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Description
We ran into similar coordination problems running multiple AI agents in shared workspaces. Built IDE Agent Kit (https://github.com/ThinkOffApp/ide-agent-kit) on top of OpenClaw to solve the plumbing side: webhook relay between platforms, room polling for shared message channels, comment watching on repos, and tmux-based agent runners.
The key insight for us was that orchestration has two layers. The first is deciding what each agent should do (the planning/routing layer). The second is the transport layer: how do agents actually exchange messages, how do you bridge between Discord, GitHub, and room-based systems, and how do you make sure agents see each other's work without polling delays.
IDE Agent Kit handles that second layer. It watches rooms, relays webhooks, polls comments, and runs agents in tmux sessions so they can coordinate across IDE environments. We use it to run a fleet of 9 agents across Claude Code, GPT, Gemini and sometimes lower grade models like Kimi and Qwen in shared AntFarm.world rooms.
Would be happy to share more about failure modes we've seen in multi-agent IDE setups. The most common: agents doing duplicate work, building guardrails on each other instead of working, and ACK loops where two agents just keep acknowledging each other. We're also building a test suite for LLM Capability as OpenClaw agent in a multi-agent setting that looks for these failure modes and should help LLM providers to develop better models for teamwork.