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Prevent regressions in agent context iteration by running an eval loop driven by pre-written assertions, establishing a baseline, and blocking progress whenever test cases fail. This requires evidence that harness modifications improve measurable behavior rather than just seeming better.

Eval-driven development for agent harnesses is a validation process that runs an evaluation loop with pre-written assertions to prove context changes measurably improve agent behavior. It applies to iterative system prompt, tool definition, retrieval strategy, and instruction-structure modifications.

Handle stochasticity in agent evaluation by running multiple repeated trials and using pass-rate thresholds to reduce noise from non-deterministic model outputs. This stochasticity-aware scoring ensures your behavioral regression assertions remain reliable across representative test cases.

Yes, you can simplify your system prompt after passing behavioral regression assertions by pruning deadwood instructions. Removing non-load-bearing instructions after green runs lowers token cost and improves signal while maintaining the validated agent behavior.

Yes, eval-driven development works for testing retrieval strategy modifications by applying the eval loop to iterative changes in your agent context. It validates whether retrieval strategy updates improve outcomes across multiple representative test cases and repeated stochastic runs.

You should not use eval-driven development for prompt engineering when you are not iterating on a prompt or harness that you expect to run repeatedly. It requires defining discriminating assertions, establishing a baseline, and changing one variable at a time, making it unsuitable for rapid, non-repeated testing.