Overseeing multiple AI coding agents has never been simpler thanks to GitHub's Mission Control. This unified dashboard enables you to assign, monitor, and manage Copilot agent tasks across various repositories, all in one place. The result is that teams can save time, parallelize projects, and maintain oversight without navigating numerous pages or repos.
From Sequential to Parallel Workflows
Traditionally, agent workflows are sequential, you submit a prompt and wait for results before moving to the next task. Mission Control changes the game by allowing you to launch multiple Copilot agents concurrently, even across different repositories. This parallel approach boosts productivity, letting you oversee several agents at once and step in if any deviate from their objectives.
It's important to know when parallelization fits. Sequential workflows are preferable for dependent tasks, unfamiliar challenges, or when stepwise validation is essential. In parallel scenarios, be alert for merge conflicts, especially when agents touch overlapping files.
- Ideal for parallelization: research, log analysis, performance profiling, documentation updates, security reviews, and isolated component changes.
- Best kept sequential: when tasks depend on one another or require validation between steps.
Mastering Prompts and Custom Agents
Effective communication drives agent success. The more relevant context you provide, like code snippets, screenshots, and documentation links, the better the outcomes. We call this "context engineering". For recurring workflows, use custom agents by adding agents.md files to your repositories. These files give Copilot a consistent persona and instructions, eliminating repetitive setup.
Poor prompt: “Fix the authentication bug.”
Effective prompt: “Investigate ‘Invalid token’ errors after 30 minutes of activity. JWT tokens expire in one hour. Fix early expiration and create a pull request in the api-gateway repo.”
Custom agents help ensure consistency, reduce cognitive load, and streamline repeated tasks for your team.
Active Oversight: Monitoring and Guiding Agents
Orchestration involves more than just delegating tasks. It’s crucial to monitor real-time session logs for signs of trouble, like failing tests, unexpected file changes, or misunderstood instructions. These indicators help you intervene early and redirect agents as needed.
- Failing tests or integrations may indicate misunderstandings or environment issues.
- Unexpected file changes and edits to critical config files require scrutiny.
- Scope creep happens when agents stray from your original intent.
- Session logs provide insight into agent reasoning before changes are made, supporting proactive corrections.
When intervening, be clear and specific about what went wrong and how to adjust. Early course corrections save time and keep projects on track.
Efficiently Reviewing Agent Output
Reviewing agent output efficiently is vital. Start with session logs to understand the agent’s reasoning, then examine code diffs for unexpected or risky changes. Always verify that automated tests pass and investigate failures to distinguish between misunderstandings and actual bugs.
- Use Copilot to ask targeted questions, such as “What edge cases are missing?” or “Which tests lack coverage?”
- Batch similar reviews to minimize context-switching and identify inconsistencies more easily.
Takeaway: Lead Your AI Agent Fleet
Mission Control transforms fragmented agent management into a coordinated, productive operation. By orchestrating multiple agents with clear prompts, custom personas, and active oversight, you unlock greater throughput without sacrificing code quality. Proactive monitoring and systematic reviews ensure your AI agents deliver meaningful results quickly and consistently.
Source: The GitHub Blog – How to orchestrate agents using mission control
Unlocking Efficiency: Orchestrate AI Agents with Mission Control for GitHub Copilot