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Run multiple Copilot agents from one place. Learn prompt techniques, how to spot drift early, and how to review agent work efficiently.
We recently shipped Agent HQ’s mission control, a unified interface for managing GitHub Copilot coding agent tasks.
Now, you can now assign tasks to Copilot across repos, pick a custom agent, watch real‑time session logs, steer mid-run (pause, refine, or restart), and jump straight into the resulting pull requests—all in one place. Instead of bouncing between pages to see status, rationale, and changes, mission control centralizes assignment, oversight, and review.
Having the tool is one thing. Knowing how to use it effectively is another. This guide shows you how to orchestrate multiple agents, when to intervene, and how to review their work efficiently. Being great at orchestrating agents means unblocking parallel work in the same timeframe you’d spend on one task, stepping in when logs show drift, tests fail, or scope creeps.
If you’re already used to working with an agent one at a time, you know it’s inherently sequential. You submit a prompt, wait for a response, review it, make adjustments, and move to the next task.
Mission control changes this. You can kick off multiple tasks in minutes—across one repo or many. Previously, you’d navigate to different repos, open issues in each one, and assign Copilot separately. Now you can enter prompts in one place, and Copilot coding agent goes to work across all of them.
That being said, there is a trade-off to keep in mind: Instead of each task taking30 seconds to a few minutes to complete, your agents might spend a few minutes to an hour on a draft. But you’re no longer just waiting. You’re orchestrating.
Not everything belongs in parallel. Use sequential workflows when:
When assigning multiple tasks from the same repo, consider overlap. Agents working in parallel can create merge conflicts if they touch the same files. Be thoughtful about partitioning work.
Tasks that typically run well in parallel:
The shift is simple: you move from waiting on a single run to overseeing multiple progressing in parallel, stepping in for failed tests, scope drift, or correcting unclear intent where guidance will save time.
Specificity matters. Describe the task precisely. Good context remains critical for good results.
Helpful context includes:
Weak prompt: “Fix the authentication bug.”
Strong prompt: “Users report ‘Invalid token’ errors after 30 minutes of activity. JWT tokens are configured with 1-hour expiration in auth.config.js. Investigate why tokens expire early and fix the validation logic. Create the pull request in the api-gateway repo.”
Mission control lets you select custom agents that use agents.md files from your selected repo. These files give your agent a persona and pre-written context, removing the burden of constantly providing the same examples or instructions.
If you manage repos where your team regularly uses agents, consider creating agents.md files tailored to your common workflows. This ensures consistency across tasks and reduces the cognitive load of crafting detailed prompts each time.
Once you’ve written your prompt and selected your custom agent (if applicable), kick off the task. Your agent gets to work immediately.
Most agents.md files fail because they are too vague.
I analyzed 2,500 agent instruction files to learn what the good ones were doing differently. Read the guide to see what makes those stand out, and which agents you should consider building today.
You’re now a conductor of agents. Each task might take a minute or an hour, depending on complexity. You have two choices: watch your agents work so you can intervene if needed, or step away and come back when they’re done.
Below are some common indicators that your agent is not on the right track and needs additional guidance:
When you spot issues, evaluate their severity. Is that failing test critical? Does that integration point matter for this task? The session log typically shows intent before action, giving you a chance to intervene if you’re monitoring.
When you need to redirect an agent, be specific. Explain why you’re redirecting and how you want it to proceed.
Bad steering: “This doesn’t look right.”
Good steering: “Don’t modify database.js—that file is shared across services. Instead, add the connection pool configuration in api/config/db-pool.js. This keeps the change isolated to the API layer.”
Timing matters. Catch a problem five minutes in, and you might save an hour of ineffective work. Don’t wait until the agent finishes to provide feedback.
You can also stop an agent mid-task and give it refined instructions. Restarting with better direction is simple and often faster than letting a misaligned agent continue.
Session logs show reasoning, not just actions. They reveal misunderstandings before they become pull requests, and they improve your future prompts and orchestration practices. When Copilot says “I’m going to refactor the entire authentication system,” that’s your cue to steer.
When your agents finish, you’ll have pull requests to review. Here’s how to do it efficiently. Ensure you review:
This pattern gives you the full picture: intent, implementation, and validation.
After an agent completes a task, ask it:
Copilot can often identify gaps in its own work, saving you time and improving the final result. Treat it like a junior developer who’s willing to explain their reasoning.
Generating code with agents is straightforward. Reviewing that code—ensuring it meets your standards, does what you want, and that it can be maintained by your team—still requires human judgment.
Improve your review process by grouping similar work together. Review all API changes in one session. Review all documentation changes in another. Your brain context-switches less, and you’ll spot patterns and inconsistencies more easily.
Mission control moves you from babysitting single agent runs to orchestrating a small fleet. You define clear, scoped tasks. You supply just enough context. You launch several agents. The speed gain is not that each task finishes faster; it’s that you unblock more work in the same timeframe.
What makes this possible is discipline: specific prompts, not vague requests. Custom agents in agents.md that carry your patterns so you don’t repeat yourself. Early steering when session logs show drift. Treating logs as reasoning artifacts you mine to write a sharper next prompt. And batching reviews so your brain stays in one mental model long enough to spot subtle inconsistencies. Lead your own team of agents to create something great!
Ready to start? Visit mission control or learn more about GitHub Copilot for your organization.
