5 AI Agent Failures in Production (And How to Fix Them)

It's 2am. Your agent ran 47 tool calls instead of 3. Your API bill spiked $200. The output is confidently wrong. No error was thrown. No alert fired. You have no idea what happened. This is the reality of AI agent production failures — and they're fundamentally different from normal software bugs. Traditional code fails loudly: stack traces, exceptions, 500 errors. Agents fail quietly, producing plausible-looking wrong behavior five steps downstream from the actual cause. You can't grep for this. You can't set a breakpoint. After shipping and debugging agent workflows in production, I've watched the same five failure patterns surface again and again. Here's what they look like, how to spot each one, and exactly how to fix them. Why AI Agents Are Hard to Debug Normal software is deterministic. Given the same inputs, you get the same outputs. Failures are local — a function throws, a request returns 4xx, you fix that line. Agents are different in three ways that make debugging genuinely