Evaluation asks whether behavior meets a defined criterion on a declared dataset or production sample. Observability supplies traces, logs, metrics, correlations, and diagnostic context. Use both; neither proves what the other does.
Workflow
- Define the decision, risk, task contract, trajectory contract, and unacceptable outcomes. Select evidence by harm, reversibility, and deployment stage, not a staged completeness scale.
- Create an immutable dataset version and manifest before comparing versions. Declare provenance, rights/consent, slices, fixtures, expected side effects, contamination risk, limitations, retention, and changelog.
- Select complementary graders that observe the claimed property. Use deterministic checks for observable mechanics; use execution/environment checks for state and side effects; use human, model-judge, pairwise/ranking, domain, and safety review where appropriate.
- Run the candidate and baseline under comparable conditions. Preserve run configuration, stochastic repeats where variability affects the decision, failures/timeouts, and trajectory evidence.
- Report a multidimensional profile and uncertainty. Use paired comparisons where possible; inspect slices, missingness, base rates, confounders, effect sizes, and multiple comparisons. Non-significance is not equivalence.
- Apply a risk-tiered release gate: hard safety, privacy, authorization, and side-effect invariants cannot be averaged away. Record authority, insufficient-evidence outcomes, rollback, and follow-up. Load release-engineering when this evidence must be incorporated into artifact promotion, deployment, rollback, or a broader release train.
- Instrument production with minimized, redacted telemetry. Feed verified incidents and near misses into cases after consent, transformation, and contamination review.
When an evaluation needs intervention detail, keep the existing aggregate count separate from optional typed events. Absence of event detail means it was not collected; it does not mean zero. Collect only fields needed for the question, exclude raw messages and hidden reasoning by default, and do not treat lower counts alone as success. See trajectory review.
Stop when the supported decision, evidence gaps, residual risks, and responsible owner are recorded. Escalate rather than infer a pass when required evidence is unavailable or conflicts.
Load By Need
| Need | Load |
|---|---|
| Compare task-relevant discovery, external artifacts, delegated consumption, or fresh-session recurrence | references/agent-interface-enrichment.md |
| Choose evaluation evidence and contracts | references/evaluation-design.md |
| Separate performance, diagnostic, and replay evidence; connect offline to online measures | references/evidence-modes-and-metric-mirrors.md |
| Build immutable cases, fixtures, and provenance | references/datasets.md |
| Select or calibrate graders | references/graders.md |
| Validate judge stability, abstention, and escalation | references/judge-stability.md |
| Check evidence cherry-picking, overreach, or bounded in-workflow probes | references/grounding-probes.md |
| Provide requested machine-readable evidence or join multiple evaluation artifacts | references/decision-audit-record.md and templates/decision-audit-record.json |
| Evaluate grounded claims and attribute RAG failures | references/grounding-attribution.md |
| Define measures or compare runs | references/metrics-and-statistics.md |
| Evaluate a bounded Choice/Score/Noul decision model or GLiNER classifier inside an agent | system-one for its question contract, held-out labels, calibration where probabilities exist, and model-level latency; use this skill for the agent's end-to-end task and trajectory evidence |
| Review tools, state, recovery, or side effects | references/trajectory-review.md |
| Record optional, privacy-bounded human intervention detail | references/trajectory-review.md#optional-intervention-events |
| Triage a regression or decide release readiness | references/regression-and-release.md |
| Design traces, logs, metrics, or privacy controls | references/production-observability.md |
| Interoperate with OpenTelemetry | references/opentelemetry-genai.md |
| Need framework-specific implementation | references/framework-routing.md |
| Exercise the methodology with safe probes | references/synthetic-scenarios.md |
| Benchmark an agent's Supabase task competence with the official harness | supabase agent evals reference |
| Verify a source claim or status | references/source-index.md |
Templates
Use templates/eval-plan.md, templates/interface-enrichment-comparison.md, templates/dataset-manifest.md, templates/grader-specification.md, templates/trace-review.md, templates/run-report.md, templates/release-gate.md, and templates/metric-mirror.md. They are decision records, not checklists that manufacture evidence.
Use grounding probes only when the decision depends on evidence-backed claims. Check material qualifications and whether evidence supports the proposed action, not just the quoted sentence. Preserve separate findings, probe errors, and unknown outcomes; a probe score cannot authorize action.
Guardrails
- Do not use a scalar score, keyword hit, schema validation, or repeated output as proof beyond the property it directly observes.
- Do not capture prompts, outputs, tool arguments, intermediate reasoning, credentials, or personal data by default. Minimize before export, redact early, restrict access, set retention/deletion paths, and prepare incident response.
- Do not treat human judgment as definitive truth. Human and model graders require explicit rubrics, calibration, disagreement analysis, and revalidation when conditions change.
- Keep prevalence-oriented production samples separate from risk-enriched challenge cases. Do not silently reweight either into the other.
When Not To Use
Use systematic-debugging to investigate one active defect before proposing fixes. Use verification-methodology for general evidence-backed completion claims, secure-software-engineering to design preventive controls, and framework skills for SDK-specific code. To benchmark an agent's competence on Supabase tasks with the official scored scenarios, use the supabase agent evals reference — this skill covers methodology, not a named harness.