Blogging with GitHub Copilot's Virtual Teammates
Note: This blog post was created entirely using GitHub Copilot Coding Agent and is AI-generated. It’s published purely for demonstration and testing purposes to explore the capabilities of GitHub Copilot Coding Agent. This transparency aligns with Responsible AI practices.
Introduction to GitHub Copilot Coding Agent
GitHub has recently taken AI coding to a new level with the GitHub Copilot Coding Agent. Think of it as your virtual teammate that works independently on tasks within your repositories. Unlike the inline suggestions in GitHub Copilot, the Coding Agent tackles entire tasks and implements complete features, helping you move projects forward efficiently.
Capabilities That Will Change Your Workflow
GitHub Copilot Coding Agent extends beyond simple code suggestions with capabilities that include:
- Assigning entire tasks: Create issues or tasks and have the Coding Agent implement them
- Making changes across multiple files: The agent can understand your codebase and make coordinated changes across files
- Fixing bugs and implementing features: From bug fixes to feature implementations, the agent can handle substantial pieces of work
- Running tests and validating changes: The agent can test its own work, ensuring changes meet requirements
- Providing detailed explanations: Get comprehensive explanations about the changes made and why they were made
What makes this particularly interesting is that the agent operates within the constraints of your GitHub repository’s structure and existing patterns, creating code that aligns with your project’s style and approach.
How to Get Started with Coding Agent
Getting started with GitHub Copilot Coding Agent is simple. According to the official documentation, you’ll need:
GitHub Copilot Enterprise subscription: This feature is part of the enterprise offering.
Enable the feature: Your organization admin needs to enable Copilot Coding Agent.
Assign a task: Create an issue, then assign it to Copilot.
Review and collaborate: Once Copilot creates a pull request, review it like any other PR.
The workflow integrates seamlessly into existing GitHub processes.
The Developer and Agent Partnership
Working with GitHub Copilot Coding Agent establishes a collaborative workflow:
Task definition: You define a task or issue with clear requirements.
Agent implementation: The Coding Agent reviews your repository and creates a pull request with its implementation.
Developer review: You review the changes, possibly requesting adjustments.
Iterative refinement: The agent responds to your feedback with additional changes.
This creates a dynamic where the agent handles implementation details, while you maintain control over direction and quality. You’re the architect and reviewer, while the mechanical aspects of coding are handled for you.
Steering the Agent and Handling Roadblocks
With GitHub Copilot Coding Agent, you can steer its work when things don’t go as expected:
Provide feedback: Comment directly on the PR to clarify requirements.
Handle failed builds: Ask the agent to fix issues that arose during testing.
Refine implementation: Request changes to align with coding standards.
For example, if a build fails, you might comment:
“The build is failing because we’re missing tests for the new authentication method. Could you add unit tests for this?”
The agent can then make these adjustments, addressing the specific issues you’ve identified.
The Meta Moment: AI Writing About AI
There’s something recursive about having an AI write about an AI coding assistant. This post itself serves as a meta-example of AI-powered content creation.
It raises questions about expertise and authorship:
- Is AI providing “insider knowledge” or just regurgitating training data?
- How does reading AI-created content about AI differ from human-authored content?
- What are the implications when content can be generated without human experience?
These questions highlight the evolving relationship between human creators and AI assistants. As these tools become more sophisticated, the line between human-created and AI-created work continues to blur.
Conclusion: An Experiment in AI Capabilities
I want to emphasize that this blog post itself is an experiment in using GitHub Copilot Coding Agent. It’s not intended to replace human-authored content on this site, but rather to explore and demonstrate what’s possible with current AI technology.
The post you’ve just read was created entirely by AI through GitHub Copilot Coding Agent, without direct human authoring of the content. This kind of transparency about AI-generated content is an important part of using these tools responsibly.
While it’s fascinating to see what AI can produce, this experiment serves primarily as a learning opportunity about the capabilities and limitations of these tools. It should not be considered a serious contribution to this site’s content nor attributed to the human author of this website.
The real value comes from understanding how these technologies work and finding the right balance between AI assistance and human creativity in our workflows.
The Process: How This Post Was Created
This blog post was created by:
- Opening an issue describing the requirements for a blog post about GitHub Copilot Coding Agent
- Assigning the issue to GitHub Copilot
- Letting GitHub Copilot Coding Agent analyze the repository structure and existing blog posts
- Reviewing the AI-generated content and providing minimal guidance
- Accepting the pull request with the new blog post
The entire process was handled by GitHub Copilot Coding Agent with minimal human intervention, demonstrating the capability of AI to understand repository structure, content requirements, and writing style guidelines.
Note: This experiment demonstrates the capabilities of GitHub Copilot Coding Agent for educational purposes only. Future content on this site will continue to be human-authored.
Video Demonstration
Check out this video showing me using GitHub Copilot Coding Agent to create this very blog post (yes, it’s inception!):
If the embed doesn’t work, you can watch the video on YouTube.
