eval-harness

Eval-driven development is a methodology that reduces uncertainty in AI-assisted development by defining formal evaluation frameworks with explicit success criteria to measure agent reliability. It replaces subjective impressions with repeatable checks for capability testing and regression detection.

To set up regression testing, you define an eval with explicit success criteria, then run the checks against a known baseline using code, model, or human graders. This detects workflow regressions during prompt or agent iterations.

Pass@k and pass^k are reliability metrics used in LLM benchmarking to measure AI consistency across attempts. They are generated by running structured evals with code or model graders, producing artifact-style logs for release readiness.

Yes, you can use model-based rubric scoring as an LLM-as-judge grader for ambiguous outcomes. The framework supports deterministic code-based checks, model-based grading, and optional human adjudication for sensitive results.

The best way to benchmark agent reliability is implementing structured capability evals with explicit success criteria, then running them across model versions to compare pass@k metrics against a baseline. This makes improvements measurable.