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9.2 Agent Analytics and Agent Optimization

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Salesforce Certified Agentforce Specialist Exam

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1 Chapter 1: Exam Overview & How Agentforce Agents Work

1.1 Agentforce Specialist (AI-201) Exam Facts & Spring '26 Blueprint 1.2 How an Agent Works & the Building Blocks of Agent Script 1.3 Hybrid Reasoning: Agent Script in Canvas View and Script View

2 Chapter 2: Deterministic Control, Topics (Subagents) & Actions

2.1 Deterministic Behavior: Filters, Variables & Template Expressions 2.2 Standard and Custom Topics (Subagents) 2.3 Standard and Custom Agent Actions

3 Chapter 3: Agent Types, Security Context, Channels & Agent API

3.1 When to Use an Employee Agent or a Service Agent 3.2 Security Context: Who the Agent Runs As and How It Affects Actions 3.3 Connecting Agents to Channels: Digital Experiences, Email, Voice & Slack 3.4 When to Use the Agent API

4 Chapter 4: Prompt Builder Fundamentals

4.1 When to Use Prompt Builder 4.2 Access Controls Governing Prompt Templates 4.3 Prompt Template Types: Field Generation, Flex & More 4.4 Creating, Activating & Executing Prompt Templates

5 Chapter 5: Grounding, Prompt Best Practices, Trust Layer & Model Access

5.1 Choosing the Right Grounding Technique 5.2 Best Practices for Writing Effective Prompts 5.3 Einstein Trust Layer Security and Privacy Features 5.4 Managing and Preventing Access to Specific Models

6 Chapter 6: Data 360 Fundamentals for Agentforce

6.1 Agentforce Data Library: Types and Concepts 6.2 Chunking: Breaking Content Into Retrievable Units 6.3 Indexing and Search Types: Vector, Hybrid & Keyword 6.4 Retrievers: Individual, Ensemble & Filtered Retrieval

7 Chapter 7: Testing Agents with Testing Center

7.1 Testing an Agent with Testing Center 7.2 How Testing Center Evaluations Work

8 Chapter 8: Deploying Agents and Prompt Templates

8.1 Deploying an Agent from Sandbox to Production 8.2 Deploying Prompt Templates from Sandbox to Production

9 Chapter 9: Governance and Observability

9.1 Managing and Monitoring Agents 9.2 Agent Analytics and Agent Optimization

10 Chapter 10: Multi-Agent Orchestration, MCP & A2A

10.1 When a Multi-Agent Architecture Is Appropriate 10.2 Open Multi-Agent Protocols: MCP and A2A

9.2 Agent Analytics and Agent Optimization

Key Takeaways Explain This Topic AI

Agent Analytics is built on the Session Tracing Data Model and shows Tableau Next dashboards for Service Agent Analytics and Employee Agent Analytics.

Effectiveness metrics include deflection rate, escalation rate, abandonment rate, engagement rate, and success rate, and a session counts as engaged only when at least one action runs.

Agent Optimization's Sessions & Intents page shows processed sessions with intent summaries, intent tags, quality scores, and session outcomes.

Intent clusters form only when at least 10 semantically similar intents exist, and clustering runs about once a week.

Session Outcome is Escalated, Abandoned, Deflected, Ambiguous, or NOT_SET, based on escalation status plus LLM-evaluated deflection and abandonment scores.

Last updated: September 2026

9.2 Agent Analytics and Agent Optimization

Quick Answer: Agent Analytics answers " what's trending? " It aggregates all sessions into dashboards for effectiveness, usage, quality, health, trust, and voice , with Performance Insights breakdowns by subagent, intent, or action. Agent Optimization answers " why? " Its Sessions & Intents page shows individual sessions, intents , intent tags , quality scores , session outcomes , and traces. Use analytics to find the problem, optimization to diagnose it, the builder to fix it, and Testing Center to verify the fix.

Agent Analytics

Agent Analytics is built on the unified Session Tracing Data Model (STDM) , which captures every turn and event across sessions. It replaces legacy Agentforce Analytics , which used older Data 360 objects based on copilot events. Salesforce advises existing users to upgrade.

Navigating

In Agentforce Studio → Analytics (Observe & Optimize), choose the installed Service Agent Analytics or Employee Agent Analytics app, select an agent or All , and set filters such as timeframe, channel, and modality.

  • Tab / dimension Metrics Data source
  • Overview Summary cards and trends across areas STDM + Audit & Feedback + RAG Quality
  • Effectiveness Deflection, escalation, abandonment, engagement, success, and task resolution rates STDM
  • Usage Unique sessions, interactions, users, and average interactions per session STDM

Quality Quality score (1–5), answer faithfulness, answer relevance, context relevance Audit & Feedback + RAG Quality

  • Health Interaction latency, session duration, error rate STDM
  • Trust Instruction adherence response rate, average toxicity score Audit & Feedback + Trust Layer
  • Voice Interruption rate (voice agents) STDM
  • User Satisfaction Thumbs up/down counts and trends ( Employee agents ) Audit & Feedback
  • Performance Insights Break down any metric by subagent, intent, or action STDM + Audit & Feedback
  • Effectiveness metric definitions
  • Metric Definition
  • Deflection rate Share of sessions that ended without escalation
  • Escalation rate Share of sessions escalated to a human or another agent

Abandonment rate Share of sessions abandoned, such as a timeout or drop-off. The default session timeout is two hours

Engagement rate Share of sessions with a substantive reply tied to agent-triggered work. A session is engaged only when at least one action is triggered

Success rate Share of interactions that completed agent action steps without errors

Performance Insights is the step between seeing a trend and reviewing sessions. Break the metric down by subagent, intent, or action first, so you know where to look instead of reviewing sessions at random.

Sandbox note: Agent Analytics shows only data stored in the current org. Sandbox agent usage records are written to the production org's usage event data, tagged by sandbox org ID and environment type, and Digital Wallet in production is the source of truth for consumption.

Agent Optimization

Agent Optimization is the session-level component of Agentforce Observability. It helps you dig into unresolved interactions, find knowledge gaps, and analyze sessions .

  • Feature What it provides
  • Intents Groups of interactions in a session that address one user request

Quality scores LLM-calculated relevance of the agent's response to the request (High, Medium, or Low in the interaction summary, with reasoning)

Session analysis Intent-level view of what users ask and how agents respond

Trend identification Low-performing subagents by quality score, misinterpretations, poorly handled conversations

Access: assign the Access Agentforce Optimization (Agent Optimization) and Data Cloud User permission sets. Setup requires Agentforce Session Tracing and Data Model and Agent Optimization turned on in Einstein Audit, Analytics, and Monitoring Setup.

The Sessions & Intents page

Processed sessions have passed through the pipeline: intents extracted, clustered, and scored. They're typically available 4–5 hours after the session closes .

Unprocessed sessions are active or recent. The Trace tab is available immediately, so you can debug without waiting.

Columns include session ID, duration, outcome , custom scorer labels, intent summary , response summary , subagents and actions triggered, intent tag , and quality score .

Filter by modality (voice, chat, or all), review voice recordings , export transcripts, and view average agent response latency (from the end of the user's message to the first token of the response).

Email sessions created in real time aren't reflected in the Sessions & Intents table.

Intent pipeline and clustering

  • Parameter Value
  • Session considered closed Explicit close, or after 3 hours of inactivity
  • Intent scheduler Runs every 3 hours
  • Intent pipeline duration Typically 4–5 hours , up to 24
  • Clustering cadence About once a week , over roughly the last month of intents
  • Minimum cluster size 10 semantically similar intents for a cluster and tag
  • Untagged intents Don't appear in the session trace; may be reconsidered in later weekly runs
  • Session outcome logic

At session close, Agentforce evaluates a Deflection Score (1–5: were the user's goals achieved?) and an Abandonment Score (TRUE, FALSE, or UNSURE: did the user disengage because of dissatisfaction or an unresolved issue?).

  • Outcome Criteria
  • Escalated Transferred to a human agent
  • Abandoned Not escalated, and Abandonment Score is TRUE or Deflection Score is below 3
  • Deflected Not escalated, not abandoned, and Deflection Score is 4 or higher
  • Ambiguous Not escalated, not abandoned, and Deflection Score is exactly 3
  • NOT_SET No session end signal received

Session Outcome was introduced in Spring '26 and uses LLM-based scoring starting in Summer '26. Both scores include reasoning you can review, and you can customize the evaluation with scorers.

Reviewing and feedback

From a session, select an intent to see its tag, quality score, feedback, triggered subagents and actions, and evaluations. Reviewers can submit feedback using intent-level or session-level feedback templates (labels, true/false, free text, number, date, or URL).

Agent Optimizer (beta)

Agent Optimizer lets you ask natural-language questions in Agentforce Studio Analytics ("Sample sessions from last week and identify failure patterns"). It samples sessions (about 100 by default, up to 1,000), identifies patterns and possible causes, and suggests improvements. Diagnosis is sample-based , and sessions less than about 30 minutes old are excluded.

Loading diagram... Optimization loop

Worked Example

Symptom: Service Agent Analytics shows the escalation rate rising from 12% to 21% over two weeks.

Performance Insights shows most escalations in the Returns subagent.

Sessions & Intents , filtered to the Returns intent tag and Escalated outcome, shows low quality scores . The reasoning says customers ask about returning opened items.

The Trace shows the agent calling Answer Questions with Knowledge. The retriever returns chunks about unopened items only.

RAG metrics show high faithfulness but low context relevance, which points to retrieval (section 6.4). The opened-item policy article was never published.

Fix: publish the article, then confirm the Knowledge data library updates.

Retest with Testing Center cases generated from knowledge sources, then monitor the escalation rate.

Exam Traps

Analytics = aggregated trends; Optimization = individual sessions and intents.

Engaged sessions require at least one triggered action.

Clusters need at least 10 similar intents. Low volume means no tags yet.

Deflection score of exactly 3 = Ambiguous; 4 or higher (and not abandoned) = Deflected.

Real-time email sessions don't appear in Sessions & Intents.