You deploy an AI feature to production, everything looks fine in testing, and three days later you get a surprise API bill that makes you question your life choices. I've seen it happen. More than once. And the worst part is it's almost never a big obvious bug. It's a quiet failure mode that compounds silently.
Here's the pattern that causes it, how to spot it before it hits your wallet, and the guardrails I now put on every AI pipeline I build.
The Bug: Runaway Retries in an AI Agent Loop
Suppose you build an AI-powered job description enrichment pipeline. The flow is simple: take a raw job listing from an ATS, send it to GPT-4 with a structured prompt, and get back a cleaned, keyword-optimized description.
The problem hides in the error handling. A naive retry pattern looks like this:
