TL;DR: I run multiple AI agents in parallel — one researches, one implements, one reviews. The agents themselves are rarely the problem. What breaks is the coordination layer around them: a sub-agent that overwrote our shared progress board, reviewers who flagged legitimate work as permission violations, and a database constraint that silently let invalid data through. Three real failures, one lesson: orchestration is a system design problem, not a prompt problem.

The setup

I don't have "an AI agent." I have a team of them, and I'm the orchestrator.

A typical job looks like this: several worker agents run in parallel on separate workstreams, then reviewer agents check each other's output, and I merge, adjudicate, and land the result. It works well — until it doesn't. And when it fails, it almost never fails the way you'd expect. The models don't hallucinate the system down. The wiring does.

Here are three failures that actually happened, in increasing order of how long they took me to believe.

1.…