Common Bugs in AI Generated Apps (And Why They're So Hard to Spot)

AI tools can build you a working app in a weekend. They can also give you one that looks finished, passes every check you'd think to run, and still breaks the first time a real person uses it.

That's the new normal for founders shipping AI-generated apps. The code works in the demo. The landing page loads. The signup appears functional. But underneath, the same set of bugs keeps showing up across nearly every app that came out of a prompt rather than a plan. Not random bugs, either. Predictable patterns, repeated so consistently across AI-generated codebases that you can almost make a checklist (and we will, further down).

The tricky part isn't that these bugs exist. It's that they're invisible on the paths you naturally test. You click the happy path, everything works, and you ship. The stranger who arrives on a phone in Safari, fills in a name with an apostrophe, and hits "Submit" gets a completely different experience. As we covered in why vibe-coded apps need better testing, the inversion of the mental model is the core risk: you have the output but not the understanding, and the bugs hide in that gap.

This article catalogs the bugs that actually show up. Not theoretical risks, but the specific, recurring failure patterns that surface when AI-generated apps meet real users on real devices.

What counts as an AI-generated app

What is an AI-generated app?

An AI-generated app is a web application built primarily by describing features to an AI coding tool (such as Claude, Cursor, Bolt, or Lovable) and accepting the generated code, rather than writing it line by line. The builder directs the output but may not fully understand or have reviewed the underlying implementation. The app is real and functional, but its internals were authored by a model, not a person.

This matters because the bug patterns below aren't about code quality in the traditional sense. AI-generated code is often clean, well-structured, and syntactically correct. The bugs come from the way AI models generate code: optimizing for the visible, default, happy-path case while leaving edge cases, integrations, and cross-browser behavior either unhandled or handled with assumptions the model never states.

The 10 most common bugs in AI-generated apps

These are the patterns that show up again and again. They aren't ranked by severity; they're ranked by how often they survive to production because nobody thought to check.

1. Forms that look right but don't actually submit

The most expensive bug on the list. The AI builds a form that renders perfectly, has validation, shows a success message on submit, and never actually sends the data anywhere. The form handler is wired to a function that doesn't connect to your backend, or it posts to an endpoint that was never created.

This is the pattern we documented in depth in broken forms: the silent conversion killer. It's amplified in AI-generated apps because the model will generate both the form and the success state in one pass, making the form appear complete even when the critical link between front end and back end is missing.

What it looks like: A visitor fills out your contact form, sees "Thanks, we'll be in touch!", and your inbox never gets a thing.

2. Authentication flows that break on the second visit

AI models are excellent at scaffolding auth: login screens, signup forms, password reset flows, session management. But the generated auth code frequently has subtle state-management bugs that only surface after the initial setup.

Common variants:

• Login works after signup but fails after the session expires, because the token refresh logic was never implemented or silently errors.
• Password reset sends the email but the reset link leads to a 404 or an expired-token error.
• OAuth login works for the first provider but breaks on the callback for the second one.
• The "stay logged in" checkbox does nothing; the session always expires.

You won't catch these by testing signup once. They require testing the lifecycle: sign up, log out, come back, reset the password, switch devices.

3. Mobile layouts that collapse or hide critical elements

AI-generated UIs tend to look great at the viewport size the model was optimizing for, which is almost always a desktop-width screen. On mobile, the problems are consistent:

• Buttons hidden below the fold or under the keyboard.
• Forms where the submit button sits off-screen and can't be scrolled to.
• Navigation menus that open but can't be closed (or close but can't be reopened).
• Modals that render behind the content, or render correctly but can't be dismissed on touch.
• Text that overflows its container and becomes unreadable.

The Nielsen Norman Group has documented repeatedly that mobile usability failures are among the fastest drivers of abandonment. In AI-generated apps, the mobile experience is almost never the one the model was asked to build, so it's almost never the one that works.

4. Error states that don't exist

When an AI builds a feature, it builds the success path. Error states, the screens and messages a user sees when something goes wrong, are often simply absent.

• A payment fails and the user sees a blank screen or a raw error string.
• A file upload exceeds the size limit and nothing happens.
• The API returns a 500 and the user gets a white page with no way back.
• A required field is left empty and the form gives no indication of what's wrong.

This isn't a bug in the traditional sense; it's a missing feature. But from the user's perspective, hitting a wall with no guidance on what happened or what to do next is the same as the app being broken. The Nielsen Norman Group's error message guidelines make the case that a missing error state is often worse than a bad one, because at least a bad error message tells the user something went wrong.

5. Links and routes that lead nowhere

AI models will generate navigation, menus, footer links, and CTAs that point to routes the model intended to create but never did. Or it creates the route in one iteration and a later prompt reorganizes the file structure, leaving orphan links behind.

Common spots:

• Footer links to "/about", "/pricing", or "/terms" that 404.
• CTA buttons that point to a route that existed before the last refactor.
• Breadcrumbs that reference parent pages that were never built.
• Internal links in generated content that point to anchors that don't exist on the target page.

This is related to the problem we covered in the 404 page is the most ignored, highest-trust-killing page on your site: dead links erode trust instantly, and in AI-generated apps, they're the default rather than the exception.

6. Data that doesn't persist (or persists wrong)

The AI builds a settings page, a profile editor, or a preferences panel. You fill in your information, hit save, and see a confirmation. Navigate away and come back: your data is gone, partially saved, or saved to the wrong field.

This happens because AI models often generate the UI and the save handler but wire them to local state rather than a database call, or they make the database call but map the fields incorrectly. The save looks like it worked because the local state updates immediately. The problem only surfaces on a page reload.

7. Third-party integrations that are mocked or incomplete

When you ask an AI to "add Stripe payments" or "integrate with SendGrid," the model frequently generates code that looks like a full integration but is actually:

• Using test/sandbox keys hardcoded in the source.
• Calling the API but never handling the webhook that confirms the transaction.
• Mocking the response entirely so the UI behaves as if the integration works.
• Missing error handling for declined cards, expired tokens, or rate limits.

The result is a feature that works flawlessly in development and fails on the first real transaction. Stripe's own documentation warns that the gap between a working test-mode integration and a production-ready one is substantial, and AI-generated code almost always stops at the test-mode version.

8. Accessibility that's surface-level at best

AI models will add alt attributes to images and aria-label to buttons when prompted, but the deeper accessibility requirements are almost never met:

• Tab order that jumps randomly across the page.
• Focus traps in modals (you can tab in but never tab out).
• Form inputs with no associated labels, so screen readers can't identify them.
• Color contrast that fails WCAG AA on text the model chose.
• Interactive elements that can't be activated with a keyboard.

These aren't cosmetic. They affect real users, and depending on your jurisdiction, they carry legal exposure. WCAG 2.1 defines the baseline, and AI-generated apps rarely meet it without deliberate, manual remediation.

9. Performance cliffs on real data

The demo runs fast because the demo has five items in the list. AI-generated code frequently lacks pagination, virtualization, or any concept of data volume:

• A dashboard that loads all records at once and freezes when there are more than a few hundred.
• An image gallery with no lazy loading that grinds to a halt on a slow connection.
• Search that filters client-side over the full dataset instead of querying the server.
• API calls that return the entire table because the generated query has no LIMIT.

These bugs are invisible until you have real users generating real data. By the time you notice, the experience has already degraded for everyone.

10. Cross-browser inconsistencies

AI models generate code that works in the environment they're implicitly targeting, which is typically the latest Chrome. The gaps show up in Safari, Firefox, and older mobile browsers:

• CSS features (like gap in certain flex contexts, or :has()) that aren't supported in the user's browser.
• JavaScript APIs that throw in Safari but work in Chrome.
• Date inputs that render as a plain text field in browsers that don't support the native date picker.
• Scroll behavior, animations, and transitions that stutter or break entirely.

The Baymard Institute has documented how even small rendering inconsistencies erode user confidence. When the app looks polished in one browser and broken in another, users don't blame the browser. They blame you.

Why AI produces these specific bugs

These aren't random failures. They share a root cause: AI models optimize for the visible, stated, happy-path case.

When you prompt "build me a signup form," the model builds a signup form that works when you fill it in correctly, on the device you're testing on, in the browser you're using. It doesn't think about what happens when the form is submitted with a + in the email, or on a phone where the keyboard covers the button, or when the integration key expires in three months. It's not being careless. It's doing exactly what it was asked to do: build the thing you described, not the things you didn't.

This is the fundamental gap. Traditional development builds outward from an understanding of all the ways things can go wrong. AI development builds a surface that handles the ways things go right. The delta between those two is exactly the bug list above.

And because the code looks clean (well-named functions, reasonable structure, no obvious smells), the bugs pass code review too. They're not code-quality bugs. They're coverage bugs, lurking on the paths nobody prompted the model to think about.

A bug-hunting checklist for AI-generated apps

Before you ship (and after any significant prompt-driven change), walk through this:

1. Submit every form end to end. Not just "does it show a success message," but does the data actually arrive where it should? Check the database, the inbox, the CRM.
2. Test auth across the full lifecycle. Sign up, log out, log back in, reset password, try on a second device. Don't stop at first signup.
3. Open on a real phone. Not a resized browser window. A real phone, in Safari. Tap every button, fill every form, scroll every page.
4. Trigger every error state. Leave required fields empty, upload an oversized file, submit with bad data, disconnect from the network mid-action. Something should happen that isn't a blank screen.
5. Click every link and CTA. Footer links, nav items, in-page CTAs, breadcrumbs. Every one should resolve.
6. Save, leave, and come back. Any page that lets you edit and save should still have your data when you return.
7. Test integrations with real credentials. Switch from test keys to live keys. Process a real transaction. Send a real email. Confirm the webhook fires.
8. Tab through the page with a keyboard. Every interactive element should be reachable and activatable. Focus should never get trapped.
9. Load the page with real data volumes. Add a hundred records, a thousand. Does it still load in a reasonable time?
10. Open in Safari and Firefox. Not just Chrome. Look for layout shifts, broken inputs, and missing features.

For a more comprehensive version of this list, we maintain a full pre-launch website testing checklist and a step-by-step guide to testing AI-built apps end to end.

How to catch these bugs without burning your week

The checklist above is thorough. It's also the kind of thing that takes a full day to do manually, which means it gets done once at launch and never again. That's the wrong cadence, because these bugs reappear every time you prompt a new feature or refactor.

The fix is to automate the sweep. Instead of manually clicking through every form, link, and flow on every device after every change, run a scan that does it for you:

• Crawls your live site as a real visitor would. Follows links, clicks buttons, navigates flows, and flags what's broken.
• Tests across devices and browsers. Mobile, desktop, Safari, Chrome: the environments your users actually show up in.
• Fills out and submits forms. Not just checking that the form renders, but confirming it submits and the data lands.
• Screenshots the failures. So you see exactly what your user sees, not a line in a log.

This is what HowsMyApp does. It treats your site the way a stranger would and tells you what breaks. For AI-generated apps specifically, where the bugs are predictable but the paths are invisible, it's the difference between shipping confidently and shipping hopefully. It's also a core part of any real website UX audit.

Common mistakes when testing AI-generated apps

• Trusting the code because it looks clean. AI-generated code is well-structured by default. That doesn't mean it's correct. The bugs aren't in the syntax; they're in the gaps.
• Only testing the feature you just prompted. AI refactors can break things elsewhere. A new feature prompt can silently alter routing, state management, or shared components. Test the whole app, not just the latest addition.
• Skipping mobile because "it's responsive." The AI added responsive classes. That doesn't mean the layout actually works on a phone. There's no substitute for opening it on a real device.
• Assuming the integration works because the UI does. The UI showing a success state proves the front end ran. It does not prove Stripe charged the card, SendGrid sent the email, or the database wrote the row.
• Testing once and shipping. AI-generated apps change fast. Every prompt can introduce new bugs. Testing needs to be continuous, not ceremonial.

Frequently asked questions

Are AI-generated apps buggier than hand-written ones? Not necessarily buggier overall, but buggier in different, less visible ways. Hand-written apps tend to break loudly (syntax errors, crashes, failing tests). AI-generated apps tend to break quietly: the form submits but never sends, the link looks right but 404s, the page works in Chrome but not Safari. The total bug count may be similar; the visibility of those bugs is much lower.

Which AI coding tools produce the most bugs? The bug patterns above are consistent across tools (Claude, Cursor, Bolt, Lovable, Replit, and others). The issue isn't the specific model; it's the prompting paradigm. Any tool that generates code from a description will optimize for the described case and leave the undescribed edges unhandled. Better prompting reduces the gap but doesn't eliminate it.

Can I just ask the AI to fix the bugs it created? Sometimes. If you can describe the bug specifically ("the form submits but the data doesn't reach the database"), the AI can often fix it. But you have to find the bug first, and the whole problem is that these bugs hide on paths you don't naturally test. The AI can fix what you point at; it can't find what you can't see.

Do I need to read the generated code to find these bugs? No. Every bug on this list is detectable from the outside, by using the app as a real user would. That's exactly why end-to-end testing (walking the full journey on a real device and confirming each step works) is the right approach for AI-generated apps. We wrote a full guide on how to test an AI-built app end to end without reading a line of code.

How often should I test an AI-generated app? After every significant change, and on a recurring basis even when nothing has changed (because integrations rot and third-party dependencies update). The more you prompt, the more you should test. Continuous, automated testing is the only cadence that scales.

The bottom line

AI-generated apps break in predictable ways, and nearly all of them are invisible on the path you naturally test. The form that never submits, the auth that breaks on the second visit, the layout that collapses on mobile, the integration that's still running on test keys: these are the defaults, not the exceptions.

You don't need to read the code to catch them. You need to use the app the way a stranger would, on a real phone, in a real browser, with real input, and check whether the thing that's supposed to happen actually does. Do that systematically, and the bugs that survive in AI-generated apps stop being a surprise.

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Think your AI-built app is ready to ship? Run a free scan with HowsMyApp to find out what breaks when a real visitor shows up. It tests your forms, links, flows, and layouts across devices and browsers, and shows you exactly what your users will hit.