HTTP 200 Doesn't Mean Success: How Silent AI Agent Failures Slip Through

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A 58-day deployment of 78 AI agents logged 6,768 failed outputs, none of which were caught by standard HTTP status checks — every failed response returned a 200 OK code. The failures stemmed from structural issues such as missing required fields, wrong language, forbidden phrases, or mismatched output for the wrong workflow stage. Practitioners are advised to treat model responses as untrusted data and validate them against a defined contract before passing output to downstream stages. Deterministic validation checks — not secondary LLM judgments — are recommended to enforce output shape, language, and policy at each boundary. Storing structured rejection evidence, including artifact IDs, failure reasons, and contract versions, is critical for debugging and preventing silent workflow breakdowns.

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