Demos lie. An AI agent that books a meeting, queries an API, and summarizes the result in a slick demo is maybe 20% of the work. The other 80% is everything that happens when the same agent meets a real user, real data, and a Tuesday afternoon when an upstream API is having a bad day.
We build multi-agent systems for companies for a living, and the gap between "works in the notebook" and "works in production" is where most AI projects quietly die. Here are the failure modes we see most often — and what we actually do about them.
1. The agent is confidently wrong, and nothing catches it
A single LLM call has no idea when it's hallucinating. Chain three of them together and the errors compound: agent A invents a customer ID, agent B dutifully looks it up, agent C writes a confident summary about a customer who doesn't exist.
The fix isn't a better prompt. It's treating every agent output as untrusted input — the same discipline you'd apply to a form field from the public internet. Validate structured outputs against a schema. Make tools return typed results, not prose. And put a deterministic check between "the model decided X" and "X happened in your database."
