The AI Didn’t Break.
AI applications can fail even when the model works perfectly. Real production failures show why timeouts, monitoring, logging, performance, and ordinary software engineering still matter.
Production Did.
By Prateek Sharma, MCA
Congratulations. You built an AI application. It can still die because somebody forgot a timeout.
There is a point in every AI project where the impressive part starts working.
The model understands the user.
The agent calls the right tools.
The answers look good.
The demo works.
And then you put it in production.
Production does not care how impressive your AI is.
I learned that repeatedly.
The AI Was Fine
We had a search API with no timeout.
That call blocked our health check endpoint, so the platform decided the application was unhealthy and kept killing it.
The AI hadn't failed.
A perfectly ordinary network call had.
We fixed it by loading asynchronously with a 15-second ceiling.
Lesson learned: never let a third-party call sit in your startup or health path.
Then there was cost tracking.
We had it.
It worked.
It also reported that we were spending exactly zero dollars.
Excellent news, except we were definitely using paid models.
Our pricing lookup table didn't recognize versioned model identifiers.
The models were working. The accounting wasn't.
Then It Got Weirder
Our voice application dropped calls when the transcript came back as an empty string.
Not a bad transcript.
Not an incomprehensible transcript.
Nothing.
Apparently, silence was enough to take down the conversation.
Then a Slack bot crash-looped in production for hours.
The underlying bug was in a socket library, but that wasn't the interesting failure.
Nobody noticed because our process manager was running the development file-watcher. When the process crashed, it politely sat there instead of restarting.
Very considerate.
Not especially useful.
And Then There Were 17,000 Queries
On the mobile side, one screen was executing roughly 17,000 sequential database queries against a 21 MB API response.
Seventeen.
Thousand.
The fix wasn't AI.
It wasn't a better prompt.
It wasn't a more capable model.
It was one batch read in memory.
Ordinary performance engineering fixed the fancy AI application.
Production Is Still Production
AI changes some of the problems we solve.
It doesn't repeal the old ones.
You still need timeouts.
Retries.
Idempotency.
Logging.
Monitoring.
Fallback behavior.
Performance work.
And a real answer for what happens when a dependency goes down.
Because users don't care whether OpenAI failed, Anthropic failed, your search provider failed, or somebody left the dev file-watcher running in production.
They know one thing.
Your application doesn't work.
The AI may be the most interesting thing in the architecture.
Production still gets the last word.
Want to know more? See the White Paper here: https://blog.protovate.ai/we-stopped-paying-per-token-and-put-the-models-on-our-own-machine/
Originally published on Protovate.AI
Protovate builds practical AI-powered software for complex, real-world environments. Led by Brian Pollack and a global team with more than 30 years of experience, Protovate helps organizations innovate responsibly, improve efficiency, and turn emerging technology into solutions that deliver measurable impact.
Over the decades, the Protovate team has worked with organizations including NASA, Johnson & Johnson, Microsoft, Walmart, Covidien, Singtel, LG, Yahoo, and Lowe’s.
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