, and internal and external agents.

      • AI agent containment using kill switch protocol manually – Explore how detecting malicious activity and deactivating AI agents works to enforce guardrails and help improve your security posture.

      • Configure – Plan and configure your implementation of security metrics. You can configure metrics to measure the integrity of your data model and monitor potential threats in large language model (LLM) input and output. You can also customize the AI asset security score.

      • Configure overview security metrics – If you have external AI agent data you want reflected in the Privileged AI agents metric, set up a Traceloop connection to the external AI server and perform other setup to make sure agent data appears in the metric. Or, set up a static connection for Amazon Web Services (AWS) or Azure.

      • Configure Veza access intelligence in the agent map – Integrate Veza with ServiceNow to display agent risk scores and severity levels in the agent map. Complete this configuration for each hyperscaler that has AI assets you want to monitor.

      • Activate design-time security metrics – Set up AI Service Graph Connectors for the AI security tools you currently use to import AI model vulnerability and AI validation (automated red teaming) data and show it in design-time metrics. To see additional metrics, including details for each metric, install the AI Security Exposure Management plugin (com.sn_sec_ai).

      • Configure runtime security metrics – Set up a Traceloop connection and API key for prompt injection, offensive content, sensitive data input, and sensitive data anonymized trace data from external AI servers. Review data privacy for sensitive data patterns to detect in LLM input and output.

      • Configure post-runtime security metrics – Customize security and content moderation policies, sampling rate, skill call usage limit, LLMs to use, and other settings for the Top AI asset security events metric and others.

      • Configure the AI asset security score – Customize the AI asset security score to align with your enterprise’s security practices. You can omit large language model (LLM) guardrail categories from the score or change the weights of categories.

      • Configure AI agent containment using kill switch protocol manually – Connect identity providers and hyperscalers to ServiceNow to let you contain and enforce guardrails for AI assets at runtime using kill switch protocol.

      • Update system property to limit records – Update the sn_ai_security.analyzer_max_record_age_hours system property to limit the age (in hours) of AI asset invocation records evaluated for AI asset security metrics.

      • Discover your agent network with the map – Use the agent map to visualize your entire agentic ecosystem, including ServiceNow and third-party AI assets.

      • Monitor AI asset security events and recommendations – Triage and resolve AI recommendations and AI asset security events to optimize AI asset security.

      • Contain AI agents manually using kill switch protocol – Deactivate or reinstate AI agents using kill switch protocol to eliminate malicious activity and improve your security posture.

      • Review the agent containment list – The agent containment list shows agents that were deactivated and reinstated for the instance manually with kill switch protocol or automatically by policy enforcement. You can view audit log information for each AI agent which can help you stay compliant with regulatory guidance and your business rules.

      • Reference – Reference topics provide additional details about the charts and metrics that you use to assess your AI asset security posture in AI Control Tower.

      • System properties – System properties that connect Veza access intelligence and limit the age of records evaluated for AI asset security metrics are available. Limiting records can prevent analyzing stale data and reduce unnecessary processing overhead.

    • Controlling AI asset usage – Create AI asset usage policies that automatically respond to detected AI threats or block an AI agent, domain, or model outright.

      • Explore – Learn how policies let you automatically respond to detected AI threats or block AI usage outright, and where policies are enforced across AI Control Tower.

      • Threat Response – Automatically contain an AI agent the moment it crosses a risk threshold you define, instead of waiting for someone to notice and act.

      • Explicit Block – Stop a specific person, a department, or your whole organization from using an AI agent, model, or domain, enforced automatically everywhere that access could happen.

      • Configure – Set up policies in AI Control Tower by configuring control enforcement points.

      • Configure AI agent containment automatically – Connect an identity provider and the AI agent runtime platforms to AI Control Tower so a Threat Response policy can automatically contain an AI agent using kill switch protocol.

      • Manage policies – Create, change, or remove the policies that respond to detected threats or block AI usage.

      • Create a Threat Response policy – Automatically contain an AI agent when a specific threat is detected.

      • Edit a Threat Response policy – Change an existing Threat Response policy’s threat type, scope, sensitivity, or follow-up actions.

      • Create an Explicit Block policy – Stop a user, a department, or everyone in your organization from using a specific AI agent, model, or domain.

      • Edit an Explicit Block policy – Change who an Explicit Block policy blocks, what they’re blocked from using, or its follow-up actions, without recreating the policy.

      • Clone a policy – Start a new policy using an existing policy as a template.

      • Delete a policy – Remove a policy you no longer need.

      • Review policy enforcement – Review policy activities and execution results.

      • Reference – Reference topics provide additional information about threat types and where policies are enforced.

      • Threat types – Threat types available in a Threat Response policy.

      • Control enforcement points – The systems an AI Control Tower policy can act on.

    • Monitor AI assets – Evaluate the quality and safety of AI interactions across your portfolio using automated scoring, configurable metrics, and trend analysis for both ServiceNow and external AI systems.

    • Monitoring and evaluating AI systems – Continuously monitor your ServiceNow and external AI systems to determine whether they meet quality and safety standards, and investigate root causes when they don’t.

      • Explore – Learn how AI Control Tower automatically scores live AI interactions against quality and safety metrics using an OpenTelemetry-based data model of sessions, traces, and spans.

      • Sessions, traces, and spans – Understand the three levels of AI interaction data that AI Control Tower uses to structure, score, and display runtime behavior.

      • How evaluation scoring works – Understand how AI Control Tower calculates quality and safety scores so you can interpret results accurately and configure scoring to reflect your priorities.

      • Evaluation scoring examples – Worked scoring examples show how metric configuration, weights, and evaluation levels combine to produce quality and safety scores for different AI system types.

      • Monitoring and value – Quality scores from monitoring can feed the value calculation. The value an AI system reports then reflects how well it performs, not just how often it is used.

      • Configure – Set up monitoring in AI Control Tower by activating evaluations, adding metrics, and configuring metric templates.

      • Configure evaluation scoring for ServiceNow AI systems – Monitor and configure evaluation scoring for AI systems that run on the ServiceNow AI Platform.

      • Configure evaluation scoring for external AI systems – Monitor and configure evaluation scoring for AI systems that run outside the ServiceNow AI Platform.

        • Activate evaluation scoring – Assess the quality and safety of your external AI systems by connecting them to AI Control Tower and activating evaluation scoring.

        • Configure global metrics – Choose which metrics evaluate all your external AI systems by default, exclude an individual system from a specific metric, and adjust how often each metric runs.

        • Configure asset-specific metrics – Add or remove metrics for one or more external AI systems without changing the global metric configuration, and adjust metric sample rates.

        • Exclude external AI systems from a metric – Exclude one or more external AI systems from a specific metric, without changing that metric’s configuration for every other system.

      • Configure an evaluation metric template – Adjust the scoring formula for a metric template to change which metrics contribute to quality or safety scores, or to change how much weight each metric carries.

      • Configure trace data retention and access – Control how long session, trace, and span data is stored to reduce storage usage, and choose whether that data stays available for analysis.

      • Managing evaluation cost – Reduce the assists that evaluation consumes by adjusting what you evaluate, how often you evaluate it, and which AI systems you evaluate.

      • Review scores – Determine whether your AI systems are meeting quality and safety targets by reviewing scores and evaluation trends.

      • Monitoring overview – Identify low-scoring AI systems quickly by reviewing quality and safety scores, AI-generated insights, and performance trends across your AI portfolio.

      • Review scores across your AI portfolio – Assess the quality and safety of your AI systems by reviewing scores, identifying low-scoring systems, and learning which metrics are affecting each score.

      • Investigate sessions – Narrow a quality or safety issue from a broad score to a specific cause by reviewing evaluated sessions and session details.

      • Evaluated sessions – View all evaluated AI sessions across your portfolio, compare quality and safety scores by AI system, and identify the sessions that need investigation.

      • Session details – View metric breakdowns, the judge’s reasoning, and trace and span data for a specific evaluated session.

      • Investigate a low-scoring session – Determine the root cause of a low quality or safety score by working through the session detail, trace timeline, and span-level input and output data.

      • Reference – Reference topics provide additional information about the evaluation metrics in AI Control Tower.

      • Evaluation metrics – All evaluation metrics available in AI Control Tower, with their categories, evaluation levels, scoring formats, and descriptions.

    • Measure AI systems – Measure quantifies the productivity value delivered by your AI systems and tracks their costs. It also reports the net return on AI, enabling you to determine where to scale, fund, or retire AI.

    • Measure AI system – Measure is the AI Control Tower capability for measuring the productivity gains, cost, and net AI return of your AI systems. It brings together value measurement, cost tracking, and dashboards.

      • Explore – Measure helps you determine whether your AI systems deliver more value than they cost. It brings value and cost data together in dashboards so that you can compare productivity gains with AI spend.

      • Value – Value translates AI usage into measurable productivity metrics.

        • Templates – Value templates define how the AI Control Tower calculates the productivity value of an AI system. Each template captures a persona, usage indicators, a time value type, and a quality score.

        • Calculation and Value jobs – Value jobs calculate the productivity value of your AI systems on a schedule. Automated jobs run each night, and manual jobs run monthly or quarterly for AI systems that you onboard manually.

        • Formula and metrics – The AI Control Tower calculates the productivity value of an AI system by multiplying usage, time saved per invocation, and the acceptance rate.

        • Value tracking – The AI Control Tower value dashboard shows value and usage only for AI systems that were managed during the reporting period. Value realized while an AI system was managed stays on the dashboard, even after the AI system becomes unmanaged.

        • Multi-Instance Framework (MIF) – The Multi-Instance Framework (MIF) synchronizes AI value data from a sub-production instance to a production instance only for Creator skills. In this setup, the AI Control Tower calculates value for Creator skills using data from development and testing environments.

      • Cost – Cost records the actual expenses associated with your AI systems, enabling AI Control Tower to compare these costs against productivity gains and calculate the net return on investment.

        • Cost and net AI return – The AI Control Tower tracks the cost of your AI systems and subtracts it from the productivity value to show the net AI return. Cost is tracked by vendor from token consumption or a direct cost.

        • Cost types and sub-vendor rates – Record what your organization pays each AI vendor so that AI Control Tower can calculate total Enterprise AI cost and net returns. Choose the cost type that matches each contract, and set sub-vendor rates where a specific service is billed differently from its vendor.

        • Subscription-based costs – Use the Subscription cost type in AI Control Tower to track vendor costs billed per licensed seat under a fixed-term contract rather than by token or usage.

        • Cost updates and calculation schedule – AI Control Tower preserves recorded cost data and calculates cost and value on a daily schedule. These rules determine when a cost change takes effect and when it appears in your dashboards.

      • Dashboards – Dashboards provide insights into the productivity gains, costs, and net returns of the AI systems within your enterprise. There are distinct dashboards for reporting on vendors and ServiceNow AI, ensuring that enterprise-wide metrics remain separate from those specific to ServiceNow AI systems.

        • Value dashboard – The Value dashboard provides real-time visibility into the productivity gains and financial returns delivered by your AI Control Tower agents. Track hours saved, cost savings, net returns, and system performance to measure ROI and demonstrate AI investment impact.

        • Engagement dashboard – The Engagement dashboard provides visibility into AI system usage patterns across your organization, including adoption metrics, department usage, and system adoption by geographic region.

        • ServiceNow AI dashboard – The ServiceNow AI dashboard is a central hub for monitoring and evaluating the performance, adoption, and financial impact of AI systems deployed across your ServiceNow environment.

        • Value dashboard – The ServiceNow AI Value dashboard provides a comprehensive view of AI system performance, value delivery, and operational metrics across your organization. The dashboard displays productivity gains, financial returns, system usage patterns, and agent performance data.

        • Engagement dashboard – The ServiceNow AI Engagement dashboard provides visibility into user adoption, utilization patterns, and performance metrics across all AI systems in your ServiceNow instance. Monitor AI action volume, user engagement trends, departmental usage, and task efficiency gains to identify adoption opportunities and system effectiveness.

        • Creator skills dashboard – The ServiceNow AI Creator skills dashboard provides insight into how your organization uses AI-powered creator skills—automated code generation, documentation, and testing capabilities—to boost developer productivity. Monitor skill satisfaction, usage patterns, and outcomes to optimize adoption and identify high-performing skills across your instance.

        • Evaluation dashboard – Use the ServiceNow AI Evaluation dashboard to get a comprehensive visibility into AI agent performance.

          • Conversation evaluation – The ServiceNow AI Conversation evaluation dashboard displays AI response quality metrics and satisfaction scores for individual conversations. It includes detailed evaluation records with human feedback and automated scoring across quality dimensions such as intent accuracy, request completion, truthfulness, and context retention.

          • Impact evaluation – The ServiceNow AI Impact evaluation dashboard displays business outcomes and operational results from AI conversations. Metrics include issue resolution rate, time saved, cost reduction, and other KPIs demonstrating AI business value.

      • Role based data visibility – The AI Control Tower shows value, engagement, and cost data for the AI systems in a user’s scope. AI stewards can view data for all AI systems in an instance. Product owners can view data only for the AI systems that they manage.

      • Conversational evaluator – Conversational evaluator measures the quality of Virtual Agent conversations in AI Control Tower.

      • Configure – Set up default template rules, the value job user, AI cost, and usage tracking so that the AI Control Tower can calculate value and cost.

      • Value – Configure the value templates, value jobs, and calculation settings that AI Control Tower (AICT) uses to measure the business value of your AI systems.

        • Review or create default template rules – Assign a default value template to each AI system category and vendor so that newly onboarded AI systems are mapped automatically.

        • Create and publish a value template – A value template defines the formula used to calculate productivity gains for mapped AI systems. After you publish a template, the value job runs automatically and populates the Value dashboard.

        • Edit a published value template – Change the department, description, or AI system mappings of a published value template, and remove templates that you no longer need while keeping value calculation uninterrupted.

        • Assign the value job user – Assign an active user to run value jobs so that the AI Control Tower can calculate value. When the assigned user is inactive, value calculation stops.

        • Create and run manual value jobs – Set up manual value jobs for AI systems that you onboard manually, so that the AI Control Tower calculates their value on a monthly or quarterly cadence.

        • Set up MIF for value calculations – Connect a sub-production instance to a production instance through the Multi-Instance Framework (MIF) so that the AI Control Tower can run value calculations across Creator skills.

        • Move an unmanaged AI system to managed – When an AI system is unmanaged, its value data stops appearing on the value dashboard and its value templates are unpublished. Move it back to managed in AI Control Tower to restore value tracking.

        • Set look back date range – Change how many days of missing value data AI Control Tower fills in when you mark an unmanaged AI system as managed again.

      • Cost – Configure the hourly rates and vendor costs that AI Control Tower (AICT) uses to calculate what your AI systems cost to run.

        • Configure hourly rates – Set the average hourly rate that the Cost Framework uses to convert hours saved into financial savings.

        • Add integrated vendor cost – Add the cost of a vendor or sub-vendor integrated with ServiceNow so that AI Control Tower includes it in the total Enterprise AI cost. Set a sub-vendor rate when a specific service is billed differently from the vendor.

        • Add other vendor costs – Configuring costs for LLM vendors without direct integrations with the AI Control Tower requires manual input. You must manually enter token usage, direct costs, or a per-seat subscription. The AI Control Tower can then include these costs in total AI cost calculations.

        • Review and submit the cost configuration – Review all Cost Framework configurations, verify the calculations, and submit to activate the Cost Framework.

      • Dashboards – Configure the tracking settings that determine which usage data appears on the dashboards in AI Control Tower (AICT).

      • Use – Use Dashboards to review and analyze the productivity gains, AI costs, and the net return on AI investment.

      • Review AI system value and engagement data – Review productivity gains, cost savings, and engagement results for AI systems. AI stewards see data for every AI system on the instance. Product owners see data for the AI systems that list them in the Managed by field.

      • Evaluate conversations – Use Conversational Evaluator in AI Control Tower to review conversation quality scores, and estimate the cost savings from Virtual Agent conversations.

      • Reference – Reference topics provide additional information about the required roles and Performance Analytics indicators, as well as the tables used in the value calculation.

      • PA indicators and tables – Learn about the out-of-the-box Performance Analytics (PA) indicators and the database tables that the AI Control Tower uses to calculate value.

      • Feature access by role – Access to features varies by role. The value, engagement, and cost features differ between AI stewards and product owners.

    • Address action items – Track and act on the governance work and AI-generated recommendations that AI Control Tower surfaces for you in Activity Center.

    • Managing tasks and approvals – Track and resolve the governance work that AI Control Tower generates as your AI portfolio grows, such as lifecycle tasks, security tasks, cases, and risk assessments.

      • Lifecycle tasks – Advance managed AI assets through controlled stages of governance by completing the reviews, assessments, and approvals that AI asset lifecycle playbooks require.

      • Complete a lifecycle task – Advance a managed AI asset through its lifecycle by completing the reviews, assessments, or approvals that an asset lifecycle playbook requires.

      • Security tasks – Address possible security threats, policy violations, and access issues on managed AI assets by acting on the security tasks the system generates for AI stewards and security reviewers.

      • Resolve a security task – Investigate a security concern that AI Control Tower has detected on a managed AI asset, take the appropriate remediation action, and document the resolution.

      • Change and offboarding requests – Make controlled changes to a managed AI asset, or retire an asset that is no longer needed, by submitting a request for AI steward review and approval.

      • Review and approve a change request – Evaluate a change request submitted by an asset owner and either approve the change so AI Control Tower can apply it, or reject the change so the asset remains unchanged.

      • Review and approve an offboarding request – Evaluate an offboarding request submitted by an asset owner and either approve the request to move the asset into the offboarding lifecycle, or reject the request so the asset remains active.

      • AI cases – Investigate and document an incident affecting an AI asset, such as a suspected security breach, data exposure, or policy violation, using a structured case record.

      • Create an AI case – Open an AI case to investigate and document an incident affecting an AI asset, such as a suspected breach, data exposure, or policy violation.

      • Risk assessments – Complete a risk assessment that AI Control Tower generates automatically for a managed AI asset, evaluating its inherent, control, and residual risk.

      • Issues – Track a problem identified with an AI asset, such as a performance or compliance gap, through to resolution with a documented action plan.

      • Create an issue – Track a problem identified with an AI asset, such as a performance or compliance gap, and document the action plan for resolving it.

      • Attestations – Respond to a request to acknowledge a compliance policy that applies to a managed AI asset you own or steward.

      • Policy exceptions – Request an approved, time-bound deviation from a policy or control objective that applies to an AI asset.

      • Create a policy exception – Request an approved, time-bound deviation from a policy or control objective that applies to an AI asset, supported by a justification and a risk assessment.

      • AI inquiries – Ask a question or request information related to AI governance, such as a question about a policy, a process, or a specific AI asset.

      • Create an AI inquiry – Submit an AI inquiry to ask a question or request information related to AI governance, such as a question about a policy, a process, or a specific AI asset.

    • Resolving AI recommendations – Resolve the AI recommendations that AI Control Tower generates when it detects an issue or an opportunity in your AI portfolio, using AI automation, a guided AI conversation, or your own review.

      • Resolve an AI recommendation – Address an issue or opportunity by running an unsupervised agent, working through a guided conversation with a supervised agent, or resolving the issue yourself.

    • Reference – Reference topics provide additional information about the roles, tables, data model attributes, and form fields used to configure and administer AI Control Tower.

    • Installed components – Several types of components are installed with activation of AI Control Tower, including plugins, user roles, and tables.

    • Enterprise Architecture for AICT plugin installation and upgrade considerations – Upgrading AI Risk and Compliance, Enterprise Architecture Workspace, and AI Control Tower Core out of sync with each other temporarily affects the AI system-to-business application association.

    • GenAI plugin agents and skills inventory – The ServiceNow Otto for AI Control Tower plugin (com.sn_aict_genai) includes agents and skills that enhance the AI asset management and governance.

    • AI Control Tower email notifications – Identify the email notifications that AI Control Tower sends automatically, the events that trigger each notification, and the recipients. These notifications keep AI Stewards, asset owners, and assignees informed so that they can take action promptly on AI asset approvals and lifecycle events.

    • Domain separation and AI Control Tower – Domain separation is supported for AI Control Tower. Domain separation enables you to separate data, processes, and administrative tasks into logical groupings called domains. You can control several aspects of this separation, including which users can see and access data.

    • System properties – Several system properties that affect core AI Control Tower are available.

  • AI Control Tower (legacy) – The AI Control Tower is a platform that connects different parts of an organization to speed up the AI adoption.

    • Explore – Explore and learn about the AI Control Tower and its role in managing the AI asset inventory, overseeing configurations, integrating with other applications, and handling the asset approval workflow.

    • AI Control Tower dashboard – The AI Control Tower dashboard displays a comprehensive overview of the AI status, AI inventory, and other AI related metrics.

      • AI Control Tower Home – Explore the AI Control Tower home page.

      • Overview tab – Explore the overview tab in AI Control Tower and the widgets available on the tab.

      • AI strategy tab in AI Control Tower – The AI strategy tab includes information about all the AI-related priorities, goals, planning items, and execution items—projects and demands in the AI Control Tower workspace.

      • AI asset inventory tab in AI Control Tower – The AI asset inventory includes all the AI-related assets used by an organization, including AI models, prompts, systems, and databases.

      • Value tab in AI Control Tower – Use the Value dashboard page to gain insights into the value realized from multiple types of AI in your organization.

      • AI Risk and Compliance lifecycle tasks in AI Control Tower – The Risk and Compliance tab in the AI Control Tower workspace lets users with roles AI Steward [sn_ai_governance.ai_steward], and AI Risk and Compliance Analyst [sn_grc_ai_gov.ai_risk_and_compliance_analyst] view and act on risk and compliance lifecycle tasks without switching to a separate workspace.

      • Adoption tab in AI Control Tower – Use the Adoption dashboard page to view data about user engagement and feedback, and adoption of every type of AI.

      • Health tab – Monitor the performance of guardrails enabled through AI Guardian.

      • Risk and compliance tab in AI Control Tower – The Risk and compliance tab on the AI Control Tower displays the risk classification of AI assets and the compliance posture for selected authority documents and policies.

      • AI cases tab in AI Control Tower – Track, monitor, and analyze your AI case workflows, identify your workflow bottlenecks, and check your accountability of your AI-related risks by using the AI risk and compliance dashboard. As an AI steward, you can also use the dashboard to track the status and trends of your AI-related inquiries.

      • Evaluation tab – The Evaluation tab contains the Evaluation dashboard, which is designed to measure, automate, and improve the quality of interactions with Virtual Agent. This dashboard addresses several key challenges to enhance the end-user experience and overall virtual agent utility.

        • Value tab – The Value tab in the Evaluation dashboard displays the value, efficiency, and time savings of the virtual agent. The information on this tab gives you a transparent and reliable estimation of the value delivered by the virtual agent, focusing on a quality-adjusted calculation of the time saved for users.

        • Human feedback for evaluations – Expand the Human feedback section to see details on evaluations and their satisfaction scores.

        • Enabling evaluations – Evaluate random conversations by enabling continuous monitoring.

        • Configuring evaluations – Configure conversation evaluations by disabling evaluations, adjusting execution logic and thresholds, and tuning scoring weights.

      • Security & privacy tab in AI Control Tower – Review AI asset security metrics such as access issues, dormant and privileged AI agents, and map the relationships of your ServiceNow agents, agentic workflows, and tools.

      • AI assets – An AI asset refers to a digital tool or resource that uses artificial intelligence to carry out particular tasks or address problems. Examples include machine learning models, chatbots, natural language processing systems, image recognition software, and similar technologies.

      • AI asset inventory – AI asset inventory includes all the AI assets such as AI models, AI systems, prompts, datasets, and MCP servers that an organization can use.

      • AI asset lifecycle – The AI asset lifecycle defines the stages for managing an AI system, model, prompt, or dataset throughout its useful life.

      • Approvals – The approvals are requests created to either approve or reject an asset. The Approvals menu displays a list of Now Assist approvals.

      • Cases – You can view all the assigned and unassigned AI cases in a list view in the AI Control Tower.

      • Inquiries – You can view all the assigned and unassigned inquiries in a list view in the AI Control Tower.

      • AI strategies and goals – You can track and monitor the progress of your strategic priorities, goals, planning items, and execution items—projects and demands classified as Artificial Intelligence in other ServiceNow applications.

      • AI Task – Use the All Security Tasks tab to view all AI security tasks for your instance. You can also create an AI task on this page.

      • Configurations – Explore the AI Control Tower Configurations page to manage and govern ServiceNow AI assets.

      • Data sharing, processing, and security in AI Control Tower – Explore the Data sharing, processing, and Security & privacy sections.

      • Controls – Explore the Controls section on the configurations page of the AI Control Tower.

        • Approvals – Explore the Approvals section on the Configurations page of AI Control Tower.

        • ai-model-providers –

        • Automation rules – The Automation rules define how AI assets are set to be under managed assets.

      • Multi-Instance Setup – The Multi-Instance Setup enables a prod (manager) instance to manage multiple sub-prod (managed) instances and facilitate communication for AI Control Tower.

      • Playbooks – The Playbook templates list has a record of Approval Playbook for Now Assist approvals and Asset lifecycle playbook templates.

      • Automation rules – The Automation rules define how AI assets are set to be under managed assets.

    • Product Owner portal – Explore the product owner view and navigate through the accessible tabs in the AI Control Tower.

      • My overview tab – The My overview tab in the AI Control Tower home page displays all active assets to the asset owner and the AI stewards for assets that they manage or own.

      • AI portfolio tab – The AI portfolio tab in the AI Control Tower home page displays all AI assets, which includes (active, inactive, and completed) to the workspace users.

    • Definitions of AI asset inventory – Define the AI asset inventory, and AI assets.

      • AI systems – Explore the AI systems in detail with examples.

      • Example of an AI system – The AI Control Tower application and the AI Risk and Compliance application play a critical role in managing and governing the responsible use of AI systems, especially in high-stakes domains such as banking and finance.

      • AI models – Explore the AI models in detail with examples.

      • Prompts – Explore the prompts in detail with examples.

      • Datasets – Explore the datasets in detail with examples.

    • Inter-dependencies – Explore the inter-dependencies of AI systems, AI models, and datasets.

    • Explore the third-party LLMs and regions – Explore all the scenarios for configuring the third-party LLMs and SLMs based on the regions.

    • Explore Now Assist AI asset discovery – Explore the synchronizing process of AI assets including models, datasets, prompts, skills, and agentic AI components into the AI Asset Inventory of AI Control Tower.

    • AI connections – AI connection is a core feature of the AI Control Tower. It provides a unified view of all AI assets across hyperscalers, AI apps, and agentic AI frameworks via Service Graph Connectors.

      • AI connections setup – Explore the AI connections setup page and its features.

      • Service Graph Connectors for AI Control Tower – You can use Service Graph Connectors (SGC) to import and integrate third-party data into CMDB and non-CMDB tables to create AI connections.

      • Anthropic – The AI Service Graph Connector for Anthropic discovers and imports AI Models and tracks usage data (per-user AI asset cost) into ServiceNow AI Control Tower.

      • AWS – The AI Service Graph Connector for Amazon enables you to discover and import AI assets from your AWS environment into ServiceNow AI Control Tower.

      • Databricks – The AI Service Graph Connector for Databricks enables you to discover and import AI assets from your Databricks environment into ServiceNow AI Control Tower.

      • GCP Vertex AI – The AI Service Graph Connector for GCP Vertex AI enables you to discover and import AI assets from your Google Cloud environment into ServiceNow AI Control Tower.

      • Hugging Face – The AI Service Graph Connector for Hugging Face enables you to discover and import AI assets from your Hugging Face environment into ServiceNow AI Control Tower.

      • IBM – The AI Service Graph Connector for IBM enables you to discover and import AI assets from your IBM environment into ServiceNow AI Control Tower.

        • Create an AI connection for IBM – Create an AI connection for IBM in AI Control Tower using the AI Service Graph Connector for IBM.

        • IBM APIs – Explore the IBM APIs used by the AI Service Graph Connector for IBM

        • Data mapping – The AI Service Graph Connector for IBM uses separate data sources and staging tables for each asset type, per service. The staged data is transformed and loaded into target CMDB tables.

        • Target CMDB classes – When you complete setting up the connection, you can configure the integration to periodically pull data from IBM. The data is saved in tables that extend from the Configuration Item [cmdb_ci] table.

        • Properties – AI Service Graph Connector for IBM properties control the behavior of the connector.

      • LangGraph – The AI Service Graph Connector for LangGraph enables you to discover and import AI assets from your LangGraph environment into ServiceNow AI Control Tower.

        • Create an AI connection for LangGraph – Create an AI connection for LangGraph in AI Control Tower using the AI Service Graph Connector for LangGraph (version 1.1.1).

        • LangGraph APIs – Explore the APIs used in AI Service Graph Connector for LangGraph.

        • Target Tables – Target tables for storing AI Service Graph Connector for LangGraph data.

      • Microsoft – The AI Service Graph Connector for Microsoft enables you to discover and import AI assets from Azure AI Foundry and Copilot Studio environments into ServiceNow AI Control Tower.

      • Moveworks – The AI Service Graph Connector for Moveworks enables you to discover and import AI assets from Moveworks environments into ServiceNow AI Control Tower.

      • n8n – The AI Service Graph Connector for n8n enables you to discover and import AI assets from your n8n environment into ServiceNow AI Control Tower.

        • Create an AI connection for n8n – Create an AI connection for n8n in AI Control Tower using the AI Service Graph Connector for n8n (Version 1.0.2).

        • Properties for n8n – AI Service Graph Connector for n8n properties control the behavior of the connector.

        • Target Tables – When you complete setting up the connection, you can configure the integration to periodically pull data from a n8n project. The data is saved in tables that extend from the CMDB CI classes and other non-CMDB classes.

      • OCI – The AI Service Graph Connector for OCI enables you to discover and import AI assets from your OCI environment into ServiceNow AI Control Tower.

        • Create an AI connection for OCI – Create an AI connection for OCI in AI Control Tower using the AI Service Graph Connector for OCI.

        • OCI APIs – Explore the OCI APIs used by the AI Service Graph Connector for OCI

        • Data mapping – The AI Service Graph Connector for OCI uses separate data sources and staging tables for each asset type, per service. The staged data is transformed and loaded into target CMDB tables.

        • Target CMDB classes – When you complete setting up the connection, you can configure the integration to periodically pull data from OCI. The data is saved in tables that extend from the Configuration Item [cmdb_ci] table.

      • OpenAI – The AI Service Graph Connector for OpenAI enables you to discover and import AI models and track model usage from your OpenAI environment into ServiceNow AI Control Tower.

      • Salesforce – The AI Service Graph Connector for Salesforce enables to discover and import AI agents, models, prompts, tools, and track agent usage from your Salesforce environment into ServiceNow AI Control Tower.

      • Snowflake – The AI Service Graph Connector for Snowflake enables you to discover and import AI assets from your Snowflake environment into ServiceNow AI Control Tower.

        • Create an AI connection for Snowflake – Create an AI connection for Snowflake in AI Control Tower using the AI Service Graph Connector for Snowflake (version 2.0.5).

        • Snowflake Statements API Reference – The connector uses the Snowflake Statements API to query AI assets and usage data. The following queries are executed during discovery and usage collection.

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    • Configure – Configure the AI Control Tower workflow.

    • Activation and installation of AI Control Tower – The following section provides an order of installing the applications and their dependent plugins.

    • Configure AI Control Tower – Configuring the AI Control Tower for Now Assist from the Configurations page.

    • Configure Multi-Instance management for AI Control Tower – Configuring Multi-Instance management for AI Control Tower.

    • Configure third-party LLMs using AI Control Tower – Use the AI Control Tower to configure third-party LLMs based on the data routing configuration for the APJC region..

    • Use – Use AI Control Tower for creating governance workflows.

    • Create an AI Control Tower Playbook workflow – Create a AI Control Tower Playbook workflow for Now Assist approvals to approve or reject an asset.

    • Create a ServiceNow Otto approval task – Create an ServiceNow Otto approval task to evaluate the assets.

    • Creating AI assets – You can create AI assets to track and manage the life cycles of your AI systems, AI models, prompts, and datasets.

    • Creating requests for AI assets – You can manage deployed AI assets effectively by creating change requests for updates to existing assets or by creating offboarding requests for retiring assets.

    • View AI assets by life-cycle stage – View AI assets based on the AI asset life-cycle stage that they are currently in. Use this information to determine which AI assets require your attention.

    • Enable or disable the management of an AI asset – Enable or disable the management of an AI asset by designating it as either managed or unmanaged.

    • Complete AI asset lifecycle – Complete the AI asset lifecycle process starting from assessment through deployment.

    • Using value templates – Use value templates to define, calculate, and track the value delivered by your AI systems and models.

      • Add a value template – Add a value template to define, calculate, and track the value delivered by an AI asset.

      • Edit a value template – Edit a value template to update how value is calculated for AI assets.

    • Create an AI case in the AI Control Tower – Create an AI case in the AI Control Tower by providing a detailed description, such as system behavior, affected users, and any relevant data. Ensure all necessary information, such as supporting attachments, is included for prompt resolution.

      • Create new AI case form – Use the Create New AI case form in the AI Control Tower to report an AI case with the necessary details.

    • Create an AI issue in the AI Control Tower – Identify and manage issues related to the impacted areas for the reported AI case in the AI Control Tower.

      • Create new AI issue form – Use the Create New Issue form to identify and manage issues related to the impacted areas for the reported AI case.

    • Using the access map – Learn how to use the Access map in AI Control Tower.

    • Create an Automation rule – Create an Automation rule to set AI assets as managed.

    • Reference – Reference topics for AI Control Tower.

    • Add value template form – Add value template form fields and descriptions.

    • AI Control Tower roles – Certain roles are installed along with the installation of the AI Control Tower.This section also covers roles which are installed with AI Risk and Compliance.

    • AI Control Tower email notifications – Email notifications are sent automatically when specific events occur across AI Control Tower workflows.

    • Tables installed with AI Control Tower – Certain Tables or fields are installed along with the activation of the AI Control Tower core plugin.

    • AI asset data model attributes – Additional attributes for the AI asset data model.

    • Evaluation dashboard reference – Reference topics for the Evaluation dashboard.

  • Knowledge Graph – The Knowledge Graph application, uses the structured and unstructured data from ServiceNow records, knowledge bases, and external sources to enhance the performance of ServiceNow Otto for Virtual Agent, AI agents, and generative AI skills.

  • MCP Server Console – MCP Server Console enables secure and governed access to functionality on a ServiceNow instance for AI applications with Model Context Protocol (MCP) servers. MCP servers extend ServiceNow AI Platform functionality into any external MCP client and employee experience over the Model Context Protocol.

    • Explore – Learn about how you can use Model Context Protocol (MCP) servers to allow AI applications to access data and perform actions on a ServiceNow instance.

    • Configure – Create a Model Context Protocol (MCP) server and configure the tools and inputs it exposes to MCP clients.

    • Create a server – Create a Model Context Protocol (MCP) server and configure which tools it exposes to MCP clients.

    • Creating tools – You can create tools from various tool categories to expose ServiceNow capabilities to Model Context Protocol (MCP) clients from MCP servers.

    • Create an app – Build, register, and display user interfaces along with your tool’s logic with MCP apps. This allows you to implement and manage interactive interfaces for your tools that can be displayed by MCP clients.

    • Monitoring dashboard – Explore MCP Server monitoring dashboard to review the performance and usage of the MCP servers and tools in a specific time frame.

    • Create an AI ACL – Create the necessary AI Access Control List (ACL) for the component to be called externally.

    • Check a Subflow or Action compatibility – After establishing the ACL, publish the component and check the compatibility staging table.

    • Connect – Connect to a Model Context Protocol (MCP) server from an MCP client by creating an OAuth inbound integration and configuring the client with the server details.

    • Create an OAuth inbound integration for an MCP client – Secure access to Model Context Protocol (MCP) servers on an instance by creating an OAuth inbound integration for each MCP client.

    • Integrating MCP server with third-party identity providers – Configure the ServiceNow Model Context Protocol (MCP) server to authenticate users through a third-party identity provider (IDP).

      • ServiceNow(Glide) configuration for IDP – This process involves setting up the necessary tables and mappings within your ServiceNow instance. This includes configuring ServiceNow, creating the MCP server, and connecting the MCP client.

      • Integrate and test IDP with MCP server – This phase within integration of MCP server with IDP involves connecting the MCP client (such as Claude Desktop) to the configured systems.

    • Configure an MCP client to connect to an MCP server – Configure a Model Context Protocol (MCP) client to connect to an MCP server and prompt the server to perform a task.

    • Reference – Reference topics include information about MCP Server Console roles, tables, and more.

    • Installed components – Several types of components are installed with activation of the MCP Server Console application, including tables and user roles.

    • Model Context Protocol Server Console tools – Use the following tools that are available by default with Model Context Protocol Server Console depending on the applications installed on your instance, such as ServiceNow Otto for CSM or ServiceNow Otto for ITSM.

    • Generative AI skill support in MCP Server Console – MCP Server Console supports creating tools for Model Context Protocol (MCP) servers from a subset of AI skills.

    • Knowledge Graph schema support in MCP Server Console – MCP Server Console supports creating tools from several preconfigured Knowledge Graph schemas, each suited to different types of queries.

    • Domain separation and MCP Server Console – If any conkeyrefs are broken, re-add them from the doc/source/reuse/domain-separation/domain-separation-overview.dita file.Domain separation is supported for MCP Server Console. Domain separation enables you to separate data, processes, and administrative tasks into logical groupings called domains. You can control several aspects of this separation, including which users can see and access data.

  • Agentic Usage Overview Dashboard – Monitor usage from inbound agentic connections to MCP servers from the Agentic Usage Overview Dashboard.

  • AI Readiness Evaluation – The AI Readiness Evaluation app helps prepare your organization for implementing agentic AI for ITSM and CSM, and five other jobs.

    • Explore – The AI Readiness Evaluation app automates assessment processes, evaluates data readiness impacting implementation, and provides actionable insights to promote adopting AI quickly. The app enables you to assess whether updates, installations, or customizations of your instance could affect implementation. The assessments provide direct hyperlinks to improve any issues found.

    • Configure – Run the scheduled jobs and complete the AI Readiness Evaluation guided setup configuration steps before viewing the generative AI and agentic AI assessment results.

    • Run the GenAI/AgenticAI Assessment scheduled job – Before you can view the agentic AI or generative AI assessment results, you must first run the GenAI/AgenticAI Assessment scheduled job.

    • Configure the AI Readiness Evaluation guided setup – Before you can review the Now Assist assessments in Workspaces AI Readiness Evaluation , you must first complete the configuration for the five HRSD jobs.

    • Run individual assessment scheduled jobs – Use individual scheduled jobs to assess readiness for generative and agentic AI implementations across your instance.

    • Use – The AI Readiness Evaluation app helps you find actionable items in your implementation preparation and provides direct hyperlinks to improve upon those gaps.

    • Using AI Readiness Evaluation dashboard – The AI Readiness Evaluation dashboard comes with two dashboard tabs: Agentic AI - Assessment and AI Assessment.

      • Agentic AI - Assessment dashboard tab – The AI Readiness Evaluation dashboard’s Agentic AO AI - Assessment tab helps determine agentic AI readiness for IT Service Management (ITSM) and Customer Service Management (CSM) by delivering automated, data-driven insights.

      • AI Assessment dashboard tab – The AI Readiness Evaluation dashboard’s AI Assessment dashboard tab helps determine generative AI readiness by delivering automated, data-driven insights.

    • Assessing readiness status – The AI Readiness Evaluation app shows you a high-level overview of your go or no-go status for your organization’s readiness to implement agentic AI, generative AI, or both.

    • Reviewing your AI assessment – This automated assessment process evaluates potential implementation impacts and provides actionable insights for your AI products.

    • Reviewing your Agentic AI assessment – This automated assessment process evaluates potential implementation impacts and provides actionable insights for agentic AI in ServiceNow Otto for IT Service Management (ITSM) and ServiceNow Otto for Customer Service Management (CSM).

    • Reference – AI Readiness Evaluation admins and implementers can work with select system properties for additional customization.

    • AI Readiness Evaluation system properties – Use system properties to customize your readiness assessment results. Access these properties from the System Property [sys_properties] table.

  • Natural Language Understanding – ServiceNow Natural Language Understanding (NLU) provides an NLU Workbench and an NLU inference service that you can use to enable the system to learn and respond to human-expressed intent. By entering natural language examples into the system, you help it understand word meanings and contexts so it can infer user or system actions.

    • Exploring Natural Language Understanding – ServiceNow Natural Language Understanding (NLU) provides an NLU Workbench and an NLU inference service that you can use to enable the system to learn and respond to human-expressed intent. By entering natural language examples into the system, you help it understand word meanings and contexts so it can infer user or system actions.

    • Activate the NLU Workbench – Activate the following plugins to activate the NLU Workbench.

    • NLU Workbench roles – NLU Workbench is installed with these roles.

    • NLU Workbench properties – Refer to these system properties for the Natural Language Understanding (NLU) application.

    • NLU language support – The NLU Workbench application provides support for creating NLU models in different languages for use in other applications, such as Virtual Agent.

    • NLU Service updates – Refer to this documentation so you are up to date with changes to the NLU Service.

    • NLU models – Use NLU models to apply ServiceNow Natural Language Understanding on your instances. Create, manage, test, and publish NLU models with the NLU Workbench.

    • Model management – Manage your NLU model’s life cycle in the NLU Workbench. Model management phases guide you through the iterative process of building, testing, and publishing your model.

    • Creating models – Creating models is the first step to taking advantage of Natural Language Understanding (NLU) in your instances. Create models for Virtual Agent and AI Search in the NLU Workbench.

      • Create an NLU model from blank – Create a Natural Language Understanding (NLU) model from scratch. Start with an empty model to have full control over the model’s content.

      • Create an NLU model from a CSV file – Upload a CSV or XLSX (Excel Workbook) file containing utterances and their intents to create a Natural Language Understanding (NLU) model. Use this method to quickly create models from your data or other exported models.

      • Create an NLU model using a pre-built model – Use the prebuilt Natural Language Understanding (NLU) models in the NLU Workbench to create a new one.

      • Duplicate an NLU model – Duplicate an existing Natural Language Understanding (NLU) model to create a new one. Duplicating a model copies the settings and contents of the original model, including its default test set.

      • Export an NLU model – Export a Natural Language Understanding (NLU) model to create a CSV file of the intents and utterances. You can then use the CSV file to edit, share, and import.

      • Add an NLU model to an update set – Use update sets to move your Natural Language Understanding (NLU) models from one instance to another. Update sets include all records needed for your model to function on the target instance.

      • Delete an NLU model – Delete a Natural Language Understanding (NLU) model permanently.

    • Build and train your model – After creating a model, build the model’s content by adding intents, entities, vocabulary, and test set utterances. Your NLU model content determines how the model responds to user inputs.

      • NLU intents – Intents drive your models’ responses by matching a system action to user inputs. Models with good intents help Virtual Agent and Search respond to your users accurately.

      • Create an NLU intent – Create an intent for your Natural Language Understanding (NLU) model. Intents provide your model with a system action to perform when it receives user input.

      • Reusing intents from prebuilt NLU models – Reuse Natural Language Understanding (NLU) intents by importing them from a prebuilt NLU model to other models. Reusing intents saves time when building your models.

      • Import an NLU intent – As you create intents for your Natural Language Understanding (NLU) model, you can also import and reuse intents from other models in the same application scope. Reusing intents saves time when building new models.

      • Resolve intent issues – Use the issue cards to identify intents that have conflicts, need reviewing, or need more utterances. Resolving intent issues ensures the intents in your Natural Language Understanding (NLU) models work properly.

      • NLU entities – Entities provide your model with additional context when receiving user input. Add entities to your utterances and intents to improve the predictions of your Natural Language Understanding (NLU) model.

      • Create a simple entity – Create one or more simple entities from words in your utterance examples. An entity is an object of, or context for, an action.

      • Create a mapped entity – Create an entity mapped to a vocabulary source, or to a list of values you manually create for the entity. Mapped entities can help provide multiple values the model can use as context when interpreting utterances.

      • Create a pattern entity – Create a pattern entity from a word or phrase with repeatable patterns, such as email addresses and phone numbers. These patterns help the system to recognize similar utterances based on the patterns.

      • Create a system-derived entity – Create a custom entity that’s derived from a default system entity such as date, time, duration, or location.

      • Create an open-ended entity – Use an open-ended entity when you want to improve intent prediction accuracy. Open-ended entities help your model focus on the context of the utterances.

      • Import entities – Reuse entities that you have created across your other Natural Language Understanding (NLU) models. Importing entities saves time and helps improve the intents in your model.

      • Using regular expressions in entities – Learn how to use regular expressions in your NLU entities to establish patterns that help the system locate, match, and manage text.

      • NLU vocabulary – Use NLU vocabulary items to help the system recognize the various ways your users express their requests. Use vocabulary sources to help the system recognize objects in tables or lists, such as names of conference rooms or catalog items.

      • Create a regular vocabulary item – Add a word or phrase that your users might use, and match that vocabulary item to a synonym. Your model uses the synonym during intent prediction.

      • Create a pattern vocabulary item – Use regular expression (regex) encoding to establish a pattern format for vocabulary items such as email addresses, phone numbers, and record naming conventions. You can create your own patterns for the vocabulary data in your instance.

      • Create a list vocabulary source – Create a list of words or phrases to act as a vocabulary source. The values in the list source are replaced by the synonym if they are detected in a user utterance.

      • Create a table vocabulary source – Use the values from a ServiceNow table as a vocabulary source. Your Natural Language Understanding (NLU) models use your provided synonym to interpret utterances that contain values from the chosen source fields of the table.

      • Sync a table vocabulary source – Synchronize your table vocabulary sources to obtain the latest changes to the ServiceNow source table. Synchronizing your vocabulary sources ensures your NLU models have the latest values when predicting intents.

      • Pre-built vocabulary – Use ServiceNow pre-built vocabulary for software and hardware terms so the system recognizes their multiple variations in utterances.

      • Test set creation and management – Use the default test set of your NLU model to test the model’s performance and accuracy. Manage your test set over time by building or updating its content in the NLU Workbench.

      • Train and try your NLU model – Train and try your model iteratively so that its intents and entities are validated, compiled, and saved to your model.

      • Test panel feedback – When testing your NLU model on the Try model section of the test panel, use this feature to provide feedback on the model’s intent predictions.

    • Test and publish your model – Assess the performance of your NLU model to identify areas for improvement. Then publish your model to make it available to other applications such as Virtual Agent.

      • Test your model – Test your Natural Language Understanding (NLU) model against its default test set. Testing helps determine how your model is performing with the current content.

      • Publish your NLU model – Publish your Natural Language Understanding (NLU) model to activate it and make it available for use in other applications that consume NLU.

      • Compare draft and published versions of your NLU model – Compare a draft trained Natural Language Understanding (NLU) model to its most recent published version. Test and review the changes to make sure that your draft model will have increased performance.

    • Irrelevance detection in NLU – Keep Virtual Agent chats focused with Irrelevance detection. Use the Irrelevance detection feature to train your NLU model to avoid making predictions for utterances that are not relevant.

    • Tune your model – On your model’s overview in NLU Workbench, open the Tune your model phase to review and incorporate user utterances from the Expert Feedback Loop.

    • NLU model settings – Change your NLU model’s name, description, or confidence threshold on the Settings page of the model overview.

    • Multilingual model management – Use multilingual Natural Language Understanding (NLU) models for the system to understand user input in several languages. The NLU Workbench helps you manage and maintain a consistent structure for content across languages to provide a unified experience.

    • Model language grouping – Language grouping makes it easier to manage your multilingual Natural Language Understanding (NLU) models. You can review existing language groups and designate new language groups.

    • Translate a multilingual model – Add a language to an existing NLU model by translating it. Use one of several translation options to add a secondary model in a supported language.

    • Enable or disable a secondary model intent – Enable and disable intents in your Natural Language Understanding (NLU) models to make them active or inactive. Disable intents while editors or admins edit, review, or update its content and translations.

    • Assign an NLU editor to a model – Assign an editor to review your Natural Language Understanding (NLU) model translations and edit model content. Delegate the maintenance, testing, and optimization of model content to an editor.

    • Import primary model content to a secondary model – When content in your primary NLU model is updated, you can import the updates directly to secondary models.

    • Virtual Agent and NLU Workbench integration – Virtual Agent administrators can access and update their NLU models from within the Virtual Agent Designer user interface.

    • NLU Workbench - Advanced Features – NLU Workbench - Advanced Features expands the functionality of NLU Workbench to help you manage and improve your models.

    • Install NLU Workbench - Advanced Features – You can install the NLU Workbench - Advanced Features application (com.snc.nlu.workbench.advanced) if you have the admin role.

    • Multi-model Batch Testing – Test multiple Natural Language Understanding (NLU) models against a large set of utterances to evaluate the performance of the models. Add test sets, test multiple models, and see test results.

      • Create a test set – To create or add to an NLU test set, you can upload a file of test utterances matched with correct intents. Use the test set to assess the performance of your model.

      • Run a multi-model batch test – Test multiple Natural Language Understanding (NLU) models against a test set. Evaluate the quality of your models and refine them to improve intent prediction.

    • Cross-model Conflict Review – Identify conflicting intents within or across models so you can take corrective actions, resolve such conflicts, and improve your NLU model performance.

    • NLU Model Performance – Use NLU Model Performance to see how well your models predicted intents in Virtual Agent (VA) based on end-user confirmation.

    • NLU Expert Feedback Loop – Provide feedback on Virtual Agent chat log utterances to help the system continuously learn and to better predict user input.

    • Issue Auto Resolution Tuning in NLU – Use the NLU Workbench homepage to support Issue Auto Resolution (IAR) tuning in NLU.

      • Issue Auto Resolution tuning options – When you are tuning your Issue Auto Resolution model in NLU Workbench, you can adjust the output for several goals: precision, automation, or a balance of the two. Compare how your choice of tuning options affect match rate and coverage, before committing.

    • Intent Discovery – Use the Intent Discovery application to help identify opportunities for incident deflection. For example, you can use it to identify which Virtual Agent conversations to activate next.

      • Install Intent Discovery – You can install the Intent Discovery application (sn_nlu_discovery) if you have the admin role.If the application does NOT include demo data or it does NOT install related applications and plugins, delete or revise the following sentence:

      • Components installed with Intent Discovery – Several types of components are installed with activation of the sn_nlu_discovery plugin, including tables.

  • Natural Language Query – Natural Language Query (NLQ) enables you to query the data in your instance by entering plain language requests into the user interface.

    • Exploring Natural Language Query – NLQ is a ServiceNow AI Platform feature that is active by default. Use NLQ to query the data in your instance by entering plain language requests into the user interface.

    • Using Natural Language Query – With Natural Language Query (NLQ), you can query data in your tables by entering requests in natural, everyday language.

    • Configuring NLQ – Enhance your users’ query experience by supplementing NLQ with words and terms used in your environment. Review your users’ actual requests in the NLQ logs to find possible synonyms and shortcuts to add.

    • Create an NLQ synonym – Add synonyms to improve the ability of NLQ to recognize the various ways your users request data. With synonyms, you can map commonly used words or terms to table columns.

    • Create an NLQ shortcut – Create a semantic shortcut to help improve the ability of NLQ to recognize the various ways your users request data. Semantic shortcuts operate similarly to NLQ synonyms by mapping common words to columns, but for a selected table when certain conditions are met.

    • View NLQ logs – Review NLQ logs to see how the system has handled your users’ plain-language requests. Use log records from attempted requests to expand NLQ synonyms or shortcuts.

    • View NLQ Table Guesser logs – Use the Table Guesser logs to review the CMDB tables that were picked by NLQ in response to plain-language queries.

    • Natural Language Query References – The following components are installed with Natural Language Query.

    • NLQ properties – The Natural Language Query (NLQ) properties control how and where NLQ operates.

    • Natural Language Query roles – Natural Language Query (NLQ) is installed with these roles.

  • AI Desktop Actions – ServiceNow AI Desktop Actions enables you to design, configure, and manage desktop actions that automate repetitive tasks in your desktop and web environment. AI agents can autonomously and semi-autonomously process instructions, generate execution plans, and run desktop actions across legacy systems, thick client applications, and web applications without APIs.

    • Explore – Create desktop actions with AI Desktop Actions to automate repetitive tasks on your desktop and web environment using AI agents and agentic workflows.

    • Adaptive desktop actions for web – Adaptive desktop actions enables AI agents to automate repetitive tasks across web applications through a browser extension. The agent interacts directly with the browser by clicking, typing, and scrolling, without preconfigured APIs, scripts, or back-end logic.

    • Adaptive desktop actions for desktop and web – Let an AI agent perform automated actions directly on your desktop across browsers, desktop applications, and files using AI Desktop Actions. You maintain control and can pause the automation at any time to review or adjust steps.

      • User interface – The AI Desktop Actions application includes a task input field, an execution workspace, and status indicators that show the current state of AI agent task execution.

    • Defined desktop actions for desktop – Automate desktop and web-based tasks that involve fixed steps using AI Desktop Actions.

      • Design workspace – The Design workspace is an interactive environment within AI Desktop Actions that enables you to create desktop actions by recording and configuring user interactions with desktop applications. The workspace provides a visual canvas where you can design multi-screen automation workflows that capture business processes across different applications.

      • Action recorder – With Action recorder, you can capture steps to automate repetitive tasks in AI Desktop Actions. You can save the steps that you perform on application elements as a reusable desktop action.

      • Execution workspace – Execution workspace enables you to test, run, and monitor your desktop actions. It enables you to observe how your automations interact with desktop applications, including handling situations where human input is needed.

    • When to use adaptive vs. defined path desktop actions – Use this guide to determine which type of desktop action best fits your automation scenario before you begin configuration.

    • Supporting information – Get a quick overview of the important information that is related to the AI Desktop Actions application.

    • Configure – You can enable the AI Desktop Actions application if you have the admin role.If the application does NOT include demo data or it does NOT install related applications and plugins, delete or revise the following sentence:

    • Adaptive desktop actions – Adaptive path desktop actions automatically adjust their behavior based on user context and system conditions. Configure these settings to optimize desktop action performance and user experience across different scenarios.

      • Install the Chrome extension – Install the ServiceNow Web Automation Chrome extension to the Google Chrome browser. The browser extension enables AI agents to interact with web applications during task execution.

      • Configure allowed websites – Specify a list of websites that AI agents configured with adaptive desktop actions are permitted to open and perform tasks.

      • File upload and download in adaptive desktop actions – Upload files to web forms and track file downloads during automated browser tasks. The agent validates file safety, confirms the target field with the reasoning model, and escalates to the user when it can’t proceed safely.

      • Configure Chrome to download files automatically – Configure Chrome so that the files can automatically complete downloads without manual intervention, and each download’s status and path can be tracked automatically.

      • Credential and dynamic parameter management – Logs in automatically when a goal references stored credentials configured for a website, without exposing sensitive values in chat or asking you to provide the input.

    • Adaptive desktop actions for desktop and web – Adaptive desktop actions adjust their execution based on user context and system conditions. Configure these settings to optimize desktop action performance and user experience across different scenarios for desktop and web-based tasks.

      • Download installer – Download and install the AI Desktop Actions installer on your macOS machine for running adaptive desktop actions.

      • Control resource access – Control which desktop resources, such as files, folders, websites, and applications, AI Desktop Actions can access within your organization.

      • Create a desktop action policy – Desktop action policies control which users are subject to resource access rules. Configure a policy to define the user criteria and link the rules that govern automated resource access.

      • Create a desktop action access rule – Configure rules to enforce security boundaries for AI agents in your organization. Resource access rules define which files, folders, websites, and applications AI Desktop Actions can access.

      • Reusable skills in adaptive desktop actions – Reusable skills save steps from completed adaptive desktop actions, allowing future requests with similar intent to run those steps with new data. This accelerates repeated tasks and reduces AI token consumption.

    • Defined desktop actions for desktop – AI Desktop Actions to execute predefined automation sequences on your desktop. Defined path actions provide consistent, repeatable workflows for common desktop tasks.

      • Download installer – Download the AI Desktop Actions installer so that you can install AI Desktop Actions on your Windows machine for designing and running defined desktop actions.

      • Download and install .Net Desktop Runtime – Reduce setup time and prevent installation errors by downloading and installing .Net Desktop Runtime following the instructions.

      • Enable AI agents to securely access parameters – Enable AI agents to securely access stored values, such as credentials and other input data, through Desktop Action Parameter records. Parameters protect sensitive values and provide dynamic inputs to desktop actions during agent execution.

      • Create a Desktop action parameter record – Create a Desktop action parameter record to store a name that an AI agent references when accessing credentials or other values during desktop action execution.

      • Create a parameter value record – Create a Desktop action parameter value record to store the value that an AI agent retrieves during desktop action execution.

    • Design defined desktop actions – Desktop actions enable you to automate repetitive tasks on your desktop and web applications. This capability helps you streamline repetitive tasks, improve efficiency, and integrate desktop application workflows into your ServiceNow processes.

    • Record steps with AI – Create desktop actions by recording steps with AI to automate repetitive tasks in AI Desktop Actions. AI validates anchor positions and generates screen contexts automatically after recording, reducing the risk of automation failures at runtime.

    • Record steps without AI – Create desktop actions by auto-capturing steps to automate repetitive tasks in AI Desktop Actions. You can save the steps that you perform on the application elements as a reusable desktop action of type on-screen task.

    • Manually capture steps – Extend the automation logic in a desktop action by manually capturing steps in AI Desktop Actions.

    • Add details – Add desktop action details, such as name, description, and associated applications, and review inputs and outputs.

    • Test and activate – Test the desktop action and then activate it so that it’s available as a tool in the AI Agent Studio. You can add this tool to the AI agents that execute desktop actions in your desktop environment.

    • Screen, anchor, and step properties – Learn about the properties of screens, anchors, and steps. There are multiple types of steps and each step type has distinct properties. You can update the properties to modify the behavior of the steps.

    • Increase payload limit – By default, maximum 10 MB of file size is allowed in a scripted REST API request payload. Increase the payload limit to 15 MB by creating system properties in the global scope.

    • Create AI agents and add desktop action tools – Create an AI agent and add desktop action tool in AI Agent Studio to mimic human-like intelligence while executing desktop actions for repetitive tasks in web and desktop environment.

    • Desktop action examples – Learn the key concepts and workflow for creating end-to-end desktop actions and building automations for your desktop environments.

    • Example: Badge management automation – Automate various tasks related to badge requests through desktop actions using AI Desktop Actions and AI agents.

    • Example: Shipping order processing – Automate various tasks related to shipping management through desktop actions using AI Desktop Actions and AI agents.

    • Execute desktop actions – These examples explain how to trigger AI Agents that execute automations you designed using desktop actions. The following topics walk you through the end-to-end execution flow so you understand how AI-driven execution works in your desktop environment.

    • Example 1: Defined desktop action - recorder – As an HR representative, automatically process various badge requests by triggering AI agents that use desktop action tools from the ServiceNow Otto panel.

    • Example 2: Defined desktop action - manual capture – As a shipping coordinator, enter shipping details automatically from Excel to the Shipping Management app by triggering AI agents that use desktop action tools from the ServiceNow Otto panel.

    • Example 3: Adaptive desktop action for web – Trigger an AI agent that uses adaptive desktop actions from the ServiceNow Otto panel. These desktop actions perform tasks on an external website or web application.

      • Delete an AI agent chat log – After you close an AI agent session, you can delete its chat if any sensitive information was captured. Deleting your chat log permanently erases the chat history of that session, including screenshots.

    • Example 4: Adaptive desktop action for desktop and web – Use AI Desktop Actions to automate desktop or web-based tasks. Describe your task to the AI agent, review and approve the execution plan, and monitor real-time progress.

    • Reference – Reference topics provide additional information about the roles and tables that are installed with the AI Desktop Actions application.

    • Components installed – Several types of components are installed with activation of the sn_desktop_agents plugin, including user roles and tables.

    • System requirements and limitations – Be aware of system requirements and a few limitations when you’re using the AI Desktop Actions application for defined desktop actions.

    • Considerations for file upload and download – Lists the details of the supported file types and validation rules.

    • Glossary – Learn about the terms and concepts that are unique to AI Desktop Actions.

      • A – Learn about the terms and concepts that are unique to AI Desktop Actions.

      • Action recorder – Learn about the terms and concepts that are unique to AI Desktop Actions.

      • Adaptive path desktop action – Learn about the terms and concepts that are unique to AI Desktop Actions.

      • AI agent – Learn about the terms and concepts that are unique to AI Desktop Actions.

      • AI Agent Studio – Learn about the terms and concepts that are unique to AI Desktop Actions.

      • Anchor – Learn about the terms and concepts that are unique to AI Desktop Actions.

      • Auto-capture – Learn about the terms and concepts that are unique to AI Desktop Actions.

      • B – Learn about the terms and concepts that are unique to AI Desktop Actions.

      • Background task – Learn about the terms and concepts that are unique to AI Desktop Actions.

      • C – Learn about the terms and concepts that are unique to AI Desktop Actions.

      • Confidence threshold – Learn about the terms and concepts that are unique to AI Desktop Actions.

      • D – Learn about the terms and concepts that are unique to AI Desktop Actions.

      • Defined path desktop action – Learn about the terms and concepts that are unique to AI Desktop Actions.

      • Design workspace – Learn about the terms and concepts that are unique to AI Desktop Actions.

      • Desktop action application – Learn about the terms and concepts that are unique to AI Desktop Actions.

      • Desktop-in-Desktop (DiD) mode – Learn about the terms and concepts that are unique to AI Desktop Actions.

      • Desktop session – Learn about the terms and concepts that are unique to AI Desktop Actions.

      • E – Learn about the terms and concepts that are unique to AI Desktop Actions.

      • Execution status – Learn about the terms and concepts that are unique to AI Desktop Actions.

      • Execution workspace – Learn about the terms and concepts that are unique to AI Desktop Actions.

      • M – Learn about the terms and concepts that are unique to AI Desktop Actions.

      • Manual capture – Learn about the terms and concepts that are unique to AI Desktop Actions.

      • N – Learn about the terms and concepts that are unique to AI Desktop Actions.

      • ServiceNow Otto panel – Learn about the terms and concepts that are unique to AI Desktop Actions.

      • O – Learn about the terms and concepts that are unique to AI Desktop Actions.

      • On-screen task – Learn about the terms and concepts that are unique to AI Desktop Actions.

      • P – Learn about the terms and concepts that are unique to AI Desktop Actions.

      • Parameter record – Learn about the terms and concepts that are unique to AI Desktop Actions.

      • Parameter Value record – Learn about the terms and concepts that are unique to AI Desktop Actions.

      • S – Learn about the terms and concepts that are unique to AI Desktop Actions.

      • Screen – Learn about the terms and concepts that are unique to AI Desktop Actions.

      • Smart sizing – Learn about the terms and concepts that are unique to AI Desktop Actions.

      • Step – Learn about the terms and concepts that are unique to AI Desktop Actions.

      • Step in / Step out – Learn about the terms and concepts that are unique to AI Desktop Actions.

      • T – Learn about the terms and concepts that are unique to AI Desktop Actions.

      • Tool – Learn about the terms and concepts that are unique to AI Desktop Actions.

  • Predictive Intelligence – Predictive Intelligence is a powerful interface to train machine learning models. With Predictive Intelligence, you can improve performance, efficiency, and flexibility to your systems across multiple business units.

    • Explore Predictive Intelligence – ServiceNow Predictive Intelligence is a platform function that provides a layer of artificial intelligence that empowers features and capabilities across ServiceNow applications to provide better work experiences.

    • Predictive Intelligence frameworks – Predictive Intelligence provides three different model frameworks in the Australia release: classification, similarity, and clustering. Each framework specializes in different types of predictions.

    • ServiceNow apps and features that use Predictive Intelligence – Learn about ServiceNow applications and features that leverage Predictive Intelligence. Solutions that you can adapt are available for various business units and industries.

    • Install Predictive Intelligence – Activate Predictive Intelligence on your instance and get started with basic configuration.

    • Implement Predictive Intelligence – Activate Predictive Intelligence on your instance and get started with basic configuration.

    • Configure Predictive Intelligence – Configure solution definitions for Predictive Intelligence to train, test, and monitor solution definitions

    • Database View support for Predictive Intelligence – Use database views to join two or more tables as input for your Machine Learning (ML) solution.

    • Configuration tips for Predictive Intelligence – If you encounter issues during your solution training and solution prediction, follow these suggested resolutions.

    • Create a custom stopwords list – Exclude common words you want the system to ignore during training and prediction.

    • Create a word corpus – Build a collection of words and phrases that functions as the vocabulary the system uses to compare your instance records based on their textual similarity. You can think of the word corpus as a dictionary that you want your machine-learning system to understand.

    • Quick start tests for Predictive Intelligence – Validate that Predictive Intelligence still works after you make any configuration change such as applying an upgrade or developing an application. Copy and customize these quick start tests to pass when using your instance-specific data.

    • Activate a solution version – Predictive Intelligence activates the most recent version of the solution when it completes training a solution. However, you can activate any previously trained solution version. Only one solution version can be active at a time, and only the active version is used when making predictions.

    • Export trained solutions to production – Refine and test your ML solutions iteratively on a non-production instance, and then use update sets to export the changes to your production instance. This practice mitigates the risk of retraining solutions on your live production instance.

    • Configuring advanced settings for your ML solutions – Learn about advanced settings for your Predictive Intelligence machine learning (ML) solutions. Apply optional technology and algorithms for classification, clustering, similarity, and regression capabilities.

    • Creating and training solutions – Use one of the Predictive Intelligence (PI) frameworks to create and train machine-learning solutions. Each framework delivers a different solution type for training the system to predict, recommend, and organize data outcomes.

    • Create and train a classification solution – Specify the records used to train a classification solution, what fields trigger a prediction, and how often you want to retrain your solution.

      • Exclude a class from prediction – Exclude a class from prediction if its precision or coverage aren’t satisfactory. Excluding a class prevents the classification model from predicting a particular output field value.

      • Exclude a class from solution training – Exclude a class from solution training to prevent the model from ever making predictions for a particular output field class. For example, you can exclude a particular incident category from training if you plan to retire or change the category.

      • Tune a trained classification solution – Tune the performance of a trained classification solution by configuring class level precision and coverage values.

      • Configuring target metrics for a trained classification solution – Set values for precision, coverage, and recall statistics for a trained machine learning solution.

      • Using Group By for classification – Use APIs to simultaneously submit multiple classification solutions for training based on the Group By field.

      • Model Explainability – Analyze the importance of each input field to your model’s predictions using model explainability. Create a Workflow Classification model that includes a graphical analysis of feature importance by executing the provided script.

    • Create and train a similarity solution – Create and train a machine learning solution to collect and compare your existing records to new similar records. For example, you can compare the text in an open Incident record to a resolved Incident record to reuse its resolution.

      • Update your similarity score threshold – After you review the similarity examples provided by the system, update your solution similarity score threshold if you want the results returned by the solution to be more or less similar.

    • Create and train a clustering solution – Group similar records into clusters so you can address them collectively or identify patterns.

      • Assign a name to a cluster – Name your clusters to help identify and organize them.

      • Generate a representative sample of a cluster – View the top 25 most representative records of a cluster.

      • Apply purity on a clustering solution – Apply purity to learn details about the composition of each cluster. For example, see what percentage of incidents in a cluster have the same value for Assignment Group. You can specify which fields to focus on, or you can let auto-purity display fields by default.

      • Analyze a cluster with Cluster Insight – Analyze a cluster by a field available on the source table. With the Cluster Insight check box, you can add a filter condition on your input field when you review the list of results.

    • Create and train a regression solution – Regression solutions have been deprecated. They could be used to predict numeric outputs, such as a temperature or a stock price.

    • View solution training progress – View your solution training progress or statistics to determine if a solution is available, or how long the next training cycle might take to complete.

      • Review classification solution statistics – The Solution Statistics dashboard in Predictive Intelligence has been deprecated in the Xanadu release. It provided precision and coverage statistics for each class in a classification solution.

      • Review solution similarity examples – Review the similarity examples generated during solution training to determine whether the similarity score threshold meets your business requirements.

    • Using Predictive Intelligence – Train and use Predictive Intelligence solutions to accomplish various tasks.

    • Using Machine Learning APIs – Use ServiceNow Machine Learning (ML) APIs to train Machine Learning models and run inferences.

    • Reviewing your ML solution training jobs – Use the ML Solutions (ML Training Jobs view) module to monitor the training status and progress for Predictive Intelligence solutions. The module displays training jobs for both user interface and API solutions.

    • Use Predictive Intelligence in Workflow Studio with ML actions – Use Predictive Intelligence actions in Workflow Studio to create flows that incorporate your model predictions.

    • Using MLSolutionFactory scriptable objects – MLSolutionFactory scriptable objects enable defining ML functionality. You can use the APIs to compose data-driven functionality, such as subclustering large clusters or clusters with multiple PRBs attached.

    • Preserve ML solutions during a system clone – Save your trained machine-learning (ML) solution data during a system clone.

    • Testing and monitoring predictions – Evaluate the coverage and precision of your machine-learning (ML) solutions by testing them. Once deployed, track their performance over time. Improve predictions by using performance information to refine your solutions.

    • Test a classification solution prediction – Once your machine-learning (ML) solutions are trained, you can call on the Predictive Intelligence API to make a solution prediction. In this example procedure, we use the REST API Explorer to test a classification solution prediction for incident categorization.

    • Test a similarity solution prediction – Once your machine-learning (ML) solutions are trained, you can call on the Predictive Intelligence API to make a solution prediction. In this example procedure, we use the REST API Explorer application to test a similarity solution prediction for resolved incident recommendations.

    • Track classification prediction results over time – Use the Prediction Results dashboard to determine if classification solution predictions are improving over time. Identify solutions that need refining or retraining.

    • Reviewing prediction errors with the Observability Dashboard – The Observability Dashboard offers a unified view and actionable insights for errors detected in Predictive Intelligence. Use this dashboard to visualize logged errors and gain information on prediction reliability and potential problem areas.

    • Predictive Intelligence Usage Analytics dashboard – The Predictive Intelligence Usage Analytics dashboard is a central location to understand the effectiveness and overall value of all your Predictive Intelligence solutions. View metrics for model training successes and failures. Monitor prediction statistics including breakdowns by individual model.

    • Predictive Intelligence references – Reference pages for components of Predictive Intelligence.

    • Domain separation and Predictive Intelligence – Domain separation is supported in the Predictive Intelligence application. Domain separation enables you to separate data, processes, and administrative tasks into logical groupings called domains. You can control several aspects of this separation, including which users can see and access data.

    • Data Encryption in Predictive Intelligence – Learn which types of encryption are supported for training Predictive Intelligence solutions.

    • Predictive Intelligence language support – Predictive Intelligence provides international language support. Learn which languages are available to Predictive Intelligence solutions.

    • Predictive Intelligence properties – The properties for Predictive Intelligence control certain parameters of its machine-learning solutions, solution training process, and caching.

    • Predictive Intelligence roles – Predictive Intelligence is installed with these roles.

  • Content Understanding – Content Understanding uses AI to extract and summarize information from documents and images, so teams can act on results faster.

    • Explore – Content Understanding brings generative AI to document and visual processing, so you can extract, analyze, summarize, and answer questions about the content in your files.

    • Benefits – Content Understanding includes AI-powered skills and AI agents that extract, process, and summarize information from documents and images. Each capability delivers a distinct benefit for administrators and end users.

    • Information Extraction skill – The Extract Information from documents skill (Information Extraction skill) uses generative AI to automatically extract content from documents and images, and to respond to questions about that content.

      • Extraction methods – The Extract information from documents skill (Information Extraction skill) supports three extraction methods—field, table, and Q&A—each suited for different types of document data. Understanding which method to use helps you capture and store the right information in ServiceNow.

      • Predictions – A prediction is the value that the Information Extraction skill infers for each field, table, or question in a use case. Predictions drive automation decisions and can be applied automatically or reviewed by a person, depending on the use case configuration.

      • Use cases – Use cases in the Information Extraction skill define what documents to process and what information to extract, so generative AI can accurately retrieve structured data from unstructured documents.

      • Automation modes – Automation modes in the Information Extraction skill determine whether predictions are accepted automatically or routed to a human agent for review before a document task completes.

      • Workflow – Information Extraction is a skill that analyzes documents to provide values for defined fields, table columns, and questions in a use case. It populates ServiceNow records with extracted data from documents.

    • Attachment Summarization – The attachment summarization skill summarizes a record’s attachments alongside the record summary, so agents can review key content without opening each file.

    • Content insights AI agent – The Content insights AI agent understands documents and images, answers questions, extracts structured data, and summarizes content within a single interface.

    • Configure – Configure the capabilities in Content Understanding to extract, analyze, and answer questions about your document and visual content.

    • Information Extraction skill – Configure the Extract information from documents skill (Information Extraction skill) to define how AI agents identify and pull structured data from unstructured sources. Use this configuration to control extraction behavior, map output fields, and integrate results into your workflows.

      • Activate the skill – Activate the Information Extraction skill, so AI agents can analyze and extract information from documents using generative AI.

      • Set up a use case – Create a use case to define the information to extract from a document for processing.

      • Test a use case – Test a use case with a sample document to verify how well it extracts and processes information. Upload a document from a record or your device, then review the test outputs before saving your results.

      • Add integration – Connect a use case to a workflow by adding an integration. Integrations automate document task creation or value extraction based on triggers in the target table.

      • Review and activate – Review your configuration selections before completing the setup for a use case. Return to any previous step to make changes before finalizing.

      • Turn on full automation mode – Turn on Full automation mode to automatically complete and submit document tasks without an agent review. Full automation mode is turned off by default in document extraction use cases.

      • Manage use case – Administrators can manage an existing use case by changing the LLM, editing configuration details, creating a copy, deactivating, or deleting it.

      • Change the use case LLM – Select the LLM and file language for a use case to control how documents are processed for information extraction. Change these settings when the default model doesn’t suit your use case.

      • Copy a use case – Copy an existing use case to use as a starting point for a similar one. The copy includes the original fields, tables, questions, integrations, and flows.

      • Edit a use case – Update an existing use case to modify its name, fields, tables, questions, integrations, and flows. Use cases marked as read-only must be copied before you can edit them.

      • Deactivate a use case – Deactivate a use case to stop it from being applied during document processing when it is no longer relevant to your document intelligence workflows.

      • Delete a use case – Delete a use case and its associated fields, tables, questions, integrations, and flows from a skill when the use case is no longer needed.

    • Content insights AI agent – Configure the Content Insights AI agent to control which roles can access it, how it is triggered, and which chat assistants surface it.

    • Attachment Summarization skill – Configuring Attachment Summarization skill to automate key information extraction from documents.

    • Use – Use Content Understanding to process and extract meaning from unstructured content in your ServiceNow instance. This section provides an overview of the workflow and links to the tasks required to complete the process.

    • Information Extraction skill – The Information Extraction skill generates predictions from activated use cases. In agent review mode, you review and correct predictions before the extracted values populate your records.

      • Review predictions – After a use case is activated in agent review mode, you can review the predictions generated by the Information Extraction skill and, if needed, modify them before submitting.

    • Content insights AI agent – Use the Content Insights AI agent to generate information from your inputs, attached documents, or images

      • Question and answer – Use question and answer (QnA) to ask the Content Insights AI agent specific questions about document or image and get cited answers. QnA is the agent’s default mode.

      • Key information extraction – Use key information extraction (KIE) to extract structured data from documents with a predefined template, for processing standardized documents such as invoices, contracts, or forms.

      • Document summarization – Use the Content Insights AI agent to generate a concise summary of document or image from a record reference or uploaded attachment.

    • Reference – Reference topics provide additional information about the lists and forms that you use to configure and administer Content Understanding.

    • Personas – Personas define the roles that interact with Content Understanding and the actions each role performs.

    • Supported languages – Use this reference to identify which languages the Content Understanding application supports for text-based and image-based files, and which optical character recognition (OCR) model applies to each language group.

    • LLMs – The Content Understanding application uses large language models (LLMs) to support generative AI and agentic AI capabilities.

    • Forms – Reference forms provide fields for viewing and updating Content Understanding records, including fields, questions, and tables.

      • Field form – The field form defines the information to extract from a document, including the field name, type, and target table.

      • Question form – The Question form contains fields that define a question and how its answer is extracted from a document in a use case.

      • Table form – The Table form contains fields for defining a table and its columns for document extraction in a use case.

    • Field types – Field types determine what kind of information is extracted from a document and how that information is stored or displayed in a use case.

    • Integrations – Content Understanding integrates with ServiceNow solutions to extract, analyze, and summarize information documents.

    • Data normalization – Data extracted from documents is converted into a standard format. This ensures that values appear consistently across all fields, making the data easier to group, analyze, and integrate with other applications.

    • Limitations – Review the file format, size, page count, and language limitations that apply to skills and AI agents in Content Understanding.

  • Now Assist in Document Intelligence (Legacy) – With ServiceNow Now Assist in Document Intelligence, you can use generative AI to get key information from digital documents into your automation workflows.

    • Explore – With Now Assist in Document Intelligence, you can use generative AI to extract key information from your documents and images for use in your workflows.

    • Use cases – In Now Assist in Document Intelligence, a use case is used to define the information you want generative AI to detect from a document.

    • Document Intelligence workspace – Use the Document Intelligence workspace to review the generative AI predictions made by Now Assist for a document or image file. You can also identify missing information in the file.

    • Supporting information – Get a quick overview of the important information that is related to the Now Assist in Document Intelligence application.

    • Configure – If you have the admin role, you can configure the Now Assist in Document Intelligence feature, enabling agents to use its generative AI skills within their application workspace.

    • Activate a skill – Activate the Now Assist in Document Intelligence skills that agents can use to help analyze and extract information from documents with generative AI.

    • Set up a use case – Create a use case record to define a document you want to process with Now Assist in Document Intelligence.

    • Turn on Full automation mode – Turn on Full automation mode to automatically complete and submit document tasks without an agent review. Full automation mode is turned off by default in use cases.

    • Change the use case LLM – Choose the language models for a Now Assist in Document Intelligence use case.

    • Edit a use case – Edit a use case to change the name, fields, tables, questions, and integrations.

    • Make a copy of a use case – Make a copy of a use case to save time when you need to create a new use case with a similar structure.

    • Deactivate a use case – Deactivate a use case that you don’t want to use for your documents.

    • Delete a use case – Delete a use case when it is no longer needed for your documents.

    • Use – If you have an agent role, you can use the Now Assist in Document Intelligence workspace to analyze and extract information from your documents with generative AI.

    • Review extracted information – Use the Document Intelligence workspace to review the information that was extracted from a document or image file by Now Assist in Document Intelligence.

    • Reference – The following topics provide additional information about the features and properties installed with Now Assist in Document Intelligence.

    • Components installed – Components are installed with the activation of Now Assist in Document Intelligence.

    • Data extraction modes – The extraction mode determines how Now Assist in Document Intelligence processes a document task.

    • Document and visual insights AI agent – The document and visual insights AI agent gathers context from user input and document or image attachments, generates the requested information based on the content, and provides the information along with any relevant task details.

    • Field types – The field type specifies the information that is retrieved from a document with Now Assist in Document Intelligence.

    • Forms – Use forms to view and update Now Assist in Document Intelligence information.

      • Question form – The Question form enables you to define a question you want to ask about the document.

      • Field form – The Field form enables you to define a single field for extraction.

      • Table form – The Table form enables you to define a table for extraction.

    • Limitations – There are several important limitations to be aware of when you’re using Now Assist in Document Intelligence.

    • LLMs – Now Assist in Document Intelligence uses large language models (LLMs) to perform generative AI and agentic AI capabilities.

    • Supported languages – Now Assist in Document Intelligence supports text in multiple languages.

  • Document Intelligence (Legacy) – Document Intelligence (DocIntel) is an AI solution that enables any organization to automate and accelerate the process of extracting data from documents. That data can easily be integrated into larger automation workflows to save time and resources.

    • Explore – Document Intelligence helps you to quickly and accurately classify and extract information from documents using artificial intelligence (AI).

    • Configure – Activate Document Intelligence on your instance and get started with basic configuration.

    • Set up DocIntel – Review the following information before you start setting up Document Intelligence.

    • Install DocIntel – You can install Document Intelligence (sn_docintel) and Document Intelligence Admin (sn_docintel_admin) if you have the admin role. The sn_docintel_admin application installs related ServiceNow Store dependencies if they aren’t already installed.

    • Upgrade to version 3.0 or later – Document Intelligence 3.0 or later includes an updated database schema to support its transition from a scoped application to a ServiceNow AI Platform plugin.

    • Configure settings – Use general settings to control how Document Intelligence displays information to users.

    • Set up document extraction use cases – In Document Intelligence, a use case is used to define the structure of a type of document you want to process. It’s made up of the use case record and its related fields, field groups, integrations, flows, and all the related machine learning (ML) models.

      • Create a use case – Create a use case record to define a document you want to process in Document Intelligence. For example, invoices or driving licenses.

      • Create a field – Set up fields as part of your use case. Document Intelligence uses fields to identify and extract data from documents. Fields can be grouped to help DocIntel extract data from documents with tables, check box lists, and other logical groupings of fields.

      • Deactivate a field – Deactivate fields that you don’t want to use as part of your use case.

      • Configure data extraction modes – Configure the extraction modes for use cases to define how Document Intelligence extracts fields from documents.

    • Manage document extraction use cases – Manage your Document Intelligence use cases to efficiently support your document extraction requirements.

      • Duplicate a use case – Make a copy of a use case to save time when you want to create a new use case that shares a similar structure to another.

      • Export a use case – Export a document extraction use case for use in another ServiceNow instance by adding it to an update set.

      • Import a use case – Import a document extraction use case for use in your ServiceNow instance.

      • Delete a use case – Delete a use case when it’s no longer needed for your documents.

    • Set up document classification use cases – A document classification use case is a set of categories used to classify your documents and their individual pages. It’s made up of the use case record and its related fields (classes), and all related machine learning (ML) models.

      • Create a use case – Create a use case record to begin defining the classes or categories that you want to apply to a type of document or pages within the document.

      • Create a document class – Create fields as part of your document classification use case. Document Intelligence uses fields to define the classes or categories to apply to documents.

      • Train a use case – Train the document classification use case with user input from completed document tasks to improve Document Intelligence recommendations over time.

    • Manage document classification use cases – Manage your Document Intelligence use cases to efficiently support your document classification requirements.

      • Delete a use case – Delete a use case when it’s no longer needed for your documents.

    • Manage field values – View the field values gathered from your processed document tasks. Review the values and add any additional information.

    • Integrate – Extend the capabilities of Document Intelligence to other ServiceNow applications. Other applications can take advantage of document classification and extraction using Document Intelligence.

    • Integrate with a custom application or workflow – Configure an integration to trigger document task processing or value extraction. Integrations can be used to quickly set up flows with other applications.

    • Integrate with CSM – Document Intelligence provides document extraction capabilities to Customer Service Management (CSM). Extract relevant information from email and case attachments, such as credit card numbers or customer addresses, and add that information to cases.

    • Integrate with FSO – Document Intelligence provides document extraction capabilities to Financial Services Operations (FSO).

    • Integrate with APO – Document Intelligence provides document extraction capabilities to Accounts Payable Operations (com.sn_ap_ic).

    • Integrate with Automation Center – Use Automation Center to discover opportunities to automate document processing with Document Intelligence.

    • Use – Use document tasks to process documents for classification and data extraction in Document Intelligence.

    • Create a document task – Create a document task and upload single or multi-page documents that are in JPEG, PNG, or PDF formats to start extracting text or classifying documents.

    • Complete a document task – After the document task processing is finished, complete the task by providing input or review to train the AI.

      • Extract fields – Use the Document Intelligence workspace for field extraction, searching for recommendations, flagging fields, and identifying missing fields to complete document tasks.

      • Extract single fields – Use the Document Intelligence workspace for field extraction, searching for recommendations, flagging fields, and identifying missing fields to complete document tasks.

      • Extract check box fields – Use the Document Intelligence workspace for field extraction, searching for recommendations, flagging fields, and identifying missing fields to complete document tasks.

      • Extract table fields – Use the Document Intelligence workspace for field extraction, searching for recommendations, flagging fields, and identifying missing fields to complete document tasks.

      • Extract fields using the draw tool – Use the draw tool to extract information directly from text and tables on the document image.

      • Extract a single field using the draw tool – Use the draw tool to extract information directly from text and tables on the document image.

      • Extract a table using the draw tool – Use the draw tool to extract information directly from text and tables on the document image.

      • Adjust the draw tool grid – Use the draw tool to extract information directly from text and tables on the document image.

      • Review a field on the document image – Use the draw tool to extract information directly from text and tables on the document image.

      • Edit a field on the document image – Use the draw tool to extract information directly from text and tables on the document image.

      • Classify documents – Use the Document Intelligence workspace to label your documents. The workspace enables you to train the AI model by providing direct input and by validating or correcting the recommendations provided by DocIntel.

      • Classify documents and document pages – Use the Document Intelligence workspace to label your documents. The workspace enables you to train the AI model by providing direct input and by validating or correcting the recommendations provided by DocIntel.

    • Monitor – Track document extraction performance in Document Intelligence to understand its usage and effectiveness.

    • View reports on the DocIntel home page – Monitor document extraction performance in the Admin experience.

    • Document Intelligence monitoring dashboard – Monitor the overall performance of Document Intelligence over time in the Document Intelligence monitoring dashboard.

    • Use case performance dashboard – Monitor Document Intelligence (DocIntel) performance at the use case and field levels in the use case performance dashboard.

    • Reference – The following topics provide additional information about the features and properties installed with Document Intelligence.

    • Components installed with DocIntel – Several types of components are installed with activation of the Document Intelligence plugin, including tables and user roles.

    • Confidence scores – A confidence score is a measurement (percentage) of how reliable DocIntel is in providing a recommendation for a field. The higher the score, the more reliable the recommendation.

    • Data extraction modes – Extraction modes determine how the data is extracted in the document task and how the task is processed. The mode changes the behavior of the fields in the Document Intelligence workspace.

    • Data normalization – Certain types of data extracted from documents are converted into a standard format so that they appear the same across all fields.

    • Document field statuses – The following is a list of the statuses for fields in DocIntel document tasks. These statuses apply to fields for both document classification and data extraction.

    • DocIntel forms – Use forms to view and update Document Intelligence information.

      • Check box list form – The Check box list form enables you to define a check box list for extraction.

      • Single field form – The Single field form enables you to define a single field for extraction.

      • Single field group form – The Single field group form enables you to define a related group of single fields for extraction.

      • Table form – The Table form enables you to define a table for extraction.

    • Properties – Document Intelligence (DocIntel) system properties control the behavior of the Document Intelligence application.

    • Roles – Document Intelligence is installed with these roles.

    • Terminology – Before getting started with Document Intelligence (DocIntel), it’s important to understand some key concepts used in the application.

      • classification – Before getting started with Document Intelligence (DocIntel), it’s important to understand some key concepts used in the application.

      • confidence score – Before getting started with Document Intelligence (DocIntel), it’s important to understand some key concepts used in the application.

      • document class – Before getting started with Document Intelligence (DocIntel), it’s important to understand some key concepts used in the application.

      • document task – Before getting started with Document Intelligence (DocIntel), it’s important to understand some key concepts used in the application.

      • extraction – Before getting started with Document Intelligence (DocIntel), it’s important to understand some key concepts used in the application.

      • field – Before getting started with Document Intelligence (DocIntel), it’s important to understand some key concepts used in the application.

      • field group – Before getting started with Document Intelligence (DocIntel), it’s important to understand some key concepts used in the application.

      • field value – Before getting started with Document Intelligence (DocIntel), it’s important to understand some key concepts used in the application.

      • recommendation – Before getting started with Document Intelligence (DocIntel), it’s important to understand some key concepts used in the application.

      • unstructured document – Before getting started with Document Intelligence (DocIntel), it’s important to understand some key concepts used in the application.

      • use case – Before getting started with Document Intelligence (DocIntel), it’s important to understand some key concepts used in the application.

    • Document task statuses – The following is a list of the statuses for DocIntel document tasks. These statuses apply to tasks for both document classification and data extraction.

    • Domain separation – This domain separation overview relates to Document Intelligence. Domain separation enables you to separate data, processes, and administrative tasks into logical groupings called domains. You can then control several aspects of this separation, including which users can see and access data.

    • Supported languages – The Document Intelligence application provides support for documents in different languages.

    • DocIntel limitations – There are several important limitations to be aware of when you’re using Document Intelligence.

  • Task Intelligence – Task Intelligence uses machine learning to train solutions with your data and achieve important outcomes.

  • ServiceNow AI Lens – With ServiceNow AI Lens, which is a ServiceNow AI application, you can use generative AI to scan, extract, comprehend, and synthesize data to optimize your workflows.

    • Explore – ServiceNow AI Lens is a ServiceNow AI application that uses generative AI to scan images and screens, extract information, understand visual data, and take action—such as automatically filling in forms with scanned information—to help improve your workflows.

    • ServiceNow AI Lens features – Learn about the various ServiceNow AI Lens features to help you get started with analyzing and gathering insights from the visual data.

    • Desktop app landing page overview – Learn about the ServiceNow AI Lens desktop application landing page.

    • Supporting information – Get a quick overview of the important information that is related to the ServiceNow AI Lens application.

    • ServiceNow AI Lens skill – This skill provides generative AI capabilities to read, understand, respond, and act on visual data such as hand-written texts, images, and websites and take powerful actions to boost productivity.

    • Configure – If you have the admin role, you can enable the ServiceNow AI Lens application (sn_ai_lens) to extract and comprehend data for various workflows.

    • Activate the ServiceNow AI Lens skill – Activate the ServiceNow AI Lens skill to start using generative AI to scan and analyze visual data such as scan artifacts such as images, scanned handwritten notes, Excel sheets, web pages and optimize your workflows.

    • Download ServiceNow AI Lens and set launch preference – Download the ServiceNow AI Lens installer for your operating system and set your preferred way to launch ServiceNow AI Lens — from your browser or the desktop application.

    • Set up auto-login for ServiceNow AI Lens – Pre-configure your organization’s ServiceNow instance URL so that it appears ready-filled when users launch the ServiceNow AI Lens desktop application, and enable auto-login so that users can skip signing in on subsequent launches.

    • Define default instructions – Create a system property to define default instructions for ServiceNow AI Lens execution on a specific form.

    • Creating Lens actions – As a Lens admin, you can create Lens actions in ServiceNow AI Lens to customize Lens behavior by providing default instructions, configuring context, and more.

    • Enabling ServiceNow AI Lens in Virtual Agent – You can enable ServiceNow AI Lens in Virtual Agent so that ServiceNow AI Lens can be triggered from Virtual Agent conversation.

    • Use – If you have a lens_user role, and have activated the ServiceNow AI Lens skill, you can use the ServiceNow AI Lens application to capture screens or upload files, extract and comprehend data from the captured screenshots or uploaded files, auto-fill form fields, or preview the extracted data.

    • Create or update a record in an instance – Create or update a record in the ServiceNow instance by auto-filling the form fields with data that ServiceNow AI Lens extracts from screens and files.

      • Using the Lens browser app – Create or update a record in the ServiceNow instance by auto-filling the form fields with data that ServiceNow AI Lens extracts from screens and files.

      • Using the Lens desktop app – Create or update a record in the ServiceNow instance by auto-filling the form fields with data that ServiceNow AI Lens extracts from screens and files.

    • Auto-fill catalog item form in the Service Portal – Use ServiceNow AI Lens to extract data from documents and auto-fill catalog item forms in Service Portal. For example, auto-fill a new vendor onboarding form by extracting key details such as vendor name, address, contact email, and banking information from multiple documents, that includes Excel files, emails, images, and PDF documents.

      • Using the Lens browser app – Use ServiceNow AI Lens to extract data from documents and auto-fill catalog item forms in Service Portal. For example, auto-fill a new vendor onboarding form by extracting key details such as vendor name, address, contact email, and banking information from multiple documents, that includes Excel files, emails, images, and PDF documents.

      • Using the Lens desktop app – Use ServiceNow AI Lens to extract data from documents and auto-fill catalog item forms in Service Portal. For example, auto-fill a new vendor onboarding form by extracting key details such as vendor name, address, contact email, and banking information from multiple documents, that includes Excel files, emails, images, and PDF documents.

    • Trigger ServiceNow AI Lens from the desktop app – Trigger ServiceNow AI Lens from the desktop app by using a Lens action to preview the extracted data and initiate post processing or auto-fill a form.

    • Extract and analyze data with ServiceNow AI Lens desktop app – Extract and analyze data from one or more screenshots that you capture or files that you upload, and then preview the data analysis.

    • Extract and analyze data with ServiceNow AI Lens in Virtual Agent – Extract and analyze data from an image using ServiceNow AI Lens from a Virtual Agent conversation on a mobile device or a portal. ServiceNow AI Lens can scan an image and then gather insights or provide recommendations as per your instructions.

    • Reference – Reference topics provide additional information about the roles and tables that are installed with the ServiceNow AI Lens application.

    • Components installed – Several types of components are installed with the activation of the ServiceNow AI Lens plugin, including user roles.

    • Field types supported – Learn about the field types that are currently supported with the ServiceNow AI Lens application.

    • Limitations – Be aware of a few limitations when you’re using the ServiceNow AI Lens application.

    • Script include - AILensActionService – Use the AILensActionService script include together with Lens actions to leverage ServiceNow AI Lens as a service for extracting information from the provided images and getting answers to your questions.

  • Conversation Insights – The ServiceNow Conversation Insights application is designed to deliver Inferred customer satisfaction (CSAT) scores, with underlying factors that can help improve CSAT actionability.

    • Exploring Conversation Insights – Learn how Conversation Insights can help you to augment conversation insights with AI-based Inferred customer satisfaction (CSAT) scores and factors.

    • Install Conversation Insights – If you have the admin role, you can install the Conversation Insights application (sn_aci).If the application does NOT include demo data or it does NOT install related applications and plugins, delete or revise the following sentence:The application includes demo data and installs related ServiceNow Store applications and plugins, if they aren’t already installed.

    • Conversation Insights reference – The following topics provide additional information about the features and properties installed with Conversation Insights.

    • Components installed with Conversation Insights – Installation of the Conversation Insights application also installs the Conversation Insights [sn_aci_insights] table.

  • Conversation Improvement Themes – The Conversation Improvement Themes application helps to transform Conversation Evaluators evaluations that are part of AI Control Tower into long-term performance insights by analyzing conversation quality data over time.

    • Explore – The Conversation Improvement Themes application helps to transform conversation evaluations into long-term performance insights.

    • Activate – Activate the Conversation Improvement Themes application to analyze conversation quality.

    • Use – View performance insights using the widgets in Conversation Improvement Themes.

    • Reference – The following topics provide additional information about the features and properties installed with Conversation Improvement Themes.

    • Components installed – Several types of components are part of Conversation Improvement Themes, including scheduled jobs, tables, system properties, and flows.

    • Trobleshooting – Troubleshooting steps for errors that might occur in Conversation Improvement Themes.

  • AI glossary – Learn about the terms and concepts used in ServiceNow Otto and AI agents.

    • AI Admin Hub – Learn about the terms and concepts used in ServiceNow Otto and AI agents.

    • AI Analytics – Learn about the terms and concepts used in ServiceNow Otto and AI agents.

    • AI Guardian – Learn about the terms and concepts used in ServiceNow Otto and AI agents.

    • agents – Learn about the terms and concepts used in ServiceNow Otto and AI agents.

    • agentic AI – Learn about the terms and concepts used in ServiceNow Otto and AI agents.

    • agentic conversation – Learn about the terms and concepts used in ServiceNow Otto and AI agents.

    • agentic system – Learn about the terms and concepts used in ServiceNow Otto and AI agents.

    • agentic workflow – Learn about the terms and concepts used in ServiceNow Otto and AI agents.

    • AI agents – Learn about the terms and concepts used in ServiceNow Otto and AI agents.

    • AI compliance – Learn about the terms and concepts used in ServiceNow Otto and AI agents.

    • AI Control Tower – Learn about the terms and concepts used in ServiceNow Otto and AI agents.

    • AI governance – Learn about the terms and concepts used in ServiceNow Otto and AI agents.

    • AI steward – Learn about the terms and concepts used in ServiceNow Otto and AI agents.

    • citation – Learn about the terms and concepts used in ServiceNow Otto and AI agents.

    • custom skill – Learn about the terms and concepts used in ServiceNow Otto and AI agents.

    • Dynamic Translation – Learn about the terms and concepts used in ServiceNow Otto and AI agents.

    • External Content Connectors – Learn about the terms and concepts used in ServiceNow Otto and AI agents.

    • generative AI – Learn about the terms and concepts used in ServiceNow Otto and AI agents.

    • generative AI skill – Learn about the terms and concepts used in ServiceNow Otto and AI agents.

    • Generative AI Controller – Learn about the terms and concepts used in ServiceNow Otto and AI agents.

    • guardrail – Learn about the terms and concepts used in ServiceNow Otto and AI agents.

    • Knowledge Graph – Learn about the terms and concepts used in ServiceNow Otto and AI agents.

    • large language model (LLM) – Learn about the terms and concepts used in ServiceNow Otto and AI agents.

    • multi-turn catalog ordering – Learn about the terms and concepts used in ServiceNow Otto and AI agents.

    • native translation – Learn about the terms and concepts used in ServiceNow Otto and AI agents.

    • offensive content detection – Learn about the terms and concepts used in ServiceNow Otto and AI agents.

    • orchestrator – Learn about the terms and concepts used in ServiceNow Otto and AI agents.

    • prompt injection detection – Learn about the terms and concepts used in ServiceNow Otto and AI agents.

    • Q&A genius result – Learn about the terms and concepts used in ServiceNow Otto and AI agents.

    • Retrieval Augmented Generation – Learn about the terms and concepts used in ServiceNow Otto and AI agents.

    • ServiceNow Otto – Learn about the terms and concepts used in ServiceNow Otto and AI agents.

    • ServiceNow Otto context menu – Learn about the terms and concepts used in ServiceNow Otto and AI agents.

    • ServiceNow Otto panel – Learn about the terms and concepts used in ServiceNow Otto and AI agents.

  • Additional resources for AI products and solutions – If you’re looking for AI best practices, troubleshooting, or other implementation guidelines, select a feature or resource type to discover ServiceNow resources on other relevant websites.

  • ServiceNow Cowork – ServiceNow Cowork is an enterprise autonomous AI agent that connects to your enterprise applications and systems and executes complex and multi-step tasks autonomously, and operates within defined security, access, and governance policies.

    • Explore Cowork – ServiceNow Cowork is an autonomous AI agent that performs complex tasks, executes multi-step workflows, interacts with enterprise applications, and takes actions based on defined policies.

    • ServiceNow Cowork architecture – ServiceNow Cowork separates the client, the agent host, and an isolated sandbox so that planning happens on the host and agent commands run inside a container with its own network policy. Cowork runs on the desktop and connects to your ServiceNow instance, which governs what Cowork can do.

    • Policy management and governance in ServiceNow Cowork – Policies are governance rules on your ServiceNow instance that control what ServiceNow Cowork can do, which actions need approval, and which actions are blocked.

    • Configure – Activate ServiceNow Cowork plugin to download, install and setup Cowork.

    • Activate the ServiceNow Cowork plugin – You can activate the ServiceNow Cowork plugin (sn_app_cowork) for ServiceNow Cowork if you have the admin role. If the application does NOT include demo data or it does NOT install related applications and plugins, delete or revise the following sentence:The application includes demo data and installs related ServiceNow Store applications and plugins if they aren’t already installed.

    • Download and install ServiceNow Cowork – Install ServiceNow Cowork on a Mac with an M-series processor to use the AI desktop agent on your computer.

    • Set up ServiceNow Cowork – Connect Cowork to your ServiceNow instance and prepare your workspace and secure sandbox.

    • Create a ServiceNow Cowork agent policy – Create a policy to define what ServiceNow Cowork can do, which actions need human approval, and which actions are blocked.

    • Configure a ServiceNow Cowork agent policy – Add tool gates, approval patterns, network rules, sandbox rules, capability overrides, connector scopes, and file type rules to a policy to control what Cowork can do for the users the policy applies to.

    • Trace evaluation for Cowork in AI Control Tower – AI Control Tower scores the traces that Cowork sends for quality and safety. Review the scores to understand usage patterns and identify where responses fall short.

      • Review Cowork traces – Check the quality and safety scores of Cowork sessions in AI Control Tower, then open a session or trace to see which metric scores and details.

    • Use – Use ServiceNow Cowork to get started with tasks, connect systems and extend Cowork capabilities.

    • Start a ServiceNow Cowork chat session – Send your first request to ServiceNow Cowork, from a simple question to a complex multi-step task, and track its progress.

    • Select AI model and workspace folders – Select the AI model the agent uses, confirm its connection, and set the folders it works in. These settings control the quality of the agent’s responses and limits file access to the selected folders.

    • Respond to approval requests in Cowork – Before Cowork takes a sensitive action, such as changing a record or deleting a file, it pauses and asks for your permission. Responding to these requests keeps you in control of what the agent does on your behalf.

    • Assign tasks to ServiceNow Cowork – Use ServiceNow Cowork to complete multi-step work, such as investigating incidents, creating reports, or coordinating updates across platforms. Combine skills, sub-agents, and connectors in a single request so Cowork can complete the whole piece of work from start to finish.

    • Track tasks in Cowork – Check and track the progress of the tasks you have delegated and respond when Cowork needs your input. Tracking tasks lets you keep working while Cowork completes them, and responding quickly keeps paused tasks from stalling.

    • Connectors in ServiceNow Cowork – Connectors link ServiceNow Cowork to the platforms you work in, so Cowork can read and update your real data, not just answer questions.

      • Connect ServiceNow Cowork to Microsoft 365 – Connect your Microsoft account so ServiceNow Cowork can work with your mail, calendar, Microsoft OneDrive files, and Teams chats and channels. After you connect, you can ask Cowork to draft and send email, schedule meetings, find files, and post to Microsoft Teams.

      • Connect ServiceNow Cowork to GitHub – Connect ServiceNow Cowork to GitHub or GitHub Enterprise to enable pull request review, CI checks, read code, and manage issues.

    • Extend ServiceNow Cowork – Extend Cowork with skills, scheduled task, Model Context Protocol (MCP) servers, and sub-agents so the agent can handle more of your work. Each option adds new instructions, tools, or specialized agents, and you control what the agent can access.

      • Create a custom skill in ServiceNow Cowork – Create a skill for Cowork to do a specific task, customizing it to fit your needs. After running the same type of task a few times, turn it into a skill so future runs require just one prompt instead of detailed instructions.

      • Create a sub-agent – Create a custom sub-agent to hand off a specialized task, such as code review or security analysis, to a reusable agent with focused instructions and limited access. A narrow scope helps produce consistent results, limiting the sub-agent to only the skills and MCP servers it requires.

      • View and edit a sub-agent – Review a sub-agent’s system prompt to understand how it behaves, and edit it when its results don’t match what you need or its scope needs to change.

      • Connect ServiceNow Cowork to MCP servers – Connect MCP servers so Cowork can use tools and data from other services, such as GitHub, Sentry, SerpApi, and Postman. Each server you connect adds capabilities to the agent, and you control which servers each sub-agent can use.

    • Review and revoke standing approvals – Review the actions you have allowed Cowork to take without asking, and revoke any you no longer require.

    • Set up TOTP authorization for sensitive actions – Set up TOTP authorization to add another layer of security check before performing any sensitive action, such as changing records, deleting files, or sending messages on your behalf.

    • Set agent limits and safeguards – Set limits on how long the agent runs and when it checks in with you, so it doesn’t use more time or resources than a task needs and you stay in control of long runs.

    • Update ServiceNow Cowork – ServiceNow Cowork checks for updates automatically and verifies each update before it installs. You can also check for updates yourself at any time.

    • Reference – PLACEHOLDER: Short description of the reference information provided for ServiceNow Cowork.

    • Turn off local MCP servers – Turn off local MCP servers for users or user groups so that Cowork can’t run MCP server actions directly on user devices.

    • Prevent changes to sandbox files – Add a filesystem deny rule to your sandbox policy so that agent actions can’t modify files in the sandbox runtime directory on user devices.

    • Environment and capability controls – Environment and capability controls limit what ServiceNow Cowork can access while an action runs, keeping the agent within the boundaries you set.

    • Test a ServiceNow Cowork policy change – Verify that a policy change works as intended before you apply it to all users.

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