eval-harness

Regression testing for AI-assisted development workflows involves defining evaluation frameworks that verify existing behavior still works after edits, refactors, or model changes. You apply code-based checks, model-based review prompts, or human review to measure reliability.

Pass@k benchmark tracking is a metric system that measures whether iterative prompt or agent changes actually improve reliability. You need it when tracking capability testing across multiple attempts to ensure changes are ready to ship.

Yes, you can use model-based grading to evaluate Claude Code tasks. The evaluation framework supports code-based checks, model-based review prompts, and human grading to satisfy various capability testing and reliability reporting requirements.

The best way to define success criteria for AI agent evaluations is to establish capability testing frameworks that specify what a Claude Code session should accomplish before implementation begins, then run checks and report results with pass or fail criteria.

Yes, the evaluation framework supports stored eval artifacts. It tracks benchmarks, baselines, and pass@k metrics across iterative prompt or agent changes, producing final reports that show whether a change is ready to ship.

To measure reliability improvements when iterating on AI development prompts, define evaluation frameworks with pass@k metrics and baselines, rerun regression evaluations during development, and compare stored eval artifacts to track actual reliability gains.