This update reduces context switching by integrating diverse AI models directly into the developer workflow. It allows engineers to leverage the unique reasoning strengths of different agents for complex tasks like architectural reviews and edge-case detection within GitHub and VS Code.
Context switching equals friction in software development. Today, we’re removing some of that friction with the latest updates to Agent HQ which lets you run coding agents from multiple providers directly inside GitHub and your editor, keeping context, history, and review attached to your work.
Copilot Pro+ and Copilot Enterprise users can now run multiple coding agents directly inside GitHub, GitHub Mobile, and Visual Studio Code (with Copilot CLI support coming soon). That means you can use agents like GitHub Copilot, Claude by Anthropic, and OpenAI Codex (both in public preview) today.
With Codex, Claude, and Copilot in Agent HQ, you can move from idea to implementation using different agents for different steps without switching tools or losing context.
We’re bringing Claude into GitHub to meet developers where they are. With Agent HQ, Claude can commit code and comment on pull requests, enabling teams to iterate and ship faster and with more confidence. Our goal is to give developers the reasoning power they need, right where they need it.
Katelyn Lesse, Head of Platform, Anthropic
Agent HQ also lets you compare how different agents approach the same problem, too. You can assign multiple agents to a task, and see how Copilot, Claude, and Codex reason about tradeoffs and arrive at different solutions.
In practice, this helps you surface issues earlier by using agents for different kinds of review:
This method of working moves your reviews and thinking to strategy over syntax.
Our collaboration with GitHub has always pushed the frontier of how developers build software. The first Codex model helped power Copilot and inspired a new generation of AI-assisted coding. We share GitHub’s vision of meeting developers wherever they work, and we’re excited to bring Codex to GitHub and VS Code. Codex helps engineers work faster and with greater confidence—and with this integration, millions more developers can now use it directly in their primary workspace, extending the power of Codex everywhere code gets written.
Alexander Embiricos, OpenAI
GitHub is already where code lives, collaboration happens, and decisions are reviewed, governed, and shipped.
Making coding agents native to that workflow, rather than external tools, makes them even more useful at scale. Instead of copying and pasting context between tools, documents, and threads, all discussion and proposed changes stay attached to the repository itself.
With Copilot, Claude, and Codex working directly in GitHub and VS Code, you can:
There are no new dashboards to learn, and no separate AI workflows to manage. Everything runs inside the environments you already use.
These workflows don’t just benefit individual developers. Agent HQ gives you org-wide visibility and systematic control over how AI interacts with your codebase:
This allows teams to adopt agent-based workflows without sacrificing code quality, accountability, or trust.
Access to Claude and Codex will soon expand to more Copilot subscription types. In the meantime, we’re actively working with partners, including Google, Cognition, and xAI to bring more specialized agents into GitHub, VS Code, and Copilot CLI workflows.
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Read full articleExperienced engineers demonstrate that AI tools are most effective when integrated with established practices like TDD and custom personas. This shift toward "agentic engineering" allows developers to focus on high-level design and architectural integrity while maintaining code quality.
This workflow bridges the gap between AI-driven code generation and manual engineering, allowing developers to leverage AI for bulk tasks while maintaining full control over local testing, debugging, and final code quality within their primary IDE.
This shift to agentic workflows and ambient intelligence redefines the developer experience. It moves the focus from syntax and manual tasks to high-level intent and judgment, requiring engineers to master context management and AI steering to maintain productivity and code quality.
Providing AI with codebase context reduces repetitive prompting and ensures generated code follows team standards. By using custom instructions and MCP servers, engineers can automate complex workflows and integrate external tools directly into their AI-assisted development cycle.
