You fix the login bug. You test it, it works, you feel great. Then two days later a user tells you the checkout page is broken, and you never touched checkout. You go fix that, and now the profile page won't load. This is the loop I want to talk about, because it is one of the most demoralizing things a solo founder can go through, and it is not your fault.

I see this almost every week: someone built an app with Cursor, Lovable, Bolt, or Replit, and now every fix creates a new bug somewhere else. The dreaded "AI app fixing one thing breaks another" cycle. You feel like you are playing whack-a-mole with a mallet that spawns more moles. It is exhausting, and it makes you afraid to touch your own product.

Here is the good news: this pattern has a specific, understandable cause, and once you know what it is, you can break out of the loop. In this post I'll explain why it happens, how to recognize it, and exactly what to do so that fixing one thing stops breaking another.

What "fixing one thing breaks another" actually is

Developers have a name for this. It's called a regression: a change that fixes or adds one thing while quietly breaking something that used to work. One fix, one new break. That's a regression.

When you get regressions constantly, it means the pieces of your app are too tangled together. Programmers call this tight coupling. Imagine a set of Christmas lights where pulling out one bulb turns off the whole strand. In a well-built app, the pieces are more like separate rooms with doors between them: you can renovate the kitchen without the bathroom flooding. In a tightly coupled app, everything is one big open room, and moving the couch knocks over the lamp.

So the endless loop you're stuck in is really two problems stacked together:

  • Your code is tightly coupled, so a change in one place reaches into places you didn't expect.
  • There is no safety net to catch the damage before your users do.

Both of those come straight from how AI builders work. Let me explain why.

Why AI-built apps fall into this trap

AI coding tools are genuinely great at getting an idea live fast. I'm a fan. But they optimize for "this works in the demo right now," not "this stays working while it grows and changes." That single priority creates the exact conditions for cascading breakage.

AI writes for the moment, not the future

When you prompt an AI to add a feature, it produces code that makes that feature work. It is not thinking about how this feature will coexist with the twenty other things you asked for last week. It doesn't hold a mental map of your whole app the way an experienced developer does.

So instead of building clean, separate rooms, it tends to cram related logic together and reuse the same tangled pieces in ways that seem convenient but bind everything tightly. Each prompt adds another knot. By the time you have a real app, the knots are everywhere.

It repeats logic instead of sharing it

Here's a specific habit I see constantly. The AI needs to, say, calculate a user's subscription status in three different places. A careful developer writes that logic once and reuses it. The AI often writes it three separate times, slightly differently each time.

Now when you fix a bug in one of those copies, the other two still have the bug. Or worse, you fix the copy you found and don't even know the other two exist. The app behaves inconsistently, and you can never quite tell why.

There are no tests to catch the damage

This is the big one. In professional software, developers write automated tests: small pieces of code that automatically check "does signup still work? does checkout still work? does the profile page still load?" every time you make a change. If your fix breaks something, the tests scream before the code ever reaches a real user.

AI builders almost never set these up on their own. So you have zero safety net. You change something, it looks fine on the one screen you're looking at, you ship it, and the break surfaces three days later when a user hits a flow you forgot to check by hand. I wrote more about this in QA testing for AI-built apps, because it's the missing piece under nearly every one of these stories.

Using more AI to fix it makes the tangle worse

When a fix breaks something, the natural move is to paste the new error back into the AI and ask it to patch that too. Sometimes it works. Often it introduces a third problem, because the AI is working with the same blind spots that created the mess in the first place. It's like asking someone to grade their own homework. They repeat the same mistakes with total confidence.

The reason one fix breaks another is not bad luck. It's a tangled app with no safety net, and both of those are fixable.

If you want the deeper version of why this compounds over time, I go into it in the hidden cost of vibe-coding debt.

How to spot how bad the tangle is

You don't need to read code to gauge how tightly coupled your app is. The symptoms are visible from the founder's seat. Here's what tells me an app is deep in the whack-a-mole trap.

  • Unrelated things break together. You fixed the search bar and now the emails stopped sending. If two features that have nothing to do with each other keep breaking as a pair, they're secretly wired together.
  • The same bug keeps coming back. You fix it, it returns two weeks later. That usually means the logic is duplicated in several places and you only patched one copy.
  • You're scared to change anything. If you catch yourself avoiding a simple change because "last time I touched that, everything broke," that fear is a real signal, not paranoia.
  • Fixes take way longer than they should. A change you'd expect to take an hour eats your whole weekend because every fix reveals a new break.
  • You can't predict what a change will affect. In a healthy app, you can reason about what a change touches. In a tangled one, every edit is a coin flip.

If several of those sound familiar, the problem isn't the individual bugs. The problem is the structure underneath them, and that's actually reassuring, because structure can be repaired.

How to break the fix-one-break-another cycle

Here's the practical part. Some of this you can do yourself. Some of it is where a real developer genuinely earns their keep, and I'll be honest about which is which.

Step 1: Stop shipping fixes blind

Before you touch anything else, make one rule for yourself: never ship a fix without checking your core flows by hand. Keep a short written checklist of the things your app absolutely must be able to do. For most apps that's something like:

  1. A new user can sign up and log in.
  2. A user can do the main thing your app exists to do.
  3. A user can pay (if you charge).
  4. Key pages load without errors.

Every single time you make a change, run through that list yourself before you call it done. This is slow and manual, but it catches the most embarrassing regressions immediately. It's your temporary safety net while you build a real one.

Step 2: Make sure you can undo any change

You need to be able to roll back. Two things make this possible:

  • Your code lives in version control (usually GitHub). This gives you a saved history so you can return to the last version that worked instead of being stuck with a broken one. Most AI tools set this up, but confirm it.
  • Your data is backed up automatically. If a change corrupts data, you want a restore point. I've seen this go badly, and I wrote about it in when your AI app has no backups and loses data.

Once you know you can always get back to a working state, changes stop being tightrope walks. You can experiment without gambling your whole app.

Step 3: Get eyes on what's actually breaking

Right now you probably find out something broke when a user emails you. That's the worst possible way to learn. Add an error-tracking tool like Sentry. It catches problems the moment they happen and tells you what broke, where, and for which user.

This is the single highest-value upgrade for a tangled app, because the whole loop is driven by invisibility. You fix one thing, the break stays hidden, it surfaces days later out of context. Error tracking turns "something's wrong somewhere" into "checkout threw this exact error at 3pm." That alone shortens the loop dramatically.

Step 4: Build the real safety net (automated tests)

This is the permanent fix for the cycle. Automated tests are code that checks your app's important behavior on every change, so a regression gets caught in seconds instead of in production.

You do not have to write these yourself. But your app needs them, at least around the flows that matter most: signup, login, payment, and your core feature. Once those tests exist, the fear goes away. You can change something and get a clear yes or no on whether you broke anything, instantly. This is the difference between an app you're afraid of and an app you own.

Step 5: Untangle the worst knots

Tests catch regressions. Untangling prevents them. This means finding those duplicated pieces of logic (the subscription check written three times) and consolidating them into one, and separating things that should never have been wired together.

I'll be straight with you: this is the part where a real developer matters most. Untangling coupled code without breaking it requires understanding the whole map at once, and doing it safely is exactly what tests are for. This is the kind of work I get called in for, because doing it wrong makes the tangle worse, and the AI that created the tangle can't be trusted to undo its own blind spots. If your app is breaking specifically as it grows, this piece on why AI apps break at scale covers the same root cause from another angle.

Step 6: Know when to stop patching

There's a point where patching the same app over and over costs more than fixing the foundation once. If you've fixed the same bug three times, or you're spending more time firefighting than building, that's the signal. Sometimes the fastest path forward is having someone map the tangle, add the safety net, and repair the structure once. I wrote about that decision point in what to do when the AI can't fix your bug.

You can get off the treadmill

The fix-one-break-another loop feels like a personal failure, like you're just not smart enough to keep your own app running. It is not that. It's a predictable result of how AI builders assemble code fast: tightly coupled pieces with no tests to catch the fallout. Change those two things and the loop ends.

You can start today. Write your core-flow checklist, confirm you can roll back, and add error tracking so nothing is invisible. Those three moves alone will slow the whack-a-mole down enough to breathe.

The deeper work, untangling the knots and putting a real safety net around your app, is where I come in. I review AI-built apps every week, find the coupled, untested spots that keep biting you, and either fix them or hand you a clear plan so you know exactly what you're dealing with. If you're tired of every fix creating a new fire, let's talk about what your app actually needs.

Cover photo by Steve A Johnson on Pexels.