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Build AI Agents with GitHub Copilot by Microsoft Press

Build AI Agents with GitHub Copilot by Microsoft Press

3h 51mIntermediate2026-07-01

Authors

Microsoft Press

Microsoft Press

Microsoft

Course details

AI agents are becoming essential in modern software development, enabling applications to automate tasks, interact with data, and adapt intelligently. GitHub Copilot makes this process even more accessible by streamlining coding, reducing repetitive work, and helping developers focus on higher-level design. This course equips you with the technical know-how and hands-on skills required to understand AI agents, set up and use Copilot effectively, and build functioning agents from scratch. By the end of this course, you’ll be prepared to confidently apply AI-assisted coding to real-world projects and improve your overall productivity.

Learning objectives
Grasp the fundamentals of AI agents and their components.
Configure GitHub Copilot for your development environment.
Write, test, and refine AI agent code with Copilot’s assistance.
Build a complete working AI agent step by step.
Extend AI agents with external APIs and libraries.
Use Copilot to generate, refactor, and improve code quality.
Compare Copilot with other AI coding tools for broader perspective.
Apply ethical and responsible practices in AI-assisted development.

Concepts

Introduction

  • Course welcome

Understand AI Agents and GitHub Copilot

  • Learning objectives
  • Understand what an AI agent is
  • Explore key components of code-based AI agents
  • Recognize the role of GitHub Copilot in AI agent development
  • Compare GitHub Copilot with other AI coding tools
  • Identify use cases for AI agent development with Copilot

Set Up Your Development Environment

  • Learning objectives
  • Install GitHub Copilot in your IDE
  • Configure Copilot settings and preferences
  • Use advanced configurations
  • Set up an AI agent project
  • Write and run your first Copilot-assisted code

Build and Extend AI Agents with Copilot

  • Learning objectives
  • Implement a simple rule-based agent
  • Make and execute decisions with an agent
  • Control agent behavior with generative AI
  • Create effective AI agent prompts
  • Add simple RAG to an AI agent

Apply Best Practices and Next Steps

  • Learning objectives
  • Make AI agent code reusable
  • Using structured outputs
  • Learn best practices for creating safe AI agents

Conclusion

  • Summary and next steps

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