Last month I was helping a friend debug their autonomous coding agent. It had been "working" on a task for 47 minutes, burned through roughly twelve bucks in API costs, and somehow ended up exactly where it started. The logs showed it had called read_file on the same five files 23 times.
If you've built or experimented with AI coding agents, you've probably seen something like this. It's not a fun bug to debug — the agent isn't crashing, it isn't erroring, it just... never finishes.
The Problem: Why Agents Loop Forever
Tool-use loops are the most expensive failure mode in agent design. From the outside, the agent looks busy. It's reading files, calling tools, generating thoughts, producing output. But it's not making progress toward the goal.
The shape is almost always the same:
