AI-generated code should be reviewed differently, not because it is inherently bad, but because it has predictable failure patterns. It often looks clean, compiles, and still creates risk. The review process should be designed to catch those patterns early, before they become production behavior.

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A safe review starts by confirming intent. Reviewers should be able to answer what the change is supposed to do, how it fails, and how it will be operated. If the author cannot explain this in plain language, the code should not merge. AI can generate syntax. It cannot generate accountability.

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Then check correctness through tests that matter. AI will happily create tests that mirror the implementation rather than validate real behavior. The bar is not “tests exist.” The bar is “tests would fail if the behavior was wrong.” If the change affects critical paths, require a test that would have caught the last incident you remember.

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Next, evaluate dependency and API claims. One of the most dangerous red flags is invented certainty: nonexistent library functions, misused SDK methods, and subtle mismatches in expected behavior. Reviewers should verify any new dependency, any new external API usage, and any security-sensitive call. If the code references a method you haven’t seen before, assume it is wrong until proven otherwise.

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Operational behavior is another common blind spot. AI-generated code often has weak error handling, unclear logging, and failure modes that are silent. Review should confirm that failures are observable, retries are safe, and timeouts and resource limits exist where they should.

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Finally, look for complexity disguised as elegance. AI is prone to over-abstraction. It will introduce indirection, patterns, and “smart” structures that reduce local code but increase system complexity. If the solution is harder to understand than the problem, it’s usually the wrong trade.

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If you want a single heuristic: AI code should not be merged because it looks good. It should be merged because it is provably correct, observable, and safe to operate.

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Axveria view: The goal of review is not to judge who wrote the code. It is to confirm the system remains changeable and trustworthy.