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Why markdown task plans fail when multiple AI coding agents run in parallel

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Developers often coordinate AI coding agents with a markdown checklist in the repository, which works for a single agent but breaks down as sessions multiply. Agents can pick the same item simultaneously, stalled tasks stay marked in progress indefinitely, and concurrent edits to the plan can collide. The author argues these are classic shared-mutable-state problems that a file is poorly suited to solve. The proposed fix, workstream-mcp, is an MCP server backed by Postgres that uses atomic claims with expiry, dependency-aware scheduling, enforced state transitions, review steps, and an append-only event log. A dashboard and an outbox-based notification system round out the design.

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