Eval Harness

Eval-driven development (EDD) treats AI evaluations as unit tests. It provides a structured approach to AI development, ensuring reliability and preventing regressions by formally testing capabilities before and after implementing features.

To create regression tests for an AI agent, define expected behaviors using regression evals to ensure existing features remain unbroken. You can verify implementations meet defined standards by running evaluation checks against your codebase.

Pass@k and pass^k metrics are tracked to measure AI reliability. These metrics quantify the consistency and success rate of AI capabilities, providing formal measurement standards for evaluation-driven development workflows.

Yes, AI evaluations support code-based, model-based, and human graders for comprehensive review. This multi-grader approach facilitates structured workflows, allowing flexible evaluation strategies from initial capability definition to final reporting.

You should set up capability evaluations before writing any code when developing a new AI agent feature. Define expected behavior and success criteria first, then implement the feature and run evaluation checks to verify standards are met.