AI Agents Return 200 OK but Correctness Remains Unverified in Production

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A developer building agentic AI systems has highlighted a critical gap in current observability tools: they track what an agent does but cannot verify whether its output is actually correct. In a test using a low-capability model on a structured task, the model answered correctly only 69% of the time despite outputs appearing valid and well-formed. Adding a grounded verification layer that checked outputs against actual constraints — and retried failures — pushed accuracy to 100%, without changing the model itself. The author argues that schema-valid, fluent responses can mask fundamentally wrong answers, yet most production agent stacks have no runtime mechanism to catch this. The piece calls for a 'runtime certification layer' that evaluates output correctness in the moment, bridging the gap between live logging and offline evaluations.

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