One of Mission Control’s most powerful capabilities is coordinating a fleet of agents working in parallel. Instead of waiting for one task to complete before starting the next, you can dispatch many tasks at once and let multiple agents make progress simultaneously.
AI agents are most effective on focused, well-scoped tasks. A large feature often involves many independent pieces: database migrations, API endpoints, frontend components, tests, and documentation. These pieces can frequently be worked on in parallel because they don’t depend on each other.
With a multi-agent workflow, you can break a large feature into five or six independent tasks, dispatch them all at once, and have a fleet of agents complete the work in the time it would have taken to do one piece sequentially.
For multi-agent workflows to succeed, tasks must be designed to be independent:
Fan-out / Fan-in — Dispatch many parallel tasks (fan-out), then dispatch a single integration task that depends on their completion (fan-in). This is the most common multi-agent pattern.
Pipeline — Tasks run sequentially, where each task’s output becomes the next task’s input. Use this when work has strict ordering requirements.
Specialized fleet — Assign different task types to different agent providers based on their strengths. Route analysis tasks to Gemini CLI, implementation tasks to Claude Code, and quick fixes to Codex CLI.
The Activity Dashboard handles multiple concurrent agents naturally. Events from all active agents appear in the stream, color-coded by agent. Filter by task or agent to focus on specific work, or watch the full stream for a high-level view of fleet activity.
The task list shows the status of all tasks at a glance, making it easy to see when parallel work is converging toward completion.