eval-driven-development

You can validate agent behavior by defining an eval with expected outcomes and pass@k criteria before implementation, establishing a baseline, then re-running it after updates to confirm capability and regression performance.

Eval-driven development is a testing approach where you define expected behavior outcomes and pass@k criteria before modifying agent rules, skills, or prompts to assess capability and prevent regressions.

You test for regressions by running an eval against the current agent version to establish a pass/fail baseline, applying your rule or skill updates, and re-running the eval to verify the expected outcomes still pass.

Yes, you can apply this eval approach to continuous learning pattern extraction by defining expected behavior outcomes and pass@k criteria to ensure confidence in behavior consistency when new patterns are extracted.

Before running agent capability validation, you need to define clear input scenarios, expected results, and pass@k criteria to assess whether the agent updates meet the required behavior consistency threshold.

The best way to ensure prompt modifications do not break existing capabilities is to establish an eval baseline before changes and verify the updated prompt still passes the configured pass@k threshold for expected outcomes.