eval-harness

You can measure AI agent reliability by defining pass/fail criteria for Claude Code tasks and tracking pass@k and pass^k metrics across multiple test runs to evaluate completion consistency.

Eval-driven development is a workflow where you define pass/fail criteria for AI tasks before implementation and run continuous capability testing to track regressions with every prompt or code change.

To run regression testing, define capability evals for your existing functionality and execute multiple test runs to calculate pass@k and pass^k metrics, generating standardized reports to identify performance drops.

Yes, you can benchmark agent performance across model versions by applying the formal evaluation framework to calculate reliability metrics like pass@k and pass^k, giving your team measurable confidence in task completion.

Pass@k measures agent success within k attempts, while pass^k measures if all k attempts succeed, providing standardized metrics to evaluate AI task completion and track regressions across iterations.

You should use an eval harness when adding new features or modifying prompts in AI-assisted development and need to eliminate guesswork around unintended regressions by tracking measurable reliability data.