GitHub Agent HQ: Your Multi-Model AI Agents Are Finally Native to the Workflow

TL;DR GitHub just announced Agent HQ, transforming GitHub into an open ecosystem that unites every agent on a single platform. Coding agents from Anthropic, OpenAI, Google, Cognition, xAI, and more will become available directly within GitHub as part of your paid GitHub Copilot subscription. For .NET and Azure developers, this means agents are no longer bolted-on tools—they’re woven into your existing pull request, issue, and CI/CD workflows. No new platforms. No context-switching tax.

The Problem: Fragmented Agent Chaos

Let’s be honest. The AI agent landscape of 2025 feels like a Kubernetes cluster where nobody agreed on the networking layer. The current AI landscape presents a challenge: incredible power fragmented across different tools and interfaces. You’ve got Claude agents, OpenAI’s operators, Google’s Gemini agents, and a dozen startups all claiming to automate your workflow. But they live in isolation. You assign a task to one agent in one tab, get a result in another system, then manually ferry it back to your GitHub repo. That’s not automation—that’s a relay race. Agents shouldn’t be bolted on. They should work the way you already work. That’s why GitHub is making agents native to the GitHub flow. —

What Agent HQ Actually Does (For You, Right Now)

1. Mission Control: One Command Center for All Agents

A mission control serves as a single command center to assign, steer, and track the work of multiple agents from anywhere. Mission control is in VS Code, too, so you’ve got a single view of all your agents running in VS Code, in the Copilot CLI, or on GitHub. What this means for .NET devs:
Stop juggling tabs. Assign a bug fix to Claude, a refactoring to OpenAI’s o1, and a test-coverage improvement to Gemini—all from one dashboard in VS Code or GitHub.com. Watch progress in real-time. No API keys scattered across four different SaaS platforms.

2. Code Review That Actually Understands Your Stack

Copilot code review now blends LLM detections and tool calling with deterministic tools like ESLint and CodeQL, delivering smarter reviews and a seamless handoff to the Copilot coding agent for fixes. The agent doesn’t just flag a security issue—it proposes a fix and hands it off to another agent to implement it. You can now hand off suggested changes directly to the Copilot coding agent. Mention @copilot in your pull request, and CCR will automatically apply the suggested fixes in a stacked pull request, ready for you to review and merge.

3. Enterprise Governance (Finally)

A new enterprise-level experience focused on administrative functions for AI systems is available to all GitHub Enterprise Cloud customers using Copilot. This is your agent control plane, a suite of enterprise governance features designed to give organizations deeper control over how agents operate across their environments. Set enterprise-wide MCP allowlist via MCP registry URL to govern MCP connections in VS Code Insiders. Translation: your security team can now define which AI models, which agents, and which external tools your developers can use—without micromanaging every developer’s local setup.

The Ecosystem Shift: Why This Matters Now

More than 1.1 million public repositories now use an LLM SDK with 693,867 of these projects created in just the past 12 months alone (+178% YoY, Aug ‘25 vs. Aug ‘24). Developers are already embedding AI into their codebases. Agent HQ is GitHub’s bet that the orchestration layer should live where the code lives—on GitHub itself. 80% of new developers on GitHub use Copilot in their first week. That’s not hype; that’s a signal that AI assistance is no longer optional. But it’s fragmented. Agent HQ consolidates it.

Practical Integration: Getting Started

If you’re shipping .NET on Azure and GitHub:

Enable Copilot Enterprise or Pro+ on your account.
Open VS Code (or GitHub.com) and look for the new “Agents” tab in the Copilot sidebar.
Select your preferred model (Claude, GPT-4, Gemini, etc.) for each task type.
Assign a GitHub issue to the agent. It will:
- Analyze the issue
- Create a branch
- Write code (respecting your .NET conventions)
- Run your test suite
- Open a PR
- Request your review

# Example: Assign a Linear issue to Copilot coding agent
# (Linear integration is in public preview as of Oct 28, 2025)
# Just link the issue in Linear and mention @copilot-agent

For Azure deployments, Model router in Azure AI Foundry allows developers to automatically select the optimal Azure OpenAI models for different prompts—boosting quality while reducing costs. Azure AI Foundry Agent Service is now generally available, helping more companies like JM Family, Fujitsu, and YoungWilliams automate some of the most complex business processes. —

The Real Win: Less Glue Code, More Shipping

As an organization, you need to know how Copilot is being used. The Copilot metrics dashboard shows Copilot’s impact and critical usage metrics across your entire organization. For enterprise administrators managing AI access, including AI agents and MCP, the control plane provides your agent governance layer. Set security policies, audit logging, and manage access all in one place. The bottom line: You’re no longer gluing agents to GitHub via webhooks and custom scripts. Agents are first-class citizens in the GitHub flow. Your .NET team can focus on business logic instead of orchestration plumbing.

Watch Out For

- Selected Claude, OpenAI, and Gemini Copilot models were deprecated on October 23, 2025. Copilot Enterprise administrators may need to enable access to alternative models through their model policies in Copilot settings. Check your model roster if you’re on Enterprise. - MCP-style connectors expand the attack surface and raise exfiltration risk when agents move freely between tools. Researchers have already demonstrated connector exploits, while OpenAI’s guidance stresses least privilege, explicit consent, and defense in depth. Audit your agent permissions.