AI Agent Regression Testing After a Prompt or Model Change

AI agent regression testing evaluates whether an agent’s behavior remains consistent after changes to prompts, models, tools, retrieval settings, or conversation context by rerunning a locked set of cases against defined behavioral contracts. Because valid agent responses can vary in wording, tests should focus on structural outcomes such as required tool calls, arguments, requests for missing information, and non-negotiable policy invariants rather than exact text matches. Reproducibility requires concrete model slugs instead of moving “latest” aliases, logging the actual serving model, and holding prompts, tools, tool results, cases, judges, and compatible inference parameters constant during model comparisons. Suites should run automatically when relevant files change and periodically to detect provider-side updates, while remaining separate from ordinary unit tests because model calls incur costs. Deterministic checks are suited to tool behavior and safety boundaries, whereas calibrated independent LLM judges can assess open-ended quality, with score changes treated cautiously due to noise and bias. Hard invariant failures, such as approving a refund beyond an authorized limit instead of escalating it, should block release, while failures on both baseline and candidate usually indicate a flawed test harness rather than a model regression.

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