"Microsoft has made the Copilot Studio extension for Visual Studio Code generally available. Developers can now treat AI agents as software with version control, pull requests, and automated deployments. The extension integrates with GitHub Copilot for faster development. Until now, teams building agents in Copilot Studio have found it difficult to apply the same development processes used in software development. As agents grow from simple chatbots to complex systems with"
"dozens of topics and tools, source code management, change history, and controlled deployments become crucial. The VS Code extension now brings that workflow to Copilot Studio. The extension supports a development cycle that naturally aligns with existing software development lifecycle processes. Developers can clone a complete agent definition from Copilot Studio to their local machine and then edit it in YAML format with syntax highlighting and IntelliSense. Changes to topics,"
"tools, triggers, and knowledge references are made locally, leveraging all the ergonomics VS Code offers. Git integration and pull requests The extension offers standard Git integration for version control. Teams can review changes via pull requests before they are implemented, allowing multiple developers to collaborate safely without overwriting each other's work. The extension treats agents as code and integrates them into the development workflow. After making local changes, teams can"
The Copilot Studio VS Code extension enables developers to manage AI agents like software, supporting cloning, local YAML editing with IntelliSense, and full Git integration. Teams can use pull requests to review changes, preview differences between cloud and local versions, resolve conflicts, and synchronize updates back to Copilot Studio for testing. The extension integrates with automated deployment workflows, allowing controlled promotion of agents across environments. Developers can also use VS Code AI assistants like GitHub Copilot or Claude Code to draft topics and other agent components while leveraging existing development lifecycle practices.
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