How I Got Multiple AI Coding Agents to Stop Losing Track of Their Work

I recently wrote about agent-comm — a communication layer that lets multiple AI coding agents talk to each other. But talking is only half the problem. The other half? Knowing what work needs to be done, who's doing it, and what stage it's in. The problem When you run multiple AI agents in parallel — say three Claude Code sessions working on different parts of a feature — things fall apart fast: No shared backlog. Each agent only knows what you told it in its prompt. There's no central place to see all pending work. No pipeline visibility. Is the spec done? Has anyone started implementing? Did tests pass? Nobody knows. No dependency tracking. Agent B can't start until Agent A finishes, but there's nothing enforcing that. No artifacts. Specs, plans, test results — they live in chat context and vanish when the session ends. I needed something that gives AI agents the same project management primitives that human teams take for granted — but exposed as MCP tools they can call directly. Th