A developer has built Mission Control, an open-source task management system specifically designed for coordinating multiple AI agents like Claude Code, Cursor, and Windsurf. The tool addresses a common pain point where users struggle to manage scattered tasks across different platforms while agents work independently without visibility or coordination. The system provides a centralized dashboard where tasks can be prioritized using an Eisenhower matrix, assigned to specific agent roles, and tracked through completion.

What makes this different from tools like Linear or Asana is its agent-first architecture. Mission Control stores all data locally in JSON files with no cloud dependency, includes a token-optimized API that reduces payload size by 92% (from ~5,400 to ~50 tokens), and features an autonomous daemon that runs in the background to automatically poll tasks and spawn agent sessions. The system includes 193 automated tests and uses Zod validation with async-mutex locking for rock-solid concurrency. Users can define reusable skills as markdown modules that get injected into agent prompts, and the platform supports five built-in agent roles including researcher, developer, and marketer.

The tool is MIT licensed and actively maintained, with future plans for human collaboration features, GitHub integrations, and custom agent templates. For anyone delegating serious work to AI agents, this provides the missing infrastructure for turning chaotic agent workflows into structured, trackable processes.