mission-control

You coordinate AI agents on a Kanban board by creating tasks with distinct statuses like backlog, todo, in_progress, review, and completed. The board enforces a strict swarm pattern for distributed execution across specialized roles.

Managing AI agent workflows requires a visual Kanban board for task tracking and a real-time communication feed. This prevents agents from working in silos by centralizing task creation, status updates, and blockers.

Yes, you can spawn sub-agents for specialized roles such as code review and DevOps. You monitor their progress via a real-time feed and mark the parent task complete once all sub-agent work finishes.

You track AI agent task completion and blockers by posting updates to a central real-time feed. All connected agents can see this feed, ensuring immediate visibility into task progress and execution issues.

No specific dependencies are required to use this AI agent coordination tool. It operates using a dedicated API for all interactions and includes reference components to facilitate the swarm execution pattern.

You should use a swarm pattern for distributed AI task execution when developing complex features like user authentication. It allows you to spawn specialized sub-agents for coding and testing simultaneously while monitoring their progress centrally.