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Agentforce and Einstein Generative AI
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            Agent Analytics

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            Agent Analytics

            Analyze your AI agents’ performance within user and agent sessions. Agent Analytics is built on the unified Session Tracing Data Model (STDM), which captures every event and interaction within a session and logs it as individual events. You can query the logged ‌events to gain insights and optimize the Agent.

            Required Editions

            Available in: Enterprise, Performance, and Unlimited Editions with an Einstein for Sales, Einstein for Platform, Einstein for Service, Einstein 1 Service, or Einstein GPT Service add-on. To purchase add-ons, contact your Salesforce account executive.
            Note
            Note Beginning in April 2026, agent topics are now called subagents. There are no changes to functionality. During this transition, you may see a mix of the new and previous terms in our documentation.

            Watch this video to learn how Agent Analytics and Agentforce Optimization work together to help you continuously refine and improve your AI agents.

            Here are the steps and information required to analyze your AI agents’ performance within user and agent sessions.

            • About Agent Analytics
              Analyze the performance of your AI agents. Agent Analytics is a tool specifically designed for comprehensive session analysis. Built on the unified Session Tracing Data Model (STDM), it captures every turn and event occurring in and across all your agent sessions. You can then query the logged events to gain insights and optimize agent performance.
            • Set Up Agent Analytics
              Set up Agent Analytics to gain insights into agent performance, track conversation metrics, and optimize your agents with comprehensive dashboards and session insights.
            • Data Model and Calculated Fields for Agent Analytics
              The data model for Agent Analytics is based on Session Tracing, which is a collection of DLOs (Data Lake Objects) and DMOs (Data Model Objects) that contain detailed session trace logs of agent behavior. The data model also includes references to every LLM call the reasoning engine makes to support joins with feedback data or guardrails metrics. Extended Service Data Model DMOs such as GenAiFeedback, GenAIContentCategory, and TenantEnrichedUsageEvent support feedback, Trust Layer, and flex credit analytics.
            • Get Insights with Agent Analytics Dashboards
              Use the data from your Service Agent Analytics and Employee Agent Analytics dashboards to understand agent performance, and determine whether you want to adjust subagents and actions to improve user satisfaction.
            • Troubleshoot Agent Analytics
              If you’re unable to see the information, models, dashboards, or metrics that you expect, try these solutions.
             
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