Developer fixes silent AI agent data corruption with input-output validators

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A software developer discovered that an AI agent deployed in production had been silently writing incorrect 1970 dates to a database after receiving date inputs in an unexpected format from an upstream system. Unlike typical AI hallucinations, the agent produced no errors or warnings — it simply misinterpreted an ambiguous date string and wrote a plausible but wrong value with full confidence. The issue went unnoticed for three days because the output was structurally valid, just semantically incorrect. The developer resolved the problem by adding two validation layers: one that checks incoming data before it reaches the agent, and another that verifies the agent's output before anything is written downstream. The fix, which took about an hour to implement, is now the default setup for all agents at the developer's company, Agent Enterprise, that write to persistent systems.

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