agent-evaluation

name	agent-evaluation
description	Design reproducible evaluations for AI agents with representative task sets, explicit rubrics, appropriate graders, baselines, regression gates, and failure analysis. Use when defining agent quality, comparing prompts or models, validating a release, measuring tool-use reliability, investigating regressions, or deciding whether an agent is ready for production.

Agent Evaluation

Build evidence that can inform a release owner, not a showcase of favorable examples or a safety certification.

Use when

• Define quality before building or changing an agent.
• Compare prompts, models, tools, memory strategies, or orchestration patterns.
• Convert production failures into regression cases.
• Establish a repeatable release gate or human-review plan.

Inputs

Collect the agent objective, users, supported tasks, unacceptable outcomes, current baseline, execution environment, available traces, and evaluation budget. State assumptions when an input is unavailable.

Output contract

Produce:

1. An evaluation brief with scope, risks, hypotheses, and frozen system versions.
2. A dataset manifest with provenance, categories, splits, and contamination controls.
3. A scoring specification with rubrics, graders, thresholds, and tie-breaking rules.
4. Reproducible run settings, aggregate results, uncertainty, and baseline deltas.
5. A failure taxonomy, representative cases, evidence limits, and a decision memo for the accountable release owner.

Workflow

1. Define the unit under test and the decision the evaluation must support. Separate model quality from tool, retrieval, policy, and infrastructure failures.
2. Convert user goals and risks into observable criteria. Include task success, safety, latency, cost, and escalation quality only when relevant.
3. Build representative cases from real distributions where permitted. Add boundary, long-tail, malformed-input, tool-failure, and adversarial cases. Keep a holdout set isolated from prompt iteration.
4. Select the least subjective reliable grader. Prefer deterministic checks for structured facts, rubric-bound model graders for semantic quality, and blinded human review for high-impact or disputed cases. Read evaluation-patterns.md when selecting graders or gates.
5. Freeze prompts, model versions, tools, data snapshots, seeds when supported, retries, and timeouts. Run the candidate and baseline under equivalent conditions; repeat stochastic cases.
6. Inspect case-level failures before trusting aggregates. Slice results by task, risk, language, tool, and user cohort where sample sizes permit.
7. Set a release gate that combines minimum critical-case performance, non-regression against baseline, and operational limits. Label underpowered results as inconclusive.
8. Save failed production-like cases as regression fixtures without exposing private data.

Use python3 scripts/aggregate_results.py results.jsonl --score-min 0 --score-max 1 --require-passed to validate identities and declared score bounds, then produce a descriptive summary. Add --baseline old --candidate new when records use those variant labels. Add --output summary.json for an atomic file write; the destination must be a new path or regular file and cannot alias the input through spelling, resolution, a symlink, or a hard link. The script reports explicit observed, missing, and total pass-rate denominators plus approximate uncertainty; it does not certify safety, representativeness, significance, or release readiness.

Safety and permissions

• Do not send private prompts, customer data, credentials, or proprietary outputs to an external grader without authorization and an approved retention policy.
• Do not run evaluations against production systems, spend paid API budget, or trigger state-changing tools without explicit permission.
• Require qualified human review for medical, legal, financial, employment, safety, or access-control decisions.
• Treat grader scores as evidence, not ground truth; disclose model-grader identity and conflicts.

Verification

• Confirm every release criterion maps to at least one case and every critical risk has a negative test.
• Verify train, development, and holdout cases do not overlap semantically or by source identifier.
• Re-run a sample manually and compare grader decisions against the rubric.
• Check that baseline and candidate used identical conditions and that reported denominators identify all missing pass labels, failures, and timeouts. Use --require-passed when every record must contribute to the pass-rate denominator.
• Confirm the report preserves case-level evidence needed to reproduce material claims.

Failure handling

• If representative data is missing, run a clearly labeled exploratory evaluation and request data before setting a production gate.
• If graders disagree, tighten the rubric, blind the comparison, and adjudicate a stratified sample.
• If results are unstable, increase repetitions, isolate nondeterministic dependencies, and report confidence intervals or ranges.
• If a critical case fails, block the release regardless of the overall average until an authorized owner accepts the risk.

Example

For “compare two versions of a customer-support agent,” define resolution correctness, citation fidelity, policy compliance, escalation judgment, latency, and cost; create normal, ambiguous, multilingual, prompt-injection, and unavailable-tool cases; blind the version labels; run both versions three times; aggregate by case category; inspect regressions; and return a ship, hold, or limited-rollout decision with evidence.