agent-evaluation

Measure agents before trusting them — task suites, regression gates, calibrated judging, scorecards.

SKILL.md

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Agent Evaluation

Agent changes (model, prompt, tools, context) are behavior changes. Measure them like code: task suites, regression gates, honest scorecards.

When to use

  • Changing any model, prompt, tool set, or retrieval context for an agent.
  • Deciding between agents for a role ("is the new model actually better here?").
  • Turning an incident into a permanent regression test.

Method

  1. Task suites: golden tasks per capability with acceptance criteria — success definition, allowed cost, allowed latency, safety constraints. Version them with the agent.
  2. Reproducible runs: pin model versions, seeds/temperatures, tool states, fixtures; run the same suite per candidate; store raw transcripts.
  3. Judging carefully: deterministic checks first (did it do the thing?); LLM-judges only when calibrated against human labels, blinded, with position bias controls. The model under test never judges itself uncalibrated.
  4. Regression gates: eval suite runs before merging agent changes; regressions block until explained (accepted trade-offs are written, not vibes).
  5. Beyond accuracy: track steps-to-completion, cost, latency, refusal/error rates, safety violations, and "silent wrongness" (confident bad output) — the metrics that hurt in production.
  6. Incident replay: every production misbehavior becomes a named test case (like bug→regression for code).
  7. Scorecards: publish a compact table per run (task, pass, cost, notes) — honest comparisons beat leaderboards.
  8. Watch benchmark hygiene: contamination (tasks leaking into training), variance across runs (run N times, report spread), and drift (re-baseline after model updates).

Quality gates

  • Suite versioned, criteria written before running.
  • Every agent-affecting change gated on an eval run.
  • Judges calibrated or replaced by deterministic checks.
  • Incidents replayed as tests; scorecards stored.

Red lines

  • No "feels better" promotion for agent changes; no cherry-picked runs; no uncalibrated self-judging.

Related

testing-strategy · poc-validation · agent-safety-guardrails (adversarial cases).