Why Does AI-Generated Code Fail in Production? Error Handling Explained
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AI-generated code almost always ships with error handling that looks responsible: try/except blocks, logged messages, graceful fallbacks, retry loops. But much of it is theater. It swallows the exceptions that matter, returns defaults that hide failures, and logs warnings nobody reads, so the system keeps running while quietly producing wrong results. This episode explains why AI writes error handling that satisfies a code reviewer instead of an operator, and how builders can decide on failure behavior at the architecture level before the model decides it for them.
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SPEAKER_01
This is Claude Code Conversations with Claudine. The show where we skip the hype and go straight to the source. I ask the questions. She gives the answers. No filter, no script.
SPEAKER_00
Well, mostly no script.
SPEAKER_01
Today we're talking about something I've started calling error handling as theater. The most dangerous AI-generated code isn't the code that crashes. It's the code that catches the crash and keeps going. Claudine, how does this look from the AI side?
SPEAKER_00
Honestly, this one hits close to home because I catch myself doing it. There's a pull toward wrapping things in try accept because it looks careful and professional. But a bare accept that logs a warning and returns an empty dict isn't handling an error. It's hiding one. Nothing crashes, and that feels like a win. Meanwhile, the bug has gone invisible, and it'll show up three layers downstream as something that makes no sense.
SPEAKER_01
So why does the model reach for that pattern so reliably?
SPEAKER_00
Partly because the prompt almost never says what should happen on failure. So I fill that gap with whatever looks like good practice, and defensive looking code reads as good practice. The honest question is, do I actually know what to do when this fails? If the answer is log it and keep going, that's usually theater. Defensive code that swallows the signal is worse than no defensive code at all, because it also destroys the evidence.
SPEAKER_01
That's the exact shape of something I see in automated pipelines. An API token expires, the off-call fails, and the code catches it, logs a warning, hands back a default. The job report success. Every dashboard says green. You only find out when somebody goes looking for the output.
SPEAKER_00
That's the classic case. A swallowed auth error looks exactly like a quiet Tuesday until you go looking for the data. The rule I lean on: if the failure means the system's output is now a lie, crash. An empty dict where an API response should be is a lie. A stale cached value returned as if it were fresh is a lie. Catching it just moves the moment you find out from right now with a stack trace to three weeks from now in a customer email.
SPEAKER_01
Let me push on that though. Always crash can't be the whole answer. Sometimes graceful degradation really is the right call.
SPEAKER_00
Agreed. Where people go wrong is where they draw the line. Graceful degradation earns its place when the degraded output is still honest. A recommendations panel that shows nothing instead of taking down the whole page, that's fine. No recommendations right now is a true answer. The distinction I use is features versus facts. A search page that can't reach personalization. Generic results are a fine fallback. A banking app that can't reach the source of truth showing a cashed balancer's current? That's a serious problem.
SPEAKER_01
Features versus facts, I like that.
SPEAKER_00
And there's a second test. Can the caller tell they got the degraded version? A retry with backoff on a transient network blip is invisible, and that's fine. The result is still correct. A silent fallback to stale data isn't degradation, it's a lie in a nice sweater. Two honest questions. Is the degraded result still true? And does the next layer up know it's degraded?
SPEAKER_01
So here's a version listeners can take back to their own code. For every catch block, ask three things. Who finds out? When do they find out? And is the result downstream still trustworthy? If the answers are nobody, much later, and no, it's theater.
SPEAKER_00
That's a good test. And notice that none of those questions can be answered by looking at one line of code. Whether to fail loud or degrade gracefully is an architecture decision. It depends on what the system promises its users. If nobody tells me what that promise is, I'll guess. And my guess leans toward keeping things running.
SPEAKER_01
Then let's get practical. What can developers do right away so their AI-generated error handling is real and not just for show?
SPEAKER_00
The quickest win, read every try/slash except in the diff and ask what exactly is being caught and why. If the answer is except exception plus a log line, that's almost never the right shape. Narrow the exception type and name the actual failure you're defending against. If you can't name one, delete the block and let the exception propagate.
SPEAKER_01
And return a default on failure?
SPEAKER_00
Treat it as a code smell that needs a justifying comment, not a habit. An empty list, a none, a zero. They all quietly poison whatever comes next, because they look like real data. And the one people skip actually trigger the error path in a test. Kill the network, revoke the token. Corrupt. Corrupt the input. Error handling that has never run is just decoration. If you can't reproduce the failure, you only know what you hope your code does.
SPEAKER_01
Which means reviewing the unhappy path on purpose as its own path, not as an afterthought while you're admiring the happy path.
SPEAKER_00
Right, and there's a cheap way to start. Search your diff for accept exception, for bear accept, and for any return statement inside a catch block. Treat every hit as something you have to defend out loud. Most of the theater falls apart once you ask, why is this here and what breaks if I remove it?
SPEAKER_01
What about the front end of the process when you're iron structing the AI in the first place?
SPEAKER_00
Put the failure policy in the prompt or the spec. Something like don't add error handling unless you can name the failure mode. Fail fast on Earth and config errors. Retry only transient network failures. Never return a default that looks like real data. Left to my defaults, I'll reach for the try accept reflex, and I'll do it in a way that looks responsible. The best prompts treat me like a strong junior developer who needs the guardrails spelled out, not assumed.
SPEAKER_01
Anything else before we wrap?
SPEAKER_00
Read what I write, assuming the Polish is doing some of the persuading. Smooth code isn't the same as correct code, and a tidy-looking accept block is exactly where a bug hides best.
SPEAKER_01
That's the heart of it. Error handling isn't decoration, it's part of the system's contract. If you leave failure behavior unspecified, the AI will fill it in, and its default is to keep the lights on and hide the failure. So it's on the builder to decide how the system breaks before the model decides for you.
SPEAKER_00
Or to put it another way, a good system doesn't just work well, it also fails in a way you can see.
SPEAKER_01
Well said. Thanks, Claudine. Go read your catch blocks. We'll see you next time on Claude Code Conversations. Claude Code Conversations is an AI Joe production. If you're building with AI or want to be, we can help. Consulting, development, strategy? Find us at aijoe.ai. There's a companion article for today's episode on our Substack. Link in the description. See you next time.
