eval-harness

To measure Claude Code task completion reliability, use eval-driven development to define pass/fail criteria for agent tasks and calculate pass@k metrics across multiple attempts. This framework quantifies how often AI agents complete tasks successfully.

Eval-driven development (EDD) is a framework that defines clear pass/fail criteria for AI agent tasks before implementation. It aligns team expectations, reduces rework, and ensures AI-assisted development work meets defined quality standards.

Set up regression tests for prompt and model version changes by creating a regression testing suite that runs automated evals. This ensures existing functionality does not break when updating prompts, models, or code.

Yes, you can track pass@k metrics for Claude Code workflows to confirm a feature is ready for release. Calculate pass@3 reliability across consecutive runs to verify the agent consistently completes tasks successfully.

You need a formal evaluation framework to ensure consistent Claude Code quality because it eliminates guesswork by providing measurable, consistent task completion standards, automated eval execution, and performance reporting.

Relying on ad hoc testing instead of regression evals limits AI agent reliability by failing to catch regressions early when prompt, model, or code changes occur, lacking formal pass@k metrics to quantify performance consistently.