14 patterns AI code generators get wrong — and how to catch them

AI coding tools ship code fast. That's the point. But fast means they also introduce a specific class of bugs that are easy to miss in review — not because they're hidden, but because they look correct at a glance. After building an automated reviewer specifically for AI-generated code, I've cataloged 14 recurring patterns. Here's what they look like and how to spot them. Although an automated review agent can be helpful, manually spotting these 14 things can also benefit your work when using coding agents. The core problem AI code generators optimize for producing code that looks complete and compiles. They're not optimizing for runtime correctness, security, or the subtle behavioral contracts your codebase depends on. The patterns below all share the same root: plausible-looking code that silently fails, skips important work, or creates vulnerabilities that generic linters don't catch. 1. Fake error handling The most common one. The try/catch exists, the error variable is named, and