Learning outcomes
- Explain the operating model behind Agent Evaluation.
- Evaluate trade-offs and failure modes for Agent Evaluation.
- Apply Agent Evaluation to a production decision.
Mental model
Agent evaluation is a layered measurement system: grade the outcome, inspect the trajectory, verify tool effects, and classify the failure stage.
Learning outcomes
- Explain the mechanism and ownership boundaries behind Agent Evaluation.
- Compare the main design alternatives and their operational trade-offs.
- Diagnose common failures and select evidence for a production decision.
Theory
A useful suite separates deterministic checks, environment state verification, trajectory inspection, rubric grading, adversarial scenarios, and production outcome telemetry. Pass rates must be segmented by task and failure class so one aggregate cannot hide unsafe behavior.
Trade-offs
Rich trajectory grading improves diagnosis but costs storage, reviewer effort, and privacy exposure. Outcome-only grading is cheaper but cannot explain why the agent succeeded or failed.
Failure modes and misconceptions
Scoring only fluent final answers; using an unvalidated model judge; ignoring side effects; leaking private traces into evaluation; and failing to preserve task versions.
Decision scenario
An agent reports that it refunded an account. Decide which environment evidence, action trace, and policy checks must pass before the run counts as successful.
Why is a correct-looking final answer insufficient evidence that an agent completed a task safely?
Primary sources
anthropic-agent-evalsopenai-evals
Evidence assessment
Theory and decision mastery
Decision scenario
A production team must adopt Agent Evaluation while meeting quality, latency, security, and operating constraints.
Which decision process is most defensible?
Relationships
Agent Evaluation builds on Agent Control Loop.
Agent Evaluation informs governed production decisions and review evidence.
Primary sources
- Evaluation Best Practices — OpenAI, verified 2026-07-16
- Demystifying Evals for AI Agents — Anthropic, verified 2026-07-21