{"revision":"c10276e953456835cdbb1f3a60d58ae1980be353","document":{"id":22708,"path":"intelligent-experiences/ai-agent-dashboard.md","title":"AI Agent Analytics dashboard","bundle":null,"doc_type":null,"release":"australia","body":"\n# AI Agent Analytics dashboard\n\nTrack 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.\n\n\n\n## Required ServiceNow AI Platform roles\n\nTo use the AI Agent Analytics dashboard, you must have either the sn\\_aia.viewer or the sn\\_aia.admin role.\n\nIf you want to change the dashboard, you must duplicate it and apply changes to the copy. You can redirect the Analytics page in AI Agent Studio to the new dashboard by replacing the dashboard sys\\_id in the system property **sn\\_aia.analytics\\_dashboard\\_sysid**.\n\n## Accessing the AI Agent Analytics dashboard\n\nTo open the dashboard, navigate to **All** > **AI Agent Studio** > **Analytics**.\n\nYou can also access the dashboard from the AI Agent Studio overview page. Go to the Recent agentic workflows and AI agents activity section and select the **View analytics** link.\n\n## Indicators\n\nMost indicators are updated daily. The latency indicators are updated every 15 minutes.\n\nOnce you have installed AI agents, you can collect initial data by running the \\[Now Assist AI Agents\\] Historical Data Collection job. The other data collection jobs, \\[Now Assist AI Agents\\] Daily Data Collection and \\[Now Assist AI Agents\\] Periodic Data Collection, update the indicators.\n\n\n
\n\nName\n\n\n\nDescription\n\n
\n\nAgentic workflow latency\n\n\n\nTime taken for an agentic workflow to complete.\n\n
\n\nAI agent execution plan P90 latency\n\n\n\nTime taken for 90% of AI agent execution plans in a system to be processed.\n\n
\n\nAI agent execution plan P95 latency\n\n\n\nTime taken for 95% of AI agent executions plans in a system to be processed.\n\n
\n\nAI agent execution plan P99 latency\n\n\n\nTime taken for 99% of AI agent execution plans in a system to be processed.\n\n
\n\nAI agent latency\n\n\n\nTime taken for an AI agent in a system to be processed.\n\n
\n\nAll agentic workflows\n\n\n\nTotal number of agentic workflows created before today.\n\n
\n\nAll AI agents\n\n\n\nTotal number of AI agents created before today.\n\n
\n\nAll tools\n\n\n\nTotal number of tools created before today.\n\n
\n\nLLM P90 latency\n\n\n\nTime taken for 90% of LLM requests in a system to be processed.\n\n
\n\nLLM P95 latency\n\n\n\nTime taken for 95% of LLM requests in a system to be processed.\n\n
\n\nLLM P99 latency\n\n\n\nTime taken for 99% of LLM requests in a system to be processed.\n\n
\n\nNumber of closed tasks\n\n\n\nTotal number of tasks closed.\n\n
\n\nNumber of closed tasks with AI agent assist\n\n\n\nTotal number of tasks closed today that were closed with agentic workflows or AI agents.\n\n
\n\nSummed duration of closed tasks\n\n\n\nTotal time taken to close tasks.\n\n
\n\nSummed duration of closed tasks with AI agent assist\n\n\n\nTotal time taken to close tasks today that were closed with agentic workflows or AI agents.\n\n
\n\nTool execution P90 latency\n\n\n\nTime taken for 90% of tool executions in a system to be processed.\n\n
\n\nTool execution P95 latency\n\n\n\nTime taken for 95% of tool executions in a system to be processed.\n\n
\n\nTool execution P99 latency\n\n\n\nTime taken for 99% of tool executions in a system to be processed.\n\n
\n\nTool latency\n\n\n\nTime taken for a tool in a system to be processed.\n\n
\n\nTotal number of CS conversations\n\n\n\nTotal number of conversations in ServiceNow Otto panel or Virtual Agent.\n\n
\n\nTotal number of execution plans\n\n\n\nTotal number of execution plans created by agentic workflows and AI agents today.\n\n
\n\nTotal number of execution tasks\n\n\n\nTotal number of tasks executed by agentic workflows and AI agents today.\n\n
\n\nTotal number of tool executions\n\n\n\nTotal number of tool executions in agentic workflows and AI agents today.\n\n
\n\nUse case execution plan P90 latency\n\n\n\nTime taken for 90% of agentic workflows in a system to be processed.\n\n
\n\nUse case execution plan P95 latency\n\n\n\nTime taken for 95% of agentic workflows in a system to be processed.\n\n
\n\nUse case execution plan P99 latency\n\n\n\nTime taken for 99% of agentic workflows in a system to be processed.\n\n
\n
\n\nName\n\n\n\nDescription\n\n
\n\n% of conversations with an AI agent or agentic workflow defined\n\n\n\nNumber of conversations in ServiceNow Otto panel or Virtual Agent with a defined AI agentic or agentic workflow divided by the total number of conversations.\n\n
\n\nAverage time to close a task\n\n\n\nAverage time taken for a task to go from creation to closure.\n\n
\n\nAverage time to close a task with AI agent assist\n\n\n\nAverage time taken for a task to go from creation to closure with an agentic workflow or AI agent involved.\n\n
\n\nEfficiency gain\n\n\n\nPercentage efficiency gain comparing average time taken to close a task with AI agent assist against a task closed without AI agent assist.\n\n
\n\nPercentage of tasks closed using AI agents\n\n\n\nNumber of tasks closed using AI agents and agentic workflows divided by the total number of tasks.\n\n
## Breakdowns\n\nDifferent breakdowns enable you to divide the data differently to track specific aspects of the AI agent usage. Not all breakdowns are available for all indicators.\n\n- Agentic workflow\n- AI agent\n- AI agent execution status\n- Latency metric\n- Tool\n- Tool latency metric\n\n## Filters\n\nYou can filter data to review a subset of trends. The following table lists the available filters on the AI Agent Analytics dashboard.\n\n\n
\n\nPage\n\n\n\nAvailable filters\n\n
\n\nStatus\n\n\n\nTime created\n\n
\n\nInsights\n\n\n\n- Time created\n- Agentic workflow\n- AI agent\n- AI Agent Type\n\n
\n\nAssist consumption\n\n\n\n- Time created\n- Agentic workflow\n- AI agent\n- AI Agent Type\n\n
\n\nPerformance\n\n\n\n- Time created\n- Agentic workflow\n- AI agent\n- AI Agent Type\n- Tool\n\n
\n\nTroubleshooting\n\n\n\nTime created\n\n
## Data visualizations\n\nVisualizations are graphic elements that can be used to see data trends, percentages, and scores. The AI Agent Analytics dashboard includes the following visualizations.\n\n\n
\n\nTitle\n\n\n\nType\n\n\n\nDescription\n\n
\n\nAverage inferred CSAT for AI agents in last 7 days\n\n\n\nScore card\n\n\n\nAI-generated score estimating what a customer would rate the AI response. 1 being the user is extremely dissatisfied and 5 being the user is extremely satisfied with the interaction.\n\n
\n\nExecution tasks in last 7 days\n\n\n\nPie chart\n\n\n\nExecution tasks divided by status \\(Success, Ongoing, Cancelled, Queued\\).\n\n
\n\nAI Agent errors in last 90 days\n\n\n\nLine graph\n\n\n\nNumber of errors seen by AI agents within the last 90 days broken down by week.\n\n
\n\nAI agent tool type\n\n\n\nPie chart\n\n\n\nTools divided by type. Example: Script, Flow action, Subflow, and so on.\n\n
\n\nAI agent tool execution mode\n\n\n\nPie chart\n\n\n\nTools divided by either autonomous or supervised execution mode.\n\n
\n\nExecution plans in last 7 days\n\n\n\nPie chart\n\n\n\nExecution plans divided by status \\(Completed, Terminated, Wrap Up, or In Progress\\)\n\n
\n\nAI agent executions\n\n\n\nList\n\n\n\nList of AI agent executions. The list is broken down by AI agent and AI agent execution status. Columns span for multiple weeks, and there are change percentages and trend lines comparing the current against the previous week.\n\n
\n\nNumber of agentic workflows\n\n\n\nTrend line\n\n\n\nTotal number of agentic workflows. The agentic workflows created in the last day and a trend line are shown.\n\n
\n\nNumber of AI agents\n\n\n\nTrend line\n\n\n\nTotal number of AI agents. The AI agents created in the last day and a trend line are shown.\n\n
\n\nNumber of tools\n\n\n\nTrend line\n\n\n\nTotal number of tools. The tools created in the last day and a trend line are shown.\n\n
\n\nAI agent execution plans\n\n\n\nList\n\n\n\nList of AI agent execution plans. The list is broken down by agentic workflow. Columns span for multiple weeks, and there are change percentages and trend lines comparing the current against the previous week.\n\n
\n
\n\nTitle\n\n\n\nType\n\n\n\nDescription\n\n
\n\nAI agent execution plans\n\n\n\nScore cards\n\n\n\nFour score cards, one for each execution plan status \\(Ready, In progress right now, Completed, and Terminated\\).\n\n
\n\nAI agent executions\n\n\n\nScore cards\n\n\n\nFour score cards, one for each execution plan status \\(In progress right now, Completed, Successful, and Errors/Cancelled\\).\n\n
\n\nAI agent/tool executions\n\n\n\nList\n\n\n\nList of AI agent and tool executions. The list is broken down by AI agent and tool, and there are columns for each status \\(Cancelled, Ongoing, Queued, and Success\\).\n\n
\n
\n\nTitle\n\n\n\nType\n\n\n\nDescription\n\n
\n\nTasks closed using AI agents\n\n\n\nTrend line\n\n\n\nPercentage of tasks closed using AI agents. Trend line is shown.\n\n
\n\nTask closure efficiency gain using AI agents\n\n\n\nTrend line\n\n\n\nPercentage efficiency gained with agentic workflows and AI agents. Trend line is shown.\n\n
\n\nAverage close time assisted by AI agents\n\n\n\nTrend line\n\n\n\nAverage time taken for a task to go from creation to closure with the involvement of an AI agent or agentic workflow. Trend line is shown.\n\n
\n\nInferred CSAT of AI agents\n\n\n\nScore card\n\n\n\nEstimated CSAT score calculated using AI based on the subjective quality of the AI agents' responses. 1 being the user is extremely dissatisfied and 5 being the user is extremely satisfied with the interaction.\n\n
\n\nInferred Session CSAT\n\n\n\nScore card\n\n\n\nEstimated CSAT score calculated using AI based on the overall session interactions of a user. 1 being the user is extremely dissatisfied and 5 being the user is extremely satisfied with the interaction.\n\n
\n\nTop user intents when using agentic solutions\n\n\n\nBar chart\n\n\n\nMost common user intents as interpreted by the LLM. Intents are grouped by [Group Action Framework](https://raw.githubusercontent.com/ServiceNow/ServiceNowDocs/australia/markdown/australia/intelligent-experiences/group-action-framework.md).\n\n
\n\nAverage AI agent CSAT by intent\n\n\n\nBar chart\n\n\n\nAverage CSAT score for each user intent interpreted by the LLM. Intents are grouped by [Group Action Framework](https://raw.githubusercontent.com/ServiceNow/ServiceNowDocs/australia/markdown/australia/intelligent-experiences/group-action-framework.md).\n\n
\n\nTransfers and escalations using AI agents\n\n\n\nBar chart\n\n\n\nAn estimated score using AI that indicates whether the user requested an escalation or there was a transfer to another AI agent or human.\n\n
\n\nUser effort required with AI agents\n\n\n\nBar chart\n\n\n\nCounts of AI-generated scores that measure the time and energy users had to put in during their interaction with an AI agent. Values are on a 3-point scale of low, medium, and high.\n\n
\n\nAI agent recommended next steps\n\n\n\nBar chart\n\n\n\nCounts of AI-generated scores that indicate whether the AI agent recapped the outcome and provided clear instructions or guidance on what the customer should do next, if applicable. Values are on a 3-point scale of low, medium, and high.\n\n
\n\nAI agent resolved the users request\n\n\n\nScore card\n\n\n\nAn AI-generated score that indicates whether the AI agent resolved the user's request. Values can be yes, no or unknown.\n\n
\n\nUser frustration with AI agents\n\n\n\nBar chart\n\n\n\nAn AI-generated score that indicates whether the user expressed frustration at any point in the conversation.\n\n
\n\nAI agent empathy\n\n\n\nScore card\n\n\n\nAn AI-generated score that indicates whether the AI agent was empathetic and friendly. Values are on a 3-point scale of low, medium, and high.\n\n
\n\nUser or AI agent confusion\n\n\n\nBar chart\n\n\n\nCounts of an AI-generated score that indicates whether the user or AI agent is confused at any point of the conversation.\n\n
\n\nTop 10 agentic workflows with highest session CSAT\n\n\n\nBar chart\n\n\n\nTop 10 agentic workflows by highest CSAT score.\n\n
\n\nBottom 10 agentic workflows with highest session CSAT\n\n\n\nBar chart\n\n\n\nBottom 10 agentic workflows by CSAT score.\n\n
\n\nTop 10 AI agents with highest session CSAT\n\n\n\nBar chart\n\n\n\nTop 10 AI agents by highest CSAT score.\n\n
\n\nBottom 10 AI agents with highest session CSAT\n\n\n\nBar chart\n\n\n\nBottom 10 AI agents by CSAT score.\n\n
\n\nAI agent conversations daily\n\n\n\nTrend line\n\n\n\nTotal number of AI agent conversations in ServiceNow Otto panel or Virtual Agent. Trend line is shown.\n\n
\n\n% of conversations using AI agents\n\n\n\nTrend line\n\n\n\nPercentage of AI agent conversations in ServiceNow Otto panel or Virtual Agent that used agentic workflows or AI agents. Trend line is shown.\n\n
\n\nEfficiency gain supporting indicators\n\n\n\nList\n\n\n\nList of indicators. The list is broken down by indicator. Columns span across a time span.\n\n
\n
\n\nTitle\n\n\n\nType\n\n\n\nDescription\n\n
\n\nSum of AI agent assists\n\n\n\nScore card\n\n\n\nTotal number of assists used on the instance for both AI agents and agentic workflows.\n\n
\n\nSum of AI agent assists per tier\n\n\n\nPie chart\n\n\n\nTotals and percentages of assists divided by tier. An assist is marked as small until the number of tool executions exceeds 20.\n\n
\n\nCount of AI agent executions per assist tier.\n\n\n\nPie chart\n\n\n\nCount of assist tiers for both AI agents and agentic workflows. An assist is marked as small until the number of tool executions exceeds 20.\n\n
\n\nTop 10 agentic workflows consuming assists\n\n\n\nPie chart\n\n\n\nPercentages of assists consumed among the top 10 agentic workflows consuming assists.\n\n
\n\nSum of AI agents assists\n\n\n\nBar chart\n\n\n\nTotal number of assists used by AI agents broken down by day.\n\n
\n\nCount of AI agent executions per assist tier\n\n\n\nBar chart\n\n\n\nCount of assists used by AI agents broken down by assist tier and day.\n\n
\n\nCount of AI agent executions per agentic workflow\n\n\n\nBar chart\n\n\n\nCount of assists used by AI agents broken down by agentic workflow.\n\n
\n\nSum of AI agent assists per AI agent\n\n\n\nBar chart\n\n\n\nNumber of assists used by AI agents broken down by AI agent.\n\n
\n\nAverage tool execution count per agentic workflow\n\n\n\nList\n\n\n\nCount of tools executed by an agentic workflow broken down by agentic workflow and day.\n\n
\n\nAverage tool execution count per assist tier\n\n\n\nList\n\n\n\nCount of tools executed by an agentic workflow broken down by assist tier and day.\n\n
\n
\n\nTitle\n\n\n\nType\n\n\n\nDescription\n\n
\n\nP90 agentic workflow latency\n\n\n\nScore card\n\n\n\nTime in seconds of 90% of agentic workflows in a system to be processed.\n\n
\n\nP95 agentic workflow latency\n\n\n\nScore card\n\n\n\nTime in seconds of 95% of agentic workflows in a system to be processed.\n\n
\n\nP99 agentic workflow latency\n\n\n\nScore card\n\n\n\nTime in seconds of 99% of agentic workflows in a system to be processed.\n\n
\n\nTotal agentic workflows executed per hour\n\n\n\nBar chart\n\n\n\nVisualization of what time of day agentic workflows have been executed by hour.\n\n
\n\nTotal agentic workflows errors per hour\n\n\n\nBar chart\n\n\n\nVisualization of what time of day agentic workflows have been executed and experienced an error by hour.\n\n
\n\nAgentic workflow latency\n\n\n\nLine chart\n\n\n\nAgentic workflow latency over time. Uses 3 different indicators: P90 tracks the fastest 90% of executions, P95 tracks the fastest 95% of executions, and P99 tracks the fastest 99% of executions.\n\n
\n\nAgentic workflow latency \\(weekly average\\)\n\n\n\nLine chart\n\n\n\nAgentic workflow latency over time. Uses 3 different indicators: P90 tracks the fastest 90% of executions, P95 tracks the fastest 95% of executions, and P99 tracks the fastest 99% of executions.\n\n
\n\nAgentic workflow latency \\(daily average\\)\n\n\n\nTable\n\n\n\nAverage agentic workflow latency today. Each row is an agentic workflow and each column is a different latency metric. Uses 3 different indicators: P90 tracks the fastest 90% of executions, P95 tracks the fastest 95% of executions, and P99 tracks the fastest 99% of executions.\n\n
\n\nP90 AI agent latency\n\n\n\nScore card\n\n\n\nTime in seconds of 90% of AI agent executions in a system to be processed.\n\n
\n\nP95 AI agent latency\n\n\n\nScore card\n\n\n\nTime in seconds of 95% of AI agent executions in a system to be processed.\n\n
\n\nP99 AI agent latency\n\n\n\nScore card\n\n\n\nTime in seconds of 99% of AI agent executions in a system to be processed.\n\n
\n\nTotal AI agents executed per hour\n\n\n\nBar chart\n\n\n\nVisualization of what time of day AI agent executions have been executed by hour.\n\n
\n\nTotal AI agent errors per hour\n\n\n\nBar chart\n\n\n\nVisualization of what time of day AI agent executions have been executed and experienced an error by hour.\n\n
\n\nAI agent latency\n\n\n\nLine chart\n\n\n\nAI agent latency within the time filter. Uses 3 different indicators: P90 tracks the fastest 90% of executions, P95 tracks the fastest 95% of executions, and P99 tracks the fastest 99% of executions.\n\n
\n\nAI agent latency \\(weekly average\\)\n\n\n\nLine chart\n\n\n\nAI agent latency over time by week. Uses 3 different indicators: P90 tracks the fastest 90% of executions, P95 tracks the fastest 95% of executions, and P99 tracks the fastest 99% of executions.\n\n
\n\nAI agent latency \\(daily average\\)\n\n\n\nTable\n\n\n\nAverage AI agent latency today. Each row is an AI agent and each column is a different latency metric. Uses 3 different indicators: P90 tracks the fastest 90% of executions, P95 tracks the fastest 95% of executions, and P99 tracks the fastest 99% of executions.\n\n
\n\nP90 tool latency\n\n\n\nScore card\n\n\n\nTime in seconds of 90% of tool executions in a system to be processed.\n\n
\n\nP95 tool latency\n\n\n\nScore card\n\n\n\nTime in seconds of 95% of tool executions in a system to be processed.\n\n
\n\nP99 tool latency\n\n\n\nScore card\n\n\n\nTime in seconds of 99% of tool executions in a system to be processed.\n\n
\n\nTotal tools executed per hour\n\n\n\nBar chart\n\n\n\nVisualization of what time of day tool executions have been executed by hour.\n\n
\n\nTotal tool errors per hour\n\n\n\nBar chart\n\n\n\nVisualization of what time of day tool executions have been executed and experienced an error by hour.\n\n
\n\nTool latency\n\n\n\nLine chart\n\n\n\nTool latency within the time filter. Uses 3 different indicators: P90 tracks the fastest 90% of executions, P95 tracks the fastest 95% of executions, and P99 tracks the fastest 99% of executions.\n\n
\n\nTool latency \\(weekly average\\)\n\n\n\nLine chart\n\n\n\nTool latency over time by week. Uses 3 different indicators: P90 tracks the fastest 90% of executions, P95 tracks the fastest 95% of executions, and P99 tracks the fastest 99% of executions.\n\n
\n\nTool latency \\(daily average\\)\n\n\n\nTable\n\n\n\nAverage tool latency today. Each row is a tool and each column is a different latency metric. Uses 3 different indicators: P90 tracks the fastest 90% of executions, P95 tracks the fastest 95% of executions, and P99 tracks the fastest 99% of executions.\n\n
\n\nP90 LLM latency\n\n\n\nScore card\n\n\n\nTime in seconds of 90% of LLM requests in a system to be processed.\n\n
\n\nP95 LLM latency\n\n\n\nScore card\n\n\n\nTime in seconds of 95% of LLM requests in a system to be processed.\n\n
\n\nP99 LLM latency\n\n\n\nScore card\n\n\n\nTime in seconds of 99% of LLM requests in a system to be processed.\n\n
\n\nLLM latency\n\n\n\nLine chart\n\n\n\nTool latency within the time filter. Uses 3 different indicators: P90 tracks the fastest 90% of executions, P95 tracks the fastest 95% of executions, and P99 tracks the fastest 99% of executions.\n\n
\n\nLLM latency \\(weekly average\\)\n\n\n\nLine chart\n\n\n\nTool latency over time by week. Uses 3 different indicators: P90 tracks the fastest 90% of executions, P95 tracks the fastest 95% of executions, and P99 tracks the fastest 99% of executions.\n\n
\n\nLLM calls per hour\n\n\n\nBar chart\n\n\n\nVisualization of what time of day LLM calls have been made by hour.\n\n
\n\nLLM errors per hour\n\n\n\nBar chart\n\n\n\nVisualization of what time of day LLM calls have been made and experienced an error by hour.\n\n
\n
\n\nName\n\n\n\nType\n\n\n\nDescription\n\n
\n\nAgentic workflow errors\n\n\n\nList\n\n\n\nList of agentic workflow errors.\n\n
For more information on security controls for agentic AI, see [Security for AI agents](https://raw.githubusercontent.com/ServiceNow/ServiceNowDocs/australia/markdown/australia/intelligent-experiences/aia-security-implementation.md).\n\n\n
\n\nName\n\n\n\nType\n\n\n\nDescription\n\n
\n\nTotal blocked executions\n\n\n\nScore card\n\n\n\nTotal number of agentic AI executions blocked because of security controls.\n\n
\n\nTotal blocked executions\n\n\n\nList\n\n\n\nList of executions blocked.\n\n
\n\nTop agentic workflows with blocked executions\n\n\n\nList\n\n\n\nList of agentic workflows with the most blocked executions.\n\n
\n\nTop AI agents with blocked executions\n\n\n\nList\n\n\n\nList of AI agents with the most blocked executions.\n\n
\n\nAgentic Workflows w/o ACL defined\n\n\n\nScore card\n\n\n\nTotal number of agentic workflows missing an ACL for user access.\n\n
\n\nAgentic Workflows ACLs\n\n\n\nPie chart\n\n\n\nPie chart displaying the percentage of agentic workflows with and without ACLs for user access.\n\n
\n\nAgentic Workflows w/o ACL defined\n\n\n\nList\n\n\n\nList of agentic workflows missing an ACL for user access.\n\n
\n\nAI agents with no ACLs defined\n\n\n\nScore card\n\n\n\nTotal number of AI agents missing an ACL for user access.\n\n
\n\nAI agent ACLs\n\n\n\nPie chart\n\n\n\nPie chart displaying the percentage of AI agents with and without ACLs for user access.\n\n
\n\nAI agents w/o ACL defined\n\n\n\nList\n\n\n\nList of AI agents missing an ACL for user access.\n\n
\n\nAgentic workflows without role masking\n\n\n\nScore card\n\n\n\nTotal number of agentic workflows without roles identified for role masking and data access.\n\n
\n\nAgentic workflows without role masking\n\n\n\nList\n\n\n\nList of agentic workflows without roles identified for role masking and data access.\n\n
\n\nAI agents without role masking\n\n\n\nScore card\n\n\n\nTotal number of AI agents without roles identified for role masking and data access.\n\n
\n\nAI agents without role masking\n\n\n\nList\n\n\n\nList of AI agents without roles identified for role masking and data access.\n\n
\n\nAgentic workflows running as dynamic user\n\n\n\nScore card\n\n\n\nTotal number of agentic workflows running as a dynamic user.\n\n
\n\nAgentic workflows running as dynamic user\n\n\n\nList\n\n\n\nList of agentic workflows running as a dynamic user.\n\n
\n\nAI agents running as dynamic user\n\n\n\nScore card\n\n\n\nTotal number of AI agents running as a dynamic user.\n\n
\n\nAI agents running as dynamic user\n\n\n\nList\n\n\n\nList of AI agents running as a dynamic user.\n\n
\n\nAgentic workflows running as AI user\n\n\n\nScore card\n\n\n\nTotal number of agentic workflows running as an AI user.\n\n
\n\nAgentic workflows running as AI user\n\n\n\nList\n\n\n\nList of agentic workflows running as an AI user.\n\n
\n\nAI agents running as AI user\n\n\n\nScore card\n\n\n\nTotal number of AI agents running as an AI user.\n\n
\n\nAI agents running as AI user\n\n\n\nList\n\n\n\nList of AI agents running as an AI user.\n\n
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