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◆ COURSE · 8 lessons · 129 min read · free

GitHub Copilot in Practice: Agentic Coding for Production Engineering Teams

Teach practicing software engineers to use GitHub Copilot's current capabilities -- interaction modes, effective prompting and context supply, the coding/cloud agent and CLI, code review with custom instructions, and MCP-based extensibility -- to plan, implement, review, and validate real changes in production codebases, grounded strictly in the accepted evidence baseline rather than promotional claims or a static feature tour.

Who it's for

Software engineers who already work in production codebases and are adopting or expanding their use of GitHub Copilot, including its agentic and autonomous features; assumes existing programming and version-control fluency but not prior Copilot experience.

You'll need

  • professional software development experience
  • familiarity with git and pull-request workflows
  • access to a GitHub account with some tier of Copilot enabled

What you'll be able to do

  • Select and justify an appropriate Copilot plan and interaction surface for a given engineering context using current, non-legacy billing concepts.
  • Use chat, edit, and agent modes appropriately across supported IDEs, correctly distinguishing GitHub's canonical Edit mode from Visual Studio's separately branded Copilot Edits feature.
  • Delegate bounded engineering tasks to the Copilot coding/cloud agent and the Copilot CLI, evaluating results against current plan eligibility and billing behavior.
  • Write effective prompts and supply relevant context to Copilot using documented composition and context-provision technique, choosing an interaction mode and validation approach suited to the task.
  • Configure and apply Copilot code review with scoped custom instructions to improve review quality on AI-generated and agent-produced changes.
  • Configure MCP servers and correctly reason about tool-call approval behavior across autonomous and interactive Copilot surfaces.
  • Recognize which Copilot billing, plan, and product-naming facts are volatile or historical, and know where to look up current or legacy detail rather than relying on memorized figures.
  • Plan, execute, review, and validate an end-to-end agentic change to a realistic codebase, integrating context, agent delegation, review, and MCP practices learned throughout the course.

Lessons

  1. Lesson 1 · 16 minGetting Started with GitHub Copilot: Core Concepts and Interaction ModesGitHub Copilot is not a single button or a single kind of assistance — it's an AI collaborator you reach through several distinct interaction surfaces, each suited to a different kind of work.
  2. Lesson 2 · 13 minChoosing a Copilot Plan: Core AI Credits Billing ConceptsEvery Copilot plan you'll evaluate in this chapter runs on the same billing mechanism, so it's worth understanding before you look at plan names or prices.
  3. Lesson 3 · 11 minThe Copilot Cloud Agent and CLIChapter 1 introduced Agent mode: you give Copilot an instruction inside your IDE, it plans and edits files locally, and you watch the changes land in your editor before you accept them.
  4. Lesson 4 · 24 minReviewing AI-Generated Code: Copilot Code Review and Custom InstructionsYou already know GitHub Copilot as an interaction partner — completions, chat, edit mode, Agent mode.
  5. Lesson 5 · 18 minExtending Copilot with the Model Context Protocol (MCP)Model Context Protocol (MCP) is a client-server standard.
  6. Lesson 6 · 17 minPrompting and Context Engineering: Writing Effective Instructions and Supplying Context to CopilotBefore Copilot Chat can help with a task, the prompt itself has to carry enough information for the model to act on.
  7. Lesson 7 · 10 minReference: Legacy Billing Mechanics, Credit Rates, and Plan NuancesThis chapter is a reference, not a lesson sequence.
  8. Lesson 8 · 20 minCapstone: Supervising and Validating an End-to-End Agentic WorkflowThis capstone introduces no new Copilot feature.
Start with lesson 1 →