Build AI Agents with GitHub Copilot (Video)

Description

3+ Hours of Video Instruction

Build Functioning Agents from Scratch with Copilot

With AI agents becoming increasingly important in building applications that can reason, automate tasks, and interact with other systems, there is a growing need for developers to learn how to design, implement, and refine these agents effectively. Build AI Agents with GitHub Copilot addresses that need by providing a structured, hands-on path to understanding both the fundamentals of AI agents and the practical use of Copilot to accelerate development.

This course is designed to help developers harness the power of GitHub Copilot to build AI agents more efficiently and effectively. By combining foundational knowledge of AI agents with hands-on practice, the course takes a practical, project-based approach--guiding learners from understanding core concepts and setting up their environment to building, testing, and refining real AI agents with Copilot's assistance. Along the way, participants will explore best practices, compare Copilot with other tools, and reflect on responsible AI development, ensuring they leave with both the technical skills and strategic insight to accelerate their productivity with AI-assisted coding.

Skill Level:

• Beginner to Intermediate

Learn How To:

• 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 a broader perspective
• Apply ethical and responsible practices in AI-assisted development

Course Requirements:

• Basic programming experience in Python or JavaScript, familiarity with using an IDE, and a general understanding of software development workflows

Who Should Take This Course:

• Software Developers
• Backend Engineers
• Frontend Engineers
• Full-Stack Engineers
• Machine Learning Engineers
• AI/ML Developers

About the Presenter
Shaun Wassell is a lifelong programmer and problem-solving addict. His goal is to help people build incredible software and solve meaningful problems by mastering the art of software development. For the past 2+ years, he's been a trainer at CBT Nuggets where he focuses on creating high-quality web development and certification content. You can check out his extensive collection of React, Angular, Python, and JavaScript courses, as well as a huge amount of other software- and IT-training content at cbtnuggets.com.

About Pearson Video Training
Pearson publishes expert-led video tutorials covering a wide selection of technology topics designed to teach you the skills you need to succeed. These professional and personal technology videos feature world-leading author instructors published by your trusted technology brands: Addison-Wesley, Cisco Press, Pearson IT Certification, Sams, and Que. Topics include: IT Certification, Network Security, Cisco Technology, Programming, Web Development, Mobile Development, and more. Learn more about Pearson Video training at informit.com/video.

Video Lessons are available for download for offline viewing within the streaming format. Look for the green arrow in each lesson.

Sample Content

Table of Contents

Introduction

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

Summary

Updates

Submit Errata