Why it matters: A job can exit with code zero, pass every test, and still be quietly wrong, and AI-generated code is especially good at hiding that from you.
The Details:
Human developers instrument code by accident. The false starts, the debugging print statements, the comments left behind while working through a problem all leave traces in the final file. AI models skip that entire journey, going straight from spec to working code with no uncertainty along the way, so nothing gets logged unless you explicitly asked for it.
The most dangerous pattern is the swallowed exception. When a model hits a risky operation, a missing file, an empty upstream response, a failed network call, it often wraps the risk in a broad try/except and returns a tidy default: an empty list, a zero, a "skipped" flag. The program never crashes, the tests still pass, and the only evidence anything went wrong is a wrong answer that nobody checked.
Tests make this worse, not better. They confirm the code behaves correctly on inputs someone thought to write, and say nothing about the inputs nobody anticipated. When those inputs show up in production, a swallowed exception leaves no trail to follow back to the cause.
The fix has to happen in the specification, before generation, not after. Naming the decisions explicitly, such as which branch ran, what input triggered a fallback, row counts before and after a filter, forces the generated code to answer the questions you will actually have later. Defining failure states matters just as much: telling the model that an empty upstream response is a failure, not a success with zero rows, closes one of the most common holes.
Bottom Line: Clean, fast, test-passing AI code can still be lying to you; the only defense is making it explain its decisions before it ever gets the chance to hide them.
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