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Why AI Bug Findings Need Human Verification Before Being Trusted

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Coding agents can scan repositories and flag suspicious code, but identifying a potential problem is not the same as confirming one actually exists. A model may miss a guard clause, misread the call context, or construct a plausible-sounding explanation for a bug that cannot occur. Even when a second AI model agrees with the first, that consensus is not evidence — only direct testing, log checks, or reproducible failures can validate a claim. Static analysis tools and AI agents serve different roles: the former excels at known, well-defined checks, while agents are better at surfacing unexpected issues in unfamiliar codebases. The key discipline is treating an AI's bug report as a question worth investigating, not a finding worth reporting, until it can be independently verified.

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