Reviewing AI-generated code well is a different skill from reviewing a colleague’s pull request, and treating it the same way is how subtle bugs slip through.
AI-generated code tends to look clean and confident regardless of whether it’s actually correct, the same surface-level polish that makes AI text convincing applies to code too. Read for logic correctness, not just whether the style looks reasonable.
Empty inputs, boundary values, and error states are where AI-generated code most often has gaps, since the model optimized for the common case described in the prompt. Test these deliberately rather than assuming they were handled.
Tools that specifically check output against your existing architecture, like Claude Code, reduce this risk but don’t eliminate it. Check that error handling, logging, and naming patterns actually match the rest of the codebase, not just that the code runs.
Asking the agent to explain why it chose a specific approach, or what edge cases it considered, often surfaces gaps faster than reading the code alone. See Terminal-Bench for independently tracked coding-agent reliability data.
