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A practical guide to building eval suites that actually catch regressions — and why your prompt is code that needs CI.
Most agents that fail in production did not fail suddenly. They degraded, and nobody noticed, because nobody was measuring anything except whether the demo still looked good.
An eval is not a test suite bolted on afterwards. It is the thing that tells you whether a prompt change made the system better or merely different.
The best first eval set is not synthetic. It is the twenty real inputs where the system already got it wrong, captured verbatim with the output it produced and the output it should have produced. Twenty is enough to catch regressions. Two hundred is enough to catch drift.
An eval suite that runs weekly catches problems a week late. Wire it to the same moment a prompt changes, the same way a test runs on a commit, and the feedback arrives while the context is still in someone's head.
Good evals will sometimes tell you the clever new prompt is worse than the boring old one. That is the entire point. A measurement you only trust when it agrees with you is not a measurement.
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