agent-evaluation
Measure agents before trusting them — task suites, regression gates, calibrated judging, scorecards.
SKILL.mdskills/agent-evaluation/SKILL.md
GitHubAgent 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
- Task suites: golden tasks per capability with acceptance criteria — success definition, allowed cost, allowed latency, safety constraints. Version them with the agent.
- Reproducible runs: pin model versions, seeds/temperatures, tool states, fixtures; run the same suite per candidate; store raw transcripts.
- 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.
- Regression gates: eval suite runs before merging agent changes; regressions block until explained (accepted trade-offs are written, not vibes).
- 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.
- Incident replay: every production misbehavior becomes a named test case (like bug→regression for code).
- Scorecards: publish a compact table per run (task, pass, cost, notes) — honest comparisons beat leaderboards.
- 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).