Governance and Observability10% of the exam

Agent Analytics metrics and optimization

What each Agent Analytics metric measures (deflection, escalation, abandonment, engagement, success, task resolution), how LLM-judged quality scores point to retrieval or response problems, how intents are extracted and clustered, and the loop that turns a weak subagent or intent cluster into a measured fix.

Official exam objective
Explain agent analytics and agent optimization.

~6 min read · 16 practice questions ·

What the exam expects

  • Define the effectiveness metrics (deflection, escalation, abandonment, engagement, success, task resolution) and read them together.
  • Interpret the quality score and its dimensions (answer faithfulness, answer relevance, context relevance) and know they are judged by an LLM.
  • Explain intents, the intent and clustering pipelines and their timing.
  • Use Performance Insights and quality score reasoning to find the subagent or intent behind poor results.
  • Choose the optimization action (knowledge gaps, overlapping articles, unclear instructions) and re-measure.

Key terms: deflection rate is the share of sessions that ended without escalation; task resolution rate is the share judged fully resolved by the Trust Layer Task Resolution detector; answer faithfulness measures how closely a response sticks to grounded source data; context relevance measures whether the right source was retrieved; intent is what the user was trying to accomplish within a set of turns.

Key facts

Abandonment timeout
Detail
Default 2 hours
Quality Score scale
Detail
1–5, from an LLM evaluator
Dimension scales
Detail
Faithfulness, answer relevance and context relevance are 0–1
Score prerequisites
Detail
Audit and Feedback plus Knowledge/RAG Quality Data and Metrics
Intent pipeline
Detail
About 4–5 hours after a session closes
Clustering pipeline
Detail
Weekly; tag needs 10+ similar intents
Segment comparison
Detail
Performance Insights by subagent, intent or action
Data foundation
Detail
Built on the Session Tracing Data Model
Sources for this section (3)
  • Why can an agent show high deflection and still leave customers unresolved?
  • When will a new cluster tag appear for a brand-new kind of request?
  • Which field explains why a session got a low quality rating?
  • Which three dimensions make up the quality view besides the overall score?

The questions are free to read. Answers with their sources and self-grading are included with the pack.

Keep reading with the Agentforce Specialist pack

  • Core concepts
  • Exam traps
  • Worked scenario

The pack includes:

  • All 26 lessons of the Agentforce Specialist guide, with reading progress
  • Interactive decision walks and step-by-step sequence steppers
  • Inline practice questions and one-click topic practice (16 questions for this lesson)
  • An official source cited for every claim
  • An AI tutor that answers from the guide
  • Unlimited timed practice exams

Practice for the Agentforce Specialist exam