How to fix an AI-generated app that breaks in production

If you built an app with Cursor, Lovable, Bolt, or v0 and it works in the demo but falls over the moment real users show up, you're not doing anything wrong — you've just hit the wall every AI-accelerated build hits. The code that gets you to a working prototype is almost never the code that survives production. The good news: the failures are predictable, and fixing them rarely means starting over.

Why AI-generated apps break in production

AI coding tools optimize for “it runs on the happy path.” They're excellent at generating plausible, working-looking code fast. What they don't do is reason about the boring, load-bearing parts of a real product: concurrent users, malformed input, auth boundaries, rate limits, secrets management, and what happens when an external API is slow or down. Those gaps don't show up in a demo with one friendly user and clean data. They show up the day real people arrive.

Almost every AI-built app we take over fails for the same short list of reasons:

• Authentication that looks real but isn't — no session validation, authorization checks missing on the server, or trust placed entirely in the frontend.
• Secrets (API keys, database URLs, tokens) committed straight into the repo or shipped to the client bundle.
• No input validation, so a single unexpected value crashes a route or corrupts data.
• No monitoring and no backups, so you find out it's down from a customer, and you can't recover data when something goes wrong.
• Tightly coupled code where every new feature breaks two old ones.

Step 1: Triage — stop the bleeding

Before refactoring anything, find out what's actually on fire. Add error tracking (Sentry or similar) so you can see real exceptions instead of guessing. Add uptime monitoring so you learn about outages before your users do. Rotate any secret that has ever been committed to the repo — assume it's compromised. This first pass buys you visibility and a safety perimeter, usually in a day or two, and it changes the conversation from panic to a prioritized list.

Step 2: Secure the data

Data problems are the ones you can't undo, so they come next. Move every secret into environment variables and a secrets manager. Enforce authentication and authorization on the server — never trust the client to decide who can see or change what. Turn on automated database backups and actually test a restore. If you handle personal data, make sure it isn't leaking through logs or public endpoints. Once the data is safe, everything else is reversible.

Step 3: Add a safety net

Now make failure cheap. Wrap external calls in timeouts and error handling so one slow dependency can't take down the whole app. Add input validation at the boundaries. Put the project on continuous integration so a broken change gets caught before it ships, not after. A modest test suite around the paths that touch money, auth, and data pays for itself the first time it stops a bad deploy.

Step 4: Harden what matters

Only now is it worth refactoring — and only the parts that earn it. Untangle the code paths that keep breaking. Document the architecture so any competent engineer can pick it up. Resist the urge to rewrite everything; most of a working app is fine, and a full rewrite trades a known set of bugs for an unknown one.

When to rescue vs. rebuild

A rescue is the right call when the product works and people use it — the value is real, the foundation just needs shoring up. A rebuild is only honest when the architecture actively fights every change, or the original scope no longer matches where the product needs to go. The way to know is a proper audit: a written read of the codebase, security, and infrastructure that tells you what's broken, what's risky, and what each path actually costs. Deciding without that is guessing.

Shipping something people use is the hard part — you already did that. Making it survive real users is a solvable engineering problem.

If your AI-built app is buckling under real usage, that audit is where we'd start — a fixed-price review you keep either way, so you can make the rescue-or-rebuild call with real information instead of a hunch.