Find out why your vibe-coded app keeps breaking

If your vibe-coded app keeps breaking in ways you cannot pin down, the reason is usually that error tracking only catches part of the story. It catches unhandled exceptions and crashes, which is worth having, but a vibe-coded backend often runs without throwing while quietly doing the wrong thing. That is why you watch behavior, not just exceptions. Here is the difference, and what actually catches these failures.

What error tracking is, and what it catches

Error tracking, also called error monitoring, does one thing well: it captures the exceptions and crashes your app throws, groups the duplicates into a single issue, and tells you with a stack trace what broke, where, and how often. Sentry is the archetype, and a whole category of tools works the same way. Drop in the SDK, and when your code throws an uncaught exception, you get a grouped, deduplicated alert instead of digging through raw logs by hand.

This is genuinely useful and you should have it. But notice the trigger: error tracking only fires when something throws. Its whole model is the unhandled exception, so if a bug never raises an error, an error tracker never sees it. And plenty of bugs never throw. The code runs, returns something, and carries on while doing the wrong thing, and in a vibe-coded app those are exactly the failures you get most.

Why vibe-coded apps fail differently

A vibe-coded app is built largely from AI-generated code, and the evidence is that this code carries more latent problems than hand-written code, not fewer. GitClear's 2025 analysis of 211 million changed lines from Google, Microsoft, Meta, and enterprise repos found copy-pasted code roughly quadrupled and outnumbered refactored code for the first time, while refactoring dropped from about 25% of changes to under 10%.¹ Duplicated, unrefactored code is where near-miss bugs hide: the same flaw copied five times, four of them slightly wrong.

Security is worse. Veracode's 2025 report tested over 100 models on 80 tasks and found that 45% of AI-generated code introduced a vulnerability from the OWASP Top 10, the standard list of the most serious web application risks, and that newer, larger models were no more secure than old ones.²³ The pattern across both is the same: the code compiles and usually works on the happy path, while carrying silent logic errors, duplicated bugs, and security holes. Almost none of those throw an exception.

The errors that never throw

The failures that actually hurt in a vibe-coded app tend to be the quiet ones. A few that an error tracker will never show you:

• Wrong but plausible output. A function returns a value that looks fine and is not. No exception, just a bad number flowing downstream.
• A payment marked successful that never charged. The happy path ran to completion; the side effect silently failed.
• A validation that quietly passes. AI-generated guard code that looks right but lets bad input through.
• An LLM or agent step returning a confident wrong answer. The call succeeded, the content is wrong. Nothing to catch.
• A security hole. A missing authorization check is not an error, it is code working exactly as written, just insecurely.

None of these raise an exception, so error tracking is blind to all of them. What they have in common is that the system did something other than what you expected, which is precisely what monitoring is for.

Error tracking vs monitoring

The clean way to see it: error tracking is a subset of monitoring, the slice that covers crashes. Monitoring is the broader job of watching whether the system behaves as expected and catching it when it does not, whether or not anything threw.

This does not mean throw away error tracking. It means treat it as the floor. On top of crashes, watch the things that tell you the code is doing the right thing: error and success rates, latency on critical routes, and the business outcomes you actually care about. That set is the four golden signals plus your own key events, and it is what catches the silent failures.⁴ We cover the full version of that in monitoring errors in production.

From catching crashes to watching behavior

Fixter is as easy to drop in as an error tracker, but it watches your whole backend instead of just exceptions. You add the SDK, your logs, traces, and metrics are stored, and you define what "working" means in plain language: payments succeed, the agent returns valid output, the scraper finishes every job. When behavior drifts from that, whether it crashed or not, Fixter runs the first pass of the investigation and tells you the cause and who is affected.

There is a second reason this fits vibe-coded apps: Fixter never needs your codebase. It stores production data, not your source, which matters more when the code was written by an AI and you are not fully sure what it does. You keep the code on your machine and use the production signal to check that it behaves.

That is the shift: from waiting for the code to crash to checking that it does what you meant. Keep your error tracker for the crashes, and add behavior monitoring for everything the AI wrote that runs fine and is still wrong. If you are weighing tools, see Sentry alternatives, and for the trust angle, why your code never has to leave your machine.

Frequently asked questions

What is error tracking?

Error tracking, also called error monitoring, is capturing your application's unhandled exceptions and crashes, grouping the duplicates into issues, and alerting you with a stack trace and how often each one happens. Sentry is the best-known example. It answers 'the code threw, where and how often', but by design it only fires when something actually throws.

What is the difference between error tracking and monitoring?

Error tracking triggers on thrown exceptions and crashes. Monitoring is broader: it watches whether your system behaves as expected and catches deviations that never throw, like a payment marked successful that did not charge, or a function returning a plausible but wrong result. Error tracking is really a subset of monitoring, the part that covers crashes.

Why is error tracking not enough for a vibe-coded app?

Because vibe-coded apps tend to run without crashing while still being wrong. Studies of AI-generated code find high rates of security vulnerabilities and rising duplication and churn, and most of those problems are not exceptions, they are silent logic and security flaws. Error tracking never sees them because nothing throws.

Can error tracking catch security vulnerabilities?

Mostly no. A security vulnerability like an injection flaw or a missing authorization check is not an error, it is code working exactly as written, just insecurely. Error tracking fires on crashes, not on insecure-but-functioning code, which is why it misses the security problems that are common in AI-generated code.

Do I still need Sentry-style error tracking?

Yes. Catching crashes with a stack trace is genuinely useful and you should keep it. The point is that it is a floor, not the whole job. For a vibe-coded backend you pair it with monitoring that watches behavior: success rates, latency, and the business outcomes that tell you the code is doing the right thing, not just not crashing.