Eval Suite As Product-Research Interface

A shared eval suite resolves spec ambiguity between people up front and becomes the highest-bandwidth channel through which a product team hands research or engineering a concrete target to optimize against.

Writing evals forces an explicit answer to "what does success mean here" — without that, two people reading the same spec can build inconsistent handling of the same edge case, because natural-language requirements under-specify exactly the boundary cases an agent will actually hit. Encoding those cases as scored examples — whether a small hand-eyeballed example suite or a scaled capability or regression suite — removes the ambiguity: the suite is the spec, at whatever level of edge-case precision the examples cover.

Once that suite exists, it does a second job beyond catching regressions: it becomes the interface between the team that knows what "good" looks like (product) and the team that can move the metric (research or engineering). A product team handing over "make it feel more careful" is a request neither side can act on precisely; handing over a suite where "careful" is a scored, reproducible pass rate gives research something concrete to optimize against and product something concrete to check the result with — the same role a loss function plays between a spec and an optimizer. This is a distinct benefit from the suite's regression-catching role: it changes who can act on quality feedback and how fast, not just whether quality is being tracked.

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Sources

• Demystifying evals for AI agents (Anthropic), Why build evaluations?