What I learned building a control plane for coding agents (Claude Code, Codex, Pi, Kimi) running on multiple machines

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Summary

作者分享为运行在多台机器上的多个编码代理(Claude Code、Codex、Pi、Kimi)构建控制平面的经验教训:控制平面只排队不执行、审批必须由人类完成、区分心跳与轮询、事件溯源作为唯一事实来源,并将代理视为有角色和技能的命名队友。

Once I ran more than one coding agent, I lost track of who was doing what, on which machine, and what they had changed. So I built a small control plane for them. A few lessons that might help if you're building something similar: The control plane should never run agents. Mine only queues work. Daemons on each machine poll for it and run the agent CLIs, either on the host or in a sandbox. That kept the trust boundary simple. Approvals must come from a person, never from anything the agent can reach. When a runtime asks for permission to run a command or edit a file, the run pauses until a signed-in human answers. "Alive" is not "listening". A daemon can keep sending heartbeats while its command poll is stuck. I track both and only dispatch to machines that are actually polling. Use one source of truth. Sessions and tasks are event-sourced and everything else is derived, which made handoffs, retries and "what happened here?" much easier. Treat agents as named teammates with a role, runtime and skills, not anonymous CLI calls. Assigning work gets much easier. It supports threads, tasks, scheduled automations, and teams such as one agent fixing a bug and another reviewing it. How are you tracking and approving what your agents do across machines? I'll put the repo in the first comment.
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@vintcessun: It turns out that having multiple AI agents work together as a team is better than a single general-purpose agent in this way: each role is bound to its best model, memory and skills accumulate across conversations. Instead of taking turns, a task is handed off with a brief handover note. Runs locally, all file states are in ~/.crew44, free MIT license.

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Crew44 is a local-first orchestrator that turns coding agents like Claude Code and Codex into a coordinated team of specialists, each bound to its best model, with persistent memory and skill accumulation across sessions. It runs entirely on your machine with no cloud dependence and is free under MIT license.