AI-Generated Code Has No Error Handling — And That Will Cost You

Ask an AI to build you an app that fetches customer data, displays it on a dashboard, and lets you update records. It will give you something that works. Click the button, data appears, update saves successfully. Looks done.

Now turn off your internet connection and click that button again. Or send a request while the database is running a backup. Or submit a form with an emoji in the phone number field. In almost every case, the app does not display a helpful error message. It does not retry. It does not log what happened. It just breaks — a white screen, a spinning loader that never stops, or a cryptic error message that means nothing to the person using it.

This is the happy-path problem, and it is the single most common flaw in AI-generated code.

What “Happy Path” Actually Means

The happy path is the sequence of events where everything goes right. The user enters valid data, the server responds instantly, the database is available, the third-party API returns exactly what you expected, and the network is perfectly stable.

AI coding tools are trained to get you from idea to working demo as fast as possible. That means they optimise for the happy path. Every function assumes its inputs are valid. Every API call assumes a 200 response. Every database query assumes the connection is open and the data exists.

In the real world, things go wrong constantly. Networks drop. APIs rate-limit you. Users paste data from Excel with invisible characters. Databases run out of connections. Servers restart. Payment processors have outages. These are not rare edge cases. They are Tuesday.

The Five Failures You Will Hit First

1. API Calls With No Retry Logic

Your app calls a third-party API — a payment processor, a mapping service, an email provider. The AI-generated code makes the call once. If it fails, it fails.

In production, transient failures are normal. An API might return a 503 for two seconds because it is deploying an update. A network blip might drop a single request. These are recoverable situations, but only if your code knows to wait a moment and try again.

Professional software uses exponential backoff — wait 1 second, try again, wait 2 seconds, try again, wait 4 seconds, try again. AI-generated code? One shot. Miss, and the user sees an error (if they are lucky) or a silent failure (if they are not).

2. No Timeout Handling

AI-generated code almost never sets timeouts on network requests. This means if an external service hangs instead of failing cleanly, your app hangs too. The user stares at a loading spinner that will never complete. In the worst case, the server-side request holds open a connection indefinitely, and after enough hung requests, your entire server runs out of available connections and stops responding to everyone.

A five-dollar API call to a geocoding service going unresponsive should not bring down your entire application. But without timeouts, it absolutely can and will.

3. Unvalidated User Input

Users are creative. Not maliciously — just human. They will paste a phone number with dashes when you expected digits. They will enter “N/A” in a required numeric field. They will upload a 200MB photo from their phone when you expected a compressed thumbnail.

AI-generated validation is either nonexistent or minimal. A required field check here, maybe an email format regex there. But real validation means checking data types, ranges, lengths, formats, and character sets. It means handling the difference between “this field is empty” and “this field contains a zero-width Unicode character that looks empty but is not.”

4. No Graceful Degradation

When one part of a well-built application fails, the rest keeps working. Your analytics dashboard cannot reach the reporting API? Show cached data with a timestamp and a notice. Payment processing is down? Accept the order and queue the charge for retry.

AI-generated apps treat every failure as total. One component breaks and the entire page crashes. There is no concept of partial functionality, cached fallbacks, or degraded modes. It is all or nothing, and in production, “nothing” means lost revenue.

5. Silent Failures That Corrupt Data

This is the most dangerous category. The app does not crash. It does not show an error. It just quietly does the wrong thing.

A webhook handler receives a malformed payload and saves partial data instead of rejecting it. A sync process encounters a conflict and overwrites newer data with older data. A calculation function receives a null value and returns zero instead of flagging the error. The user has no idea anything went wrong until they find discrepancies weeks later.

What Proper Error Handling Actually Looks Like

Why AI Tools Skip Error Handling

This is not a conspiracy. It is a natural consequence of how these tools work.

When you prompt an AI to “build me a dashboard that shows customer orders,” the AI focuses on the primary intent: fetch orders, display them. Error handling is not part of the request, so it is not part of the response. The AI is doing exactly what you asked. The problem is that what you asked for and what you need in production are very different things.

Even when you explicitly ask for error handling, AI tools tend to add a generic try-catch wrapper that catches everything and does nothing useful with it. The error is caught, logged to the browser console where no one will ever see it, and the user is left wondering why nothing happened.

Real error handling is not a single pattern you bolt on. It is a philosophy that runs through every layer of the application. It requires understanding your specific business context: which failures are recoverable, which need human intervention, which should alert the ops team, and which should simply retry silently.

The Fix Is Not More Prompting

You cannot prompt your way to production-grade error handling. You can get better results by being specific — “add retry logic to all API calls,” “validate all form inputs on the server side,” “add timeouts to external requests.” But each of those prompts addresses one symptom. The underlying architecture — the way data flows through your app, the way failures propagate, the way state is managed — was not designed with failure in mind.

Retrofitting error handling onto a codebase that was not built for it is like adding seatbelts to a car that was not designed with crumple zones. The seatbelts help, but the car still is not safe.

The prototype did its job. It proved the concept. The next step is building it with the assumption that things will go wrong — because in production, they always do.