How I Built a Regression Suite for My AI Coding Agent's Prompts: 5 Lessons

A developer has shared their experience building a regression testing suite for AI coding agents after a minor prompt change led to unexpected, negative behavior in production. Treating prompts as configuration rather than code proved risky, as the agent began skipping critical tests. To solve this, the author implemented a robust CI-gated system featuring frozen repository snapshots, deterministic graders, and an LLM-based judge. The suite evaluates the agent's performance against 'golden path' tasks and incident-based fixtures, ensuring that prompt updates do not degrade system reliability. Key takeaways include prioritizing incident-based fixtures, asserting on behavioral outcomes rather than raw output, and budgeting for non-determinism by running multiple trials. This approach transforms prompt management from a manual, error-prone process into a disciplined engineering practice, ensuring that autonomous agents remain consistent and reliable as they evolve.