
Learn to build, test, and ship code faster with the GitHub Copilot coding agent through quick lessons, hands-on labs, and practical, repeatable workflows.
Compare coding agent and agent mode in GitHub Copilot: coding agent runs in the cloud to plan, edit, test, and open a pull request, while agent mode guides in-editor edits.
Upgrade your personal account to GitHub Copilot Pro or Pro Plus to access the coding agent across IDE, CLI, and mobile, while tracking premium requests and budgets to control costs.
Demonstrate the GitHub Copilot coding agent transforming a Python Tetris game in VS Code by adding scoring, detailed blocks, and slower fall speed, with issue tracking and PR workflow.
Create a new issue describing required updates to block visuals, apply simple polish, and lock render and game speed, then assign Copilot to the issue on main with supporting files.
learn to describe tasks for the GitHub Copilot coding agent in the agents panel, choosing repository and branch, without opening an issue, and monitor progress toward a pull request.
Learn to assign tasks to the GitHub Copilot coding agent directly from VS Code using the GitHub Pull Requests extension, and create a light/dark mode toggle for a Tetris game.
Assign tasks to the copilot coding agent directly in VS Code by using the GitHub extension to attach issues, assign Copilot, and monitor PRs.
Observe how the Copilot Coding Agent monitors tasks through session logs and view sessions, leveraging playwright MCP server to plan, build a Python pong game with pygame, and run tests.
Customize copilot with a repository-wide markdown file to embed your tech stack and coding guidelines. Add a root agents.md for run and test instructions, boosting output alignment with your architecture.
Configure the cloud based ephemeral development environment for the Copilot agent by pre-warming it with a GitHub Actions workflow (Copilot setup steps YAML) to install Node.js and dependencies before tests.
Set environment variables and secrets for the copilot coding agent in a reserved GitHub actions environment to securely authenticate with private services and API keys in your workflow.
Learn how model context protocol (MCP) feeds live, repo-specific information from private repositories, internal knowledge bases, and APIs into the coding agent to improve plans and code.
Conclude by showing how to scope tasks, review outputs, and choose between GitHub Copilot coding agent and agent mode, while noting evolving features and to check official docs for updates.
What if you could open a GitHub issue, describe the outcome you want, and come back to a draft PR with tests run and changes ready to review? That’s the promise of the GitHub Copilot Coding Agent. This course is a hands-on guide to delegating real work to an agent that operates in the cloud - planning tasks, updating multiple files, running checks with GitHub Actions, and iterating based on your feedback - so you ship faster with less context-switching.
What You’ll Learn:
Best practices on how to use the GitHub Copilot Coding Agent to your advantage
When to use the GitHub Copilot Coding Agent vs. Agent Mode and how to combine both for maximum efficiency.
Learn the sweet-spot tasks that are ideal for the GitHub Copilot Coding Agent
How to enable and configure the Coding Agent for a repository within your GitHub Organization/Enterprise
Writing high-signal issues (clear goals, constraints, acceptance criteria) that the agent can turn into reliable PRs.
Providing additional context to the Coding Agent to ensure the Coding Agent follows your organzation's standards
Guiding runs: reviewing plans and diffs, giving feedback in comments, and re-running tasks safely.
Automating multi-file changes, test execution, and CI workflows - without living in your IDE.
Configure the Coding Agent to work with MCP to connect to external data and increase capabilities beyond GitHub
By the end, you’ll confidently direct the Coding Agent from idea → issue → draft PR, keeping control over quality while offloading the busywork.