How to Regression-Test an AI Agent Without Breaking on Synonyms

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A software experiment explored how to build a reliable regression suite for an LLM-powered customer support agent that can produce different wording on each run. The core challenge is that strict text-matching assertions fail when the model rephrases responses, while loose checks can miss genuinely dangerous behaviour like an agent autonomously approving refunds. The proposed solution uses a scenario dataset paired with behaviour-focused evaluators that check actions and outcomes rather than exact wording. Each evaluator enforces its own pass threshold independently, with safety-critical checks set to 100% and language-quality checks allowed a small margin of error. The suite fails the build if any single evaluator misses its bar, ensuring no category of failure can be masked by strong performance elsewhere.

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