GitHub Copilot Agent Mode

Mike Romero

GitHub Copilot Agent Mode

Decided to write this in conjunction with my Hugo site build to explore the capabilities of GitHub Copilot Agent Mode. This feature allows developers to leverage AI agents to assist in coding tasks, making the development process more efficient and streamlined.

What is Copilot Agent Mode?

GitHub Copilot Agent Mode is an advanced feature of GitHub Copilot that enables AI-powered agents to perform more complex, context-aware tasks within your development environment. Unlike standard Copilot suggestions, Agent Mode can:

  • Execute multi-step instructions
  • Interact with your codebase and tools
  • Automate repetitive or complex workflows
  • Provide deeper explanations and code reviews

This makes it especially useful for tasks like refactoring, documentation, onboarding, and even CI/CD pipeline management.

Learning Objectives

  • Understand how to set up and configure GitHub Copilot Agent Mode.
  • Explore practical applications of AI agents in software development.
  • Learn how to integrate AI agents into existing workflows.
  • Discover best practices for using Agent Mode in real-world projects.

Setting Up GitHub Copilot Agent Mode

  1. Install GitHub Copilot: Use the VS Code extension for GitHub Copilot. Make sure you have it installed and configured.
  2. Enable Agent Mode: In the Copilot settings, enable Agent Mode. You can do this in the “chat” by changing the “ask” to “agent” and then selecting the agent you want to use. The default agent is suitable for most use cases. Note: A Copilot subscription may be required, but there is a free tier for experimentation.
  3. Configure Permissions: Ensure Copilot has access to your workspace and any tools you want it to interact with (e.g., terminal, file system, version control).
  4. Try It Out: Start a Copilot chat and use prompts like “Refactor this function” or “Set up a Dockerfile for Hugo” to see Agent Mode in action.

Practical Use Cases

  • Automating DevOps Tasks: Use Agent Mode to write, debug, and optimize CI/CD pipelines (e.g., GitLab, GitHub Actions).
  • Codebase Navigation: Ask the agent to find, explain, or refactor code across large projects.
  • Documentation Generation: Automatically generate or update documentation based on code changes.
  • Learning and Onboarding: New team members can use Agent Mode to get up to speed quickly by asking questions about the codebase or setup.
  • Local Development Automation: Use Agent Mode to generate Docker Compose files, Dockerfiles, and automate local dev environment setup.

Integrating with Modern CI/CD and Docker

In this project, Copilot Agent Mode was instrumental in:

  • Designing a multi-stage Dockerfile for Hugo static site builds and Nginx serving
  • Creating a robust docker-compose.yml for local development
  • Building a secure, automated GitLab CI/CD pipeline for building, pushing, and deploying Docker images to a remote Alma Linux server
  • Troubleshooting Docker-in-Docker, SSH key authentication, and registry integration
  • Documenting best practices and troubleshooting in the README

Agent Mode can help automate and document these processes, making it easier to maintain production-ready infrastructure.

Best Practices

  • Prompt Engineering: Be specific in your prompts to get the best results from Agent Mode.
  • Review Suggestions: Always review and test code or configuration changes suggested by the agent.
  • Security: Never expose sensitive credentials in prompts or code. Use environment variables and secrets management.
  • Documentation: Use Agent Mode to keep documentation up to date as your project evolves.

Where to Learn

Microsoft Learn Site is where I am learning about this and what this blog is based on. Microsoft Learn includes a lot of resources like this.

Other useful resources:

Conclusion

GitHub Copilot Agent Mode is a powerful tool for modern software development, especially when combined with best practices in CI/CD, Docker, and documentation. By leveraging AI agents, you can automate complex workflows, improve productivity, and maintain a robust, production-ready codebase.