Originally published on hexisteme notes, part of a series on building and running an AI agent fleet.
To coordinate a fleet of independent AI agent CLIs without a message broker or a heavy framework, use a filesystem work-bus: the orchestrator decomposes a goal into a graph of subtasks, writes a Task file per subtask, and polls for the Result file each worker writes back — every file written atomically. The durable coordination state lives on disk as files: language-agnostic, debuggable with ls, surviving restarts, and self-healing because an absent worker is skipped and logged instead of failing the run.
Say you have several AI agents, each an independent installed CLI — one gathers information, one writes copy, one builds an app scaffold — and you want to run a goal that needs several of them in sequence. The heavyweight answers are an in-process framework (LangGraph, an AutoGPT-style loop) or a message broker (Redis, Kafka, RabbitMQ). Both are more than a single-operator fleet needs: a framework couples your workers into one process and one language, and a broker is infrastructure you now have to run, secure, and monitor.
There's a lighter primitive that fits this shape: a work-bus made of files.
