What Actually Breaks When AI Builds Your App

I've personally triaged over 200 bug reports from vibe coders at this point. And after a while, you start seeing the same bugs show up again and again. It's almost predictable.

Doesn't matter which AI coding tool built the app. The failure modes are shockingly similar. Here's the breakdown.

What Bugs Appear When You Deploy a Vibe-Coded App to Production?

The most common bugs are auth redirect loops, missing database access policies, and broken environment variables. These three account for roughly 70% of the "it worked in preview" reports we get on VibeFix. The root cause is almost always the same: AI tools generate code that assumes a single environment. They don't think about the gap between your local dev setup and a deployed production app. Preview mode hides a lot of sins. Your localhost callback URL works fine until you push to Vercel or Railway and suddenly nothing does. And the frustrating part is that the AI never warns you. It doesn't say "hey, this won't work in production." It just generates the code and moves on. You only discover the problem after deploying, usually at the worst possible time. Below are the three specific failure patterns we see over and over, with the actual fixes that work.

Why Do Auth Redirects Loop After Deploying an AI-Built App?

Auth redirect loops happen because the callback URL in your auth provider dashboard doesn't match your production domain. This is the single most common bug we see on VibeFix, full stop. The generated auth flow creates an infinite redirect after login. You log in, get sent to the callback, which sends you back to login, which sends you to the callback... you get it. Your preview URL and your production URL are different, but the auth config only has one of them. In Clerk, this means checking your "Allowed redirect origins" list. In Supabase Auth, it's the "Site URL" and "Redirect URLs" fields. And if you're using NextAuth, look at your NEXTAUTH_URL env var. It's probably still set to localhost:3000. Check that first, before you touch any code at all. The browser network tab will show you the redirect chain clearly. Look for 302 responses bouncing between two URLs.

Why Does Database Access Break When Moving from Development to Production?

Database access breaks in production because AI tools rarely generate proper access control policies for your tables. Row Level Security policies (or whatever access control your database uses) either block queries that should work, or they're missing entirely and everything is wide open. I watched a founder spend three days confused because his app could read data in development but not in production. It was an access policy the AI forgot to generate. This happens constantly with Supabase RLS policies and Convex access rules. The AI writes the queries assuming full admin access, which is what you have locally. But production enforces the rules. Check your database dashboard and look at what rules exist for each table. Compare them against the actual queries your app runs. If you see select working but insert failing, you're almost certainly missing a policy for writes.

How Do Missing Environment Variables Break AI-Generated Apps?

Missing or misconfigured environment variables are the number one cause of "it works on my machine." I can't say this enough. Your local .env file has everything, but your deployment platform is a blank slate until you manually add each variable. Check that all your env vars are actually set on your deployment platform. Check that client-side variables have the right prefix (NEXT_PUBLIC_ for Next.js, VITE_ for Vite). And please, check that you're not accidentally exposing secrets to the browser. I've seen API keys in client bundles more times than I'm comfortable admitting. A common trap with Bolt and Lovable projects: the AI sets env vars in a .env file but the deploy step doesn't read from it. You need to add them in your hosting provider's dashboard separately. One quick way to verify: open your browser devtools, go to the console, and type process.env or check your app's network requests. If you see undefined where a key should be, that's your answer.

What Code Quality Problems Do AI Coding Tools Introduce?

AI tools generate code that looks correct but often contains subtle mismatches and conflicts. These aren't runtime crashes. They're the kind of bugs that compile fine, pass a quick glance, and then fail in confusing ways when real users interact with the app. The pattern is consistent across Cursor, Bolt, and Lovable. The AI is optimizing for "looks right in the chat window" not "works correctly in context." It doesn't have a mental model of your full codebase. It sees a slice, generates a response, and moves on. That means dependency versions clash, schema definitions drift from actual queries, and long sessions produce contradictory code. These bugs are sneaky because they often pass basic testing. You click around, things seem to work. But then a specific user flow triggers the mismatch, and suddenly you're staring at an error that makes no sense. Here are the three most common code quality failures.

Why Do AI-Suggested Package Installs Cause Dependency Conflicts?

AI tools will confidently suggest installing a package that directly conflicts with something already in your package.json. They don't check what's already installed before recommending a new dependency. I've seen apps end up with two versions of the same library, like React 18 and React 19 coexisting because Cursor added a package that pinned an older peer dependency. This causes bizarre hydration errors, duplicate provider warnings, and components that silently render the wrong version. Bolt projects are especially prone to this because the AI scaffolds the initial dependencies and then adds more during iteration without reconciling. Always check your package file before accepting any install suggestion. Run npm ls --depth=0 to see what's actually resolved. And if you see "UNMET PEER DEPENDENCY" warnings, don't ignore them. Another common one: the AI installs both axios and node-fetch when the project already uses the built-in fetch API. Three HTTP clients in one app is a mess nobody needs.

How Do Schema Mismatches Creep into AI-Generated Database Code?

Schema mismatches happen when the AI generates a database schema in one message and then writes queries that don't match it in a later message. Column names are wrong, relationships are missing, types don't align. This is one of the trickiest bugs to catch because the code reads fine on its own. Each piece looks correct in isolation. But the schema says user_id and the query references userId. Or the schema defines a column as text and the query treats it as jsonb. Compare your schema definition against the actual database calls. They're often just slightly out of sync. I've seen this in Prisma schemas, Convex table definitions, and raw SQL migrations alike. The fix is mechanical but tedious: put your schema and your queries side by side and check every field name and type.

Why Does AI Output Get Worse During Long Coding Sessions?

Context window drift causes AI tools to contradict their own earlier suggestions after extended back-and-forth sessions. Long AI sessions are a trap. After enough exchanges, the model's effective memory of your conversation degrades. It forgets what it changed three messages ago. It re-introduces bugs it already fixed. It suggests patterns that conflict with the architecture it set up earlier in the session. I've seen Cursor sessions where the AI refactored a component, then two prompts later generated code that assumed the old structure still existed. The sweet spot seems to be 10-15 exchanges before quality drops noticeably. When you notice the outputs getting weird, just start a fresh chat. Don't try to correct it in the same thread. Paste in the relevant files and explain what you need from scratch. It's faster than fighting drift.

Why Are Silent Failures the Hardest Bugs to Fix in AI-Built Apps?

Silent failures are the hardest bugs because everything looks fine from the outside, but critical functionality is broken underneath. There's no error message, no red screen, no console warning. The user completes an action, the UI responds normally, and behind the scenes nothing actually happened. Payments don't record. Webhooks don't fire. Data doesn't save. These bugs can run for days or weeks before anyone notices, usually when a customer complains that they paid but got nothing. AI tools are especially bad at generating proper error handling for async operations. They write the happy path and skip the failure cases. The result is code that silently swallows errors instead of surfacing them. I've seen try-catch blocks where the catch does literally nothing. No log, no toast, no rethrow. Just an empty block. That's AI-generated error handling in a nutshell. Here are the three silent failure patterns we see most often.

Why Do Payment Webhooks Fail Silently in Vibe-Coded Apps?

Payment webhooks fail silently because the payment provider processes the charge successfully but your app's webhook endpoint never receives or acknowledges it. Stripe or Razorpay webhooks failing silently is a nightmare to debug because there's no error on the frontend. The payment goes through on the provider's side but your app never knows about it. The customer sees "payment successful" but their account doesn't update, their order doesn't process, their subscription doesn't activate. Check the webhook endpoint URL, verify the signature validation logic, and make sure your redirect URLs are correct. About 40% of our payment bounties come from this exact issue. Also check your webhook logs in the provider's dashboard. Stripe and Razorpay both show delivery attempts and response codes. If you see 404s or 500s there, your endpoint is the problem, not the payment flow.

Why Do API Routes Work Locally But Break in Production?

API routes break in production because the deployment environment is missing configuration that exists on your local machine. This is a classic failure pattern. Your API works locally but dies in production. Almost always comes down to missing environment variables on the deployment platform or CORS settings that weren't configured for the production domain. But there's a third cause that's less obvious: serverless function timeouts. Vercel's free tier has a 10-second limit. If your AI-generated API route calls an external service that's slow, it silently times out and returns nothing useful. Bolt-generated backends hit this constantly because the AI doesn't optimize for cold starts or execution time. Check your deployment platform's function logs, not just your browser console. The error is usually there in plain text. And test with curl directly against your production URL to rule out frontend issues.

How Do You Catch Mobile Responsiveness Bugs in AI-Generated UIs?

Mobile responsiveness bugs happen because AI tools design for desktop viewport widths and don't test or account for smaller screens. The desktop version looks pixel perfect. Then you open it on your phone and everything overflows. Text breaks out of containers, buttons stack weirdly, and horizontal scroll bars appear on the entire page. Test at 390px width (that's an iPhone 14). Most of the time it's a container that doesn't have overflow-hidden or a flex layout that assumes desktop widths. Lovable apps are especially prone to this because the visual builder optimizes for the preview pane, which is desktop-sized. I'd also recommend testing at 360px (older Android devices) and 820px (iPad). Use Chrome DevTools device toolbar. If you spot overflow, look for fixed pixel widths in the CSS. The AI loves setting w-[600px] instead of max-w-full. That one change fixes most overflow issues.

How Can You Get Unstuck When Your AI-Built App Has Bugs?

The fastest way to get unstuck is to post a detailed bug report and let an experienced developer fix it for you. Auth is our most common bounty category. Session handling, token refresh, redirect flows. These are the areas where AI tools struggle the most, probably because auth logic has so many edge cases that don't appear in training data. Payment integration is second, followed by deployment issues. If you're stuck on any of these, you're not alone, and you don't need to spend days debugging something a human dev can fix in hours.

If you're posting a bounty on VibeFix, include which tool you used, be specific about what works and what doesn't, and attach screenshots of any errors. Copy your browser console output if you can.

Good bug reports get fixed in hours. Vague ones sit for days. That's just how it goes.

Post a bounty and get back to building.