Vibe Coding vs. Production Engineering: What AI-Generated Code Actually Gets Wrong
AI coding tools have collapsed the time between idea and working demo to hours. But it runs on my machine and it's production-ready have always been different bars, and AI-generated code makes that gap easier to miss, not harder.
We build with these tools daily across client and product engineering. This is where we've seen AI-generated code quietly fail once real users, real data, and real edge cases show up, and what we changed in our own workflow to catch it before it ships.
What we deliver
Assumed State vs. Real State
AI agents write for the happy path by default; production has partial failures, retries, and race conditions the model never saw in its prompt.
Route and Resolution Drift
Generated routing logic often looks correct in isolation but breaks the moment canonical and legacy paths coexist, a class of bug that's easy to ship and hard to trace.
Pattern-Matching Instead of Understanding
AI models classify by surface features, not intent. We've seen a GPS-based multiplayer game get scoped as enterprise IoT because the description pattern-matched the wrong category.
Acceptance Criteria That Aren't Testable
Vibe-coded features often look done in a demo but ship without acceptance criteria specific enough to fail a test, which means nobody finds out they're broken until a user does.
Deployment Stop Gates
Without an explicit gate between AI says it's done and this goes live, code reaches production before its edge cases are actually verified.
The Second Reviewer
The fix isn't avoiding AI coding tools. It's adding a deliberate audit step where a second model or engineer checks the gap between what the tool assumed it could do and what's actually true of your system.
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