When AI Coding Tools Write Bugs Faster Than You Can Catch Them

I want to say upfront that I'm not writing this as an AI skeptic. I use Claude Code and Cursor every single day, I've deleted whole categories of boilerplate from my life, and I genuinely think going back to writing everything by hand would feel like giving up power steering. But I've also spent entire afternoons hunting down bugs that an AI tool introduced with total, unearned confidence, and I think the discourse around these tools swings too hard between "it's magic" and "it's useless," when the actually useful conversation is about the specific, recurring ways they fail.

The subtle logic bug is the dangerous one

The bugs that scare me aren't the ones that don't compile. Those get caught immediately. What scares me is the code that compiles, passes a quick manual test, looks completely idiomatic, and is subtly wrong in a way that only shows up under a specific condition three weeks later. I had Cursor write a function to deduplicate an array of objects by ID, and it handed me this:

function dedupeById(items: T[]): T[] {
  const seen = new Set();
  return items.filter((item) => {
    if (seen.has(item.id)) return false;
    seen.add(item.id);
    return true;
  });
}

This is correct. It's also almost identical to a version with a one-character difference that isn't. That's the pattern: the bug hides exactly where your manual testing habits don't look, because the AI's blind spots and a hurried developer's blind spots often overlap. What I actually got, a different week, from a different prompt, was a debounce implementation that looked completely correct on read but cleared the wrong timer reference under rapid re-renders in a React component, because it had captured timerId in a closure from the first render rather than reading a ref. It passed every manual click-test I did because rapid re-renders during casual testing are rare; it only surfaced once real users with flaky trackpads mashed a button.

Over-confident refactors are their own category of problem

Ask Claude Code or Cursor to refactor a file and it will do it, cleanly, with a tidy summary of what changed, and that confidence is exactly the problem. I asked for a "quick cleanup" of an Express route handler that had grown some duplicated validation logic across three endpoints, and the agent helpfully extracted a shared validateRequest middleware — a genuinely good instinct — but silently changed the order in which two of the validations ran, which meant an error that used to return a 400 with a specific message now fell through to a generic 500 first. Nothing about the diff looked alarming; it read like a strict improvement.

The lesson I've taken from this, and from a handful of similar incidents, is that "refactor" is a dangerous word to hand to an AI tool without a tight leash, because the model has no felt sense of which behaviors were load-bearing and which were incidental. It optimizes for code that looks better by the metrics it can see — fewer lines, less duplication, clearer names — and none of those metrics capture "this specific ordering of checks matters for an edge case that isn't in any test file." Now when I ask for a refactor I explicitly say "preserve exact behavior including error ordering and status codes" and I still diff every changed line against the original rather than skimming the summary.

Hallucinated APIs are less common than they used to be, but not gone

A year or two ago, hallucinated APIs were the headline failure mode — a model confidently telling you to call array.uniqueBy() which has never existed in JavaScript. That's gotten noticeably better as models have been trained on more recent, more accurate data and tools like Cursor and Claude Code ground themselves against your actual installed dependencies. But it hasn't disappeared, it's just moved to less obvious places. I had Copilot suggest a query using a popular ORM's syntax for filtering and deduplication, and the problem was that this particular part of the codebase had already migrated to a different ORM six weeks earlier, and the suggestion silently blended two ORMs' APIs in a way that failed at the type level, which is genuinely the best-case failure — it didn't compile, so I caught it in about ten seconds. The version of this I actually worry about is when the mixed-API suggestion happens to typecheck anyway because of loose typing somewhere in a shared utility, and the model has essentially guessed its way into working code by coincidence, code that then behaves unpredictably against a slightly different query plan than the one the model implicitly assumed.

What actually catches these bugs

I've settled into a few habits that specifically target these failure modes, rather than generic "review your code" advice. First, I've stopped accepting large multi-file diffs from an agent without reading every single changed line, even when the summary sounds reasonable — especially when the summary sounds reasonable, because a good summary is exactly what a subtly wrong refactor produces. Second, for anything involving timing, closures, or async state — the exact category that produced my debounce bug — I now explicitly ask the model to explain, in prose, what it assumes about render timing or event ordering before I accept the code, because writing that explanation out loud tends to surface the assumption that's about to be wrong.

a habit that's paid off: run the diff through a second, fresh model session
with zero prior context, and ask specifically "what could break here"
git diff main --stat
git diff main -- src/hooks/useDebouncedValue.ts | pbcopy

Third, and this one's a genuine mindset shift rather than a tactic: I treat AI-authored tests with real suspicion, because a model that misunderstands the requirement will often write a test that confirms its own misunderstanding rather than the actual spec. If Claude Code writes both the debounce logic and the test for the debounce logic in the same pass, and the underlying mental model is wrong, the test just becomes a second wrong artifact agreeing with the first one. I've started deliberately writing the test cases myself, by hand, for anything with real business logic, specifically so the test represents my understanding of correctness and not the model's.

None of this has made me want to go back to writing everything unassisted — the honest math still comes out hugely in favor of using these tools, especially for boilerplate, migrations, and the kind of code where being subtly wrong is cheap to notice and cheap to fix. But the places I've been burned all share a signature: confidence without context, code that reads as idiomatic because it's trained on idiomatic patterns rather than trained on your specific system's invariants. Being clear-eyed about that signature is, in my experience, the actual skill that separates people getting real leverage from these tools versus people quietly accumulating debt they'll discover in production. I still use these tools constantly, every single day, on nearly everything I write — I just no longer treat a clean-looking diff as a proxy for a correct one, and that single mental adjustment has caught more bugs before merge than any linter I've ever configured.