I run 49 scheduled AI agents on one laptop. Another 24 sit beside them, deliberately switched off.
That distinction matters more than it sounds. After eighteen months, I have stopped being surprised by model errors. Models misunderstand requests and occasionally produce confident nonsense. I expect that. I test for it.
What still catches me is how rarely those errors cause an outage. No serious failure in my fleet has started with a bad model answer. They start lower in the stack: a job never loaded, a shell command that existed only in my terminal, a credential expired between runs, a timeout guard built on a utility the operating system does not ship.
The agent did not go rogue. Unix happened.
Most agent-fleet guidance is written forward from a proposed architecture: a router, specialist agents, tools and memory, clean arrows between boxes. I have drawn those diagrams myself. But every design rule I actually use came backward from an incident, after the arrows stopped moving and the dashboard still looked green.
