AI-generated error handling frequently looks safe while doing the opposite: it catches a real failure, logs a quiet warning, and returns a value that pretends nothing went wrong. That pretense is the actual danger, because the bug does not vanish, it just resurfaces three layers downstream where nobody can trace it back to its source.
The Details:
Models default to broad except blocks and fallback returns because failure behavior was never specified in the prompt. Without architectural context about what the system promises users, the safest-looking move for the model is to keep execution running rather than halt it, even when halting is the correct answer.
A stale cache masquerading as fresh data, or an empty dict standing in for a failed API call, is not graceful degradation, it is a fabricated fact. The useful test is whether the degraded output is still true and whether the next layer up knows it received a degraded version; if either answer is no, the handler is hiding a lie rather than managing a failure.
Genuine graceful degradation applies to features, not facts. A personalization service timing out and falling back to generic search results is honest, because the user still gets something real. A financial balance or auth check silently substituting old data for current truth is a different category entirely and should crash loudly instead.
The fix is procedural: audit every try/except in AI-generated code by asking who learns about the failure, when they learn it, and whether downstream data stays trustworthy. Flag any bare exception catch that can't name a specific failure mode, treat any return-on-failure as needing a comment, and actually trigger the failure path in a test rather than trusting handling that has never run.
Bottom Line: A system that fails silently has not avoided the failure. It has only postponed the moment someone discovers it, and made that moment much harder to debug.
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