Why AI-Built Code Works... Then Suddenly Breaks

There is a pattern that every business owner who has built software with AI tools eventually encounters. It goes like this:

You build the app. You test it. It works perfectly. You show it to your team. It works perfectly. You deploy it. It works for a week, maybe two. Then something breaks. Not spectacularly — just quietly, in a way that takes hours to notice and days to diagnose.

You fix it. Something else breaks. Fix that. Another thing. The app that worked flawlessly in testing has become a whack-a-mole game of mysterious production failures.

This is not random bad luck. It is a predictable pattern with specific, identifiable causes. Understanding those causes is the first step to either preventing them or knowing when your tool has outgrown its AI-generated foundations.

The Core Problem: Testing Is Not Production

When you test your AI-built app, you are operating in controlled conditions. One user at a time. Clean data. Stable internet. The same browser. Predictable inputs. A known environment.

Production is chaos. Multiple users simultaneously. Data with years of accumulated inconsistencies. Mobile connections that drop mid-request. Browsers you have never heard of. Users doing things you never imagined. Server environments that differ from your development setup in dozens of invisible ways.

AI tools build for the controlled conditions. Production delivers the chaos. The gap between those two is where every mysterious failure lives.

Cause 1: Hardcoded Values Everywhere

This is the most common and the most frustrating cause of the “it worked before” problem.

AI-generated code is riddled with hardcoded values — configuration that is baked directly into the code rather than pulled from environment settings. API URLs pointing to localhost:3000. Database connections using a development password. Feature flags set to test mode. Timezone defaults set to UTC when your users are in AEST.

During development and testing, these hardcoded values happen to be correct. The app is running on localhost. The test database has the test password. Everything matches. The moment you deploy to a different environment, some of those values are wrong, and the failures they cause are unpredictable.

The timezone issue is a classic. Your app processes dates and times. It works perfectly when you test it in your browser because your browser sends the correct local time. In production, the server is in a data centre in the US, running on UTC. Suddenly, appointments are showing up on the wrong day. Reports are grouping data into the wrong periods. Nothing is “broken” — the code is running exactly as written. It is just running in a different timezone than you assumed.

Cause 2: No Logging or Monitoring

When something breaks in a well-built application, there is a trail. Log files record what happened, when, and why. Monitoring systems alert the team within minutes. Error tracking services capture the exact state of the application when the failure occurred.

AI-generated applications have none of this. When something goes wrong, there is no record. No alert. No breadcrumbs to follow. You find out when a user complains — if they complain at all.

This means debugging production issues in a vibe-coded app is like solving a crime with no witnesses, no security footage, and no forensic evidence. You are left guessing, or worse, trying to reproduce a problem that only happens under production conditions you cannot replicate in development.

Cause 3: Race Conditions

This is the one that makes business owners question their sanity. A race condition happens when two things happen at the same time and the result depends on which one finishes first.

Here is a simple example. Two people edit the same customer record at the same time. Person A changes the phone number. Person B changes the email address. Without proper handling, one of those changes overwrites the other. Person B’s save includes the old phone number because they loaded the record before Person A saved. Now you have lost Person A’s update and nobody knows.

In testing, you are the only user. There are no simultaneous operations. No conflicts. No races. In production, with multiple users and background processes running concurrently, race conditions can happen on every request.

AI-generated code has no concept of concurrent access. It reads data, modifies it, and writes it back with the assumption that nothing else touched that data in between. At scale, that assumption fails constantly.

Cause 4: Missing Environment Configuration

Production environments are different from development environments in ways that matter. HTTPS versus HTTP. Different domain names. Different API keys. Different memory limits. Different file system permissions. Different versions of runtime dependencies.

AI-generated code often assumes the development environment. It makes HTTP requests to APIs that require HTTPS in production. It writes temporary files to a directory that exists on your laptop but not on the server. It assumes unlimited memory for a data processing task that works fine with test data but crashes with production volumes.

Cause 5: Dependency on External Services

Your app calls a payment processor, a mapping API, an email service, and a third-party data provider. In development, these services are either mocked (fake responses) or used lightly (a handful of test calls). In production, they are called thousands of times under real conditions.

External services have rate limits. They have outages. They change their APIs. They return errors under load that they never return with light usage. AI-generated code treats these services as always available and always returning the expected response. When that assumption breaks — and it will — the cascading failure can take your entire app offline.

A payment processor returning a timeout instead of a success or failure response is a classic example. The AI-generated code has two paths: success and failure. It has no path for “we do not know yet.” So the order sits in limbo. The customer’s card may or may not have been charged. The inventory may or may not need to be adjusted. And nobody knows which state the system is in.

The Pattern Is Always the Same

Every one of these causes follows the same pattern: a simplifying assumption that is true in testing and false in production.

• One user at a time (true in testing, false in production)
• Data is clean and predictable (true in testing, false in production)
• External services always respond correctly (true in testing, false in production)
• The environment matches development (true in testing, false in production)
• Nothing happens concurrently (true in testing, false in production)

AI tools make these assumptions because making them produces working demos fastest. Every one of these assumptions is a landmine buried in your codebase, waiting for production conditions to step on it.

What You Can Do Right Now

If you have an AI-built app in production and you are experiencing mysterious failures, three actions will give you the most immediate value:

1. Add logging. Even basic request and error logging will transform your ability to diagnose problems. You cannot fix what you cannot see.

2. Audit hardcoded values. Search the codebase for URLs, API keys, configuration values, and timezone references. Move everything to environment variables with different values for development and production.

3. Identify concurrent operations. Any place where two users might modify the same data at the same time is a race condition waiting to happen. These are the ticking time bombs, and they get worse as your user count grows.

The prototype was built for controlled conditions. Production is not controlled. Bridging that gap is what turns a demo into a dependable business tool.