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. Type a prompt into Cursor, Copilot, or Claude, and you'll have a running feature before your coffee gets cold. That speed is real, and it's not going away — but it's created a new failure mode we didn't have five years ago: code that looks finished because it runs, not because it's correct.
It runs on my machine and it's production-ready have always been different bars. What's changed is how easy it now is to mistake one for the other. A human engineer writing slowly tends to surface uncertainty as they go — they'll pause at a tricky edge case, leave a TODO, ask a question in Slack. An AI agent doesn't pause. It fills every gap with a plausible-looking assumption and keeps going, which means the places where it guessed wrong look identical to the places where it got it right.
We build with these tools daily, across both our own product (a proposal and delivery platform for agencies) and client engineering work. This post is about the six places we've actually watched AI-generated code fail once real users, real data, and real edge cases showed up — not hypothetical risks, but bugs we caught, some before they shipped and at least one after.
What we deliver
Assumed State vs. Real State
AI agents write for the happy path by default, because the happy path is what's statistically most common in their training data. Production has partial failures, retries, dropped connections, and race conditions that never show up in a single well-formed prompt. We've had agents ship code that assumed a database write always succeeds before the next step runs — fine in a demo, a data-integrity bug the first time a request times out.
Route and Resolution Drift
Generated routing logic often looks correct in isolation — a new endpoint, a new path, a clean diff. The problem shows up once canonical and legacy paths have to coexist, which is nearly always the real state of a production system. On our own platform, proposal, estimate, and share-link routes resolved correctly in testing but drifted apart once older URLs were still live in the wild, and it took a dedicated audit pass to find every place the two path systems disagreed.
Pattern-Matching Instead of Understanding
AI models classify by surface features in the text, not by actually understanding what a system does. We watched a GPS-based multiplayer game get scoped by an AI agent as enterprise IoT/SaaS infrastructure, because words like location tracking and real-time sync pattern-matched the wrong category. The generated proposal was fluent, confident, and completely wrong about what we were actually building.
Acceptance Criteria That Aren't Testable
A feature can look done in a demo and still ship without acceptance criteria specific enough to fail a test. Handles errors gracefully isn't testable; returns a 409 with a retry-after header when the resource is locked is. Vibe-coded features accumulate the former, because it's what a fluent-sounding prompt produces, and the gap only becomes visible when a real user hits the exact case nobody wrote a test for.
Deployment Stop Gates
Without an explicit, enforced gate between the AI agent says this is done and this goes live, code reaches production before its edge cases are actually verified. On our own team this is now a hard rule: no auto-fixes or auto-deploys without a human review step, and every release has to pass a set of verifier scripts before touching the production baseline. It's the single change that's caught the most real bugs.
The Second Reviewer
The fix isn't avoiding AI coding tools — the productivity gain is real and worth keeping. It's adding a deliberate audit step where a second model, or a second engineer, checks the gap between what the tool assumed and what's actually true of your system. In practice that means treating every AI-generated change as a draft that gets a structured prompt-and-review cycle, not a finished PR.
Frequently asked questions
- Does this mean you shouldn't use AI coding tools?
- No — we use them every day, across our own product and client work. The argument isn't against AI-assisted development, it's against removing human review from the loop because the output looks finished.
- How do you catch these issues before they ship?
- A combination of explicit deployment stop gates, verifier scripts that have to pass before a release is allowed, and a review step where a human (or a second AI pass) audits the diff between what the agent assumed and what's actually true of the system.
- Is this only a problem for junior teams or people new to AI tools?
- No — several of the examples above happened on our own senior team's work. Fluent, confident-looking output is the failure mode, and experience with the tool doesn't remove that; a deliberate review step does.
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