Here’s the thing about building with the latest AI models like GPT-5: you’re basically handing the keys to a super-smart, super-eager intern. It’s brilliant, it’s fast, & it can do some incredible things with the tools you give it. But if you don’t give it crystal-clear instructions & some guardrails, that intern might just keep trying the same thing over & over, racking up a bill that’ll make your eyes water.

We’re talking about infinite loops in tool-calling agents. It’s one of the scariest, most frustrating problems developers face when moving from simple prompts to building real, autonomous agents. You give your GPT-5 powered agent a task, it decides to use a tool (like a search API or a database lookup), gets a result it doesn’t quite understand, & tries again. And again. And again.

Before you know it, you’ve burned through your budget, your application is stuck, & your users are staring at a loading spinner. I’ve been there, & it’s not fun. So let’s talk about how this actually happens & what you can do to stop it. This isn't just theory; these are the practical, in-the-trenches strategies you need to know.

You'd think something so potentially destructive would be complex. It's not. The core logic of an AI agent is often shockingly simple, & that’s where the danger lies. It can be boiled down to a basic loop that looks something like this in pseudocode: