eval-harness

AI regression evals for Claude Code workflows are configured using a formal evaluation framework that defines tests to ensure existing functionality remains intact after code changes. It coordinates an end-to-end workflow from definition to structured reporting.

Eval-driven development tracks AI reliability by running capability and regression evals across model versions and code changes. It uses pass@k metrics and structured evaluation reports to verify new capabilities before integration and monitor traceable results over time.

AI evaluation frameworks support code-based, model-based, and human graders to cover both deterministic and open-ended assessments. This allows flexible scoring of Claude Code tasks depending on the complexity of the capability being tested.

Yes, the evaluation framework is designed to define and run capability evals that explicitly verify new AI capabilities before integration. It implements eval-driven development principles to validate functionality against a baseline.

The framework supports pass@k and pass^k metrics to measure evaluation results over time. These metrics provide traceable artifacts within a structured evaluation report to gauge reliability across different model versions.

To run a baseline evaluation for a Claude Code task, you install the eval harness and define a capability eval. The framework then coordinates the end-to-end workflow, executing the eval and generating a structured evaluation report.