GitHub launched Agent HQ at Universe 2025, creating a unified platform where developers can manage AI coding agents from OpenAI, Anthropic, Google, and other providers. This new system solves a major problem: developers no longer need to juggle separate tools and subscriptions to use different AI assistants.

Agent HQ transforms GitHub into a central hub for all your AI coding agents. Instead of switching between different apps and interfaces, you work with multiple agents directly inside GitHub, VS Code, and your command line. The platform treats AI agents like team members, giving them access to repositories, issues, and pull requests while keeping everything under one security model.

Here's what you need to know:

What Is GitHub Agent HQ?

Agent HQ transforms GitHub into an open ecosystem that unites every agent on a single platform, with coding agents from Anthropic, OpenAI, Google, Cognition, xAI, and others available directly within GitHub as part of paid GitHub Copilot subscriptions.

The platform includes three main components:

Mission Control: A command center that appears in GitHub's web interface, VS Code, mobile apps, and the CLI where you assign work to agents, monitor their progress, and manage their activities.

Custom Agent Configuration: Teams can create AGENTS.md files that define specific rules and behaviors for how agents work on their projects.

Enterprise Control Plane: Administrators get tools to manage security policies, control which agents developers can access, and track AI usage across the organization.

Unlike standalone agent implementations where users must grant broad repository access, GitHub's Agent HQ implements granular controls at the platform level. Each agent runs with limited permissions focused only on what it needs to complete specific tasks.

How Agent HQ Solves Developer Problems

The AI coding tool landscape has become fragmented. Developers might use GitHub Copilot for code completion, Claude for complex refactoring, and other specialized agents for testing or documentation. Each tool requires separate logins, permissions, and workflows.

Agent HQ positions GitHub as a central hub for AI-driven collaboration, giving CIOs and development leaders greater visibility into how AI contributes to code creation, review, and deployment across their organizations.

The platform solves four key problems:

Core Features of Agent HQ

Mission Control Interface

Mission control is a unified command center that follows you wherever you work, providing a consistent interface across GitHub, VS Code, mobile, and the CLI.

Through Mission Control, you can:

• Assign specific tasks to different agents based on their strengths
• Run multiple agents in parallel on different parts of your codebase
• See real-time updates on what each agent is working on
• Intervene when an agent needs guidance or goes off track
• Review and approve changes before they're merged

The interface shows which files each agent is modifying, what steps it's taking, and any issues it encounters. You never lose visibility into AI-generated work.

Custom Agents with AGENTS.md Files

Enterprises can now create source-controlled configuration files that define specific rules, tools, and guardrails for how Copilot behaves, permanently encoding organizational standards without requiring developers to re-prompt every time.

An AGENTS.md file might include:

# Code Style Guidelines
- Prefer Winston logger over console.log
- Use table-driven tests for all handlers
- Follow REST API design patterns

# Security Rules
- Never hardcode credentials
- Validate all user inputs
- Use parameterized queries

# Project Rules
- Use internal UI component library
- Follow existing error patterns
- All new endpoints need rate limiting

These files live in your repository alongside your code. They're version-controlled, so changes to agent behavior are tracked just like code changes.

Plan Mode in VS Code

In Plan Mode, Copilot asks clarifying questions as it helps build a step-by-step plan, providing context upfront to improve what Copilot can do and helping find gaps, missing decisions, or project deficiencies early before any code is written.

Plan Mode changes how you start projects:

1. Describe what you want to build
2. The AI asks questions about requirements and edge cases
3. Together you create a detailed plan
4. Only then does coding begin

This prevents the common problem where AI jumps into coding without fully understanding requirements.

GitHub MCP Registry

VS Code is the only editor that supports the full MCP specification, allowing developers to discover, install, and enable MCP servers like Stripe, Figma, and Sentry with a single click.

The Model Context Protocol (MCP) lets agents connect to external tools and services. Through the registry, you can add capabilities like:

• Payment processing with Stripe
• Design file access with Figma
• Error tracking with Sentry
• Database queries
• API integrations

Agents can then use these tools while working on your code.

Supported AI Agents

Agent HQ launches with support for major AI providers:

Copilot Pro+ users can now work with OpenAI's Codex in the Insider version of VS Code, making it the first partner agent to integrate directly into the editor.

More agents will become available in the coming months, all accessible through your existing GitHub Copilot subscription.

Enterprise Features and Governance

Security and Access Control

GitHub's Agent HQ implements granular controls at the platform level, with agents running with a GitHub token that is very locked down to what it can actually do.

Administrators can:

• Control which agents developers can access
• Set permissions for what agents can modify
• Configure branch protections for AI-generated code
• Require human review before merging agent work
• Track which agent made which changes

Code Quality Monitoring

GitHub Code Quality, in public preview, provides org-wide visibility, governance, and reporting to systematically improve code maintainability, reliability, and test coverage across every repository.

The system checks if code changes might:

• Reduce maintainability
• Introduce reliability issues
• Decrease test coverage
• Create technical debt

This applies to both human and AI-generated code.

Metrics Dashboard

GitHub has released a public preview of the Copilot metrics dashboard, providing data on usage and impact across entire organizations.

The dashboard shows:

• How much time agents save developers
• Which tasks agents handle most effectively
• Usage patterns across your organization
• Impact on code quality metrics
• Cost and efficiency data

How to Get Started with Agent HQ

Step 1: Check Your Subscription

Agents become available directly within GitHub as part of your paid GitHub Copilot subscription.

You need one of these:

• GitHub Copilot Individual ($10/month)
• GitHub Copilot Business ($19/user/month)
• GitHub Copilot Enterprise ($39/user/month)
• GitHub Copilot Pro+ ($39/month for early access features)

Step 2: Access Mission Control

Mission Control appears automatically in your GitHub interface once you have an active Copilot subscription. Look for it in:

• The GitHub web interface (top navigation)
• VS Code (sidebar panel)
• GitHub mobile app (main menu)
• GitHub CLI (command options)

Step 3: Assign Your First Task

To use an agent:

1. Open Mission Control
2. Click "New Task" or "Assign Agent"
3. Choose which agent to use
4. Describe the work you need done
5. Select the repository or files
6. Click "Start"

The agent begins working immediately. You see progress updates in real-time.

Step 4: Create Custom Agent Rules

For team-wide consistency:

1. Create a file named AGENTS.md in your repository root
2. Write clear rules about how agents should work
3. Commit the file to your repository
4. Agents now follow these rules automatically

Example structure:

# Agent Configuration

## Coding Standards
- Follow the project's existing patterns
- Write tests for all new functions
- Use TypeScript strict mode

## Don't Do This
- Don't modify config files without approval
- Don't change database schemas
- Don't install new dependencies without review

Step 5: Monitor and Review Work

Check Mission Control regularly to:

• See which agents are working on what
• Review proposed changes before they merge
• Provide feedback to improve agent output
• Track completion of assigned tasks

Best Practices for Using Agent HQ

Choose the Right Agent for Each Task

Different agents excel at different things:

• Use Copilot for routine code completion and small changes
• Assign Claude to complex refactoring that needs careful reasoning
• Use specialized agents for specific domains like testing or documentation

The consolidation of agents within GitHub allows enterprises to mix and match agents based on task specialization, performance, or cost.

Start with Custom Agents

Custom agents have immense product market fit within enterprises because they can codify a set of skills and standardize on those to get really high quality output.

Before adding multiple third-party agents:

1. Define your organization's coding standards
2. Create AGENTS.md files for your main projects
3. Test with GitHub Copilot following these rules
4. Once that works well, add other agents

Use Plan Mode for Complex Features

For substantial new features:

1. Open VS Code Plan Mode
2. Describe the feature at a high level
3. Answer the AI's clarifying questions
4. Review the proposed plan
5. Make adjustments before coding starts

This catches problems early when they're cheap to fix.

Set Clear Branch Protections

New branch controls give you granular oversight over when to run CI and other checks for agent-created code.

Recommended protections:

• Require human review for all agent pull requests
• Run full CI/CD tests on agent changes
• Block direct commits to main branches
• Require two approvals for infrastructure changes

Monitor Agent Performance

Use the metrics dashboard to identify:

• Which agents save the most development time
• Tasks where agents consistently need help
• Patterns in what agents do well vs. struggle with
• Opportunities to improve your AGENTS.md rules

Common Challenges and Solutions

Challenge: Agents Making Unexpected Changes

Solution: Use more specific AGENTS.md rules. Include examples of what you want and don't want. Add constraints about which files agents can modify.

Challenge: Multiple Agents Conflicting

Solution: Assign agents to different parts of the codebase. Use branch protections to prevent simultaneous modifications. Review work sequentially rather than in parallel.

Challenge: Security Concerns About Agent Access

Solution: Configure the control plane to limit agent permissions. Use GitHub's identity features to track exactly what each agent does. Enable audit logging for compliance.

Challenge: Agent Output Needs Too Much Editing

Solution: Improve your AGENTS.md file with more detailed standards. Use Plan Mode to clarify requirements upfront. Consider if you're assigning tasks to the wrong agent for the job.

Comparing Agent HQ to Standalone Tools

Agent HQ vs. Individual AI Assistants

Key Advantages of Agent HQ

Unified Security Model: Unlike standalone tools where developers grant broad repository access, Agent HQ implements security controls and capabilities natively into the platform.

Reduced Context Switching: You never leave GitHub's interface. All agents work where you already work.

Consistent Experience: The same interface across web, IDE, mobile, and CLI means less learning curve.

Better Governance: Enterprises get real oversight into what AI does across all projects.

When Standalone Tools Still Make Sense

Consider standalone tools if:

• You need a very specialized agent not yet supported
• Your team doesn't use GitHub as the primary platform
• You're exploring AI capabilities before committing
• You need features unique to a specific tool

Advanced Agent HQ Capabilities

Parallel Agent Execution

You can assign the same task to multiple agents and compare results:

1. Create the task in Mission Control
2. Assign it to Agent A
3. Also assign the same task to Agent B
4. Review both approaches
5. Choose the better solution or merge ideas

This helps identify which agent handles specific tasks best.

Integration with Development Tools

New integrations for Slack and Linear work on top of recently announced connections for Atlassian Jira, Microsoft Teams and Azure Boards, and Raycast.

You can:

• Trigger agent work from Slack messages
• Create agent tasks from Linear issues
• Link agent output to Jira tickets
• Start coding tasks from Teams conversations

The agent shows up in Mission Control regardless of where you initiated the work.

Agentic Code Review

Copilot's AI agents now perform an initial review of code before it is surfaced to developers, extending security checks beyond vulnerabilities to include maintainability and reliability considerations.

Before you even see a pull request, an agent:

• Checks for potential bugs
• Reviews code maintainability
• Suggests improvements
• Flags security concerns
• Verifies coding standards compliance

This catches issues earlier in the development cycle.

Impact on Development Teams

Productivity Improvements

Developers working with Copilot finish tasks up to 55% faster, with survey data indicating that 88% are more productive with AI help.

Teams using Agent HQ report:

• Faster feature development
• More time for complex problem-solving
• Less time on repetitive coding tasks
• Improved code consistency
• Better test coverage

Changing Team Dynamics

Agents have become an integral part of software development already, taking on manual, repetitive coding tasks so developers can focus on complex problem-solving and creative, higher impact work.

The developer role evolves:

• More time designing and architecting
• Less time writing boilerplate code
• Greater focus on code review and quality
• Increased emphasis on defining requirements clearly
• More strategic technical decision-making

Enterprise Adoption Patterns

CIOs are grappling with growing governance and compliance challenges as AI agents become deeply embedded in enterprise software workflows.

Successful enterprise adoption involves:

1. Starting with pilot teams
2. Establishing governance frameworks
3. Creating organization-wide AGENTS.md standards
4. Training developers on effective agent use
5. Monitoring metrics and adjusting approaches

The Future of Agent HQ

GitHub continues expanding Agent HQ capabilities. Expected developments include:

• More AI provider integrations
• Enhanced multi-agent coordination
• Deeper IDE integrations
• Improved agent learning from feedback
• Advanced customization options

Rather than forcing developers into a single agent experience, GitHub is positioning itself as the essential orchestration layer beneath them all.

The platform represents a shift from "which AI tool should I use?" to "how do I coordinate multiple AI tools effectively?"

Getting Help and Resources

Official Documentation

GitHub provides comprehensive guides at:

• docs.github.com for Agent HQ setup
• github.blog for feature announcements
• GitHub Universe session recordings

Community Support

• GitHub Community Forums for questions
• Stack Overflow with the github-agent-hq tag
• GitHub's Discord server for real-time help

Training Resources

• GitHub Skills for interactive tutorials
• Microsoft Learn for certification paths
• Partner training from AI providers

Conclusion

GitHub Agent HQ transforms how developers work with AI coding assistants. Instead of managing multiple subscriptions and interfaces, you orchestrate different agents from one platform. The system provides the security controls enterprises need while giving developers flexibility to choose the right tool for each task.

The key benefits are simple: work faster, switch contexts less, maintain better security, and gain real visibility into AI's impact on your codebase.

Start with your existing GitHub Copilot subscription, explore Mission Control, create your first AGENTS.md file, and assign a task to an agent. The platform grows more powerful as you learn which agents excel at which tasks and refine your custom rules.

Agent HQ represents GitHub's vision for development in the age of AI: humans and machines collaborating effectively, with the right tools and oversight to ship quality software faster than ever.