course
GitHub Copilot: Agentic Engineering Fundamentals (GH-300)
Boost your productivity with GitHub Copilot.
Description
This course introduces GitHub Copilot and its ecosystem through practical labs and demos. Participants will learn how to integrate Copilot into their daily workflows efficiently.
The course starts with the foundations of GitHub Copilot, including installation, access management, security, and compliance.
You'll explore the context mechanisms that drive code completions.
You will work with all major Copilot features: inline suggestions, next edit suggestions, and Copilot Chat (Ask, Agent, and Plan modes).
The CLI integration covers interactive sessions and agentic capabilities like planning and delegation.
Practical labs on your own projects reinforce each topic, enabling you to apply the features immediately to realistic use cases.
Customization is a core component. You'll learn how to tailor Copilot behavior using instruction files, prompt files, etc. - the practice of context engineering. Diving into Model Context Protocol (MCP) for integrating custom tools and context-aware extensions into Copilot, building and running a small custom made MCP server of your own.
The course includes best practices for prompt writing, context management, quality assurance, and responsible usage. Security risks, potential model biases, and limitations of generative AI are discussed explicitly.
By the end of this course, you will confidently apply GitHub Copilot and its ecosystem to improve code quality, spend less time writing boilerplate, and integrate AI workflows into real-world development tasks.
Note: The Microsoft course GH-300 is a certification-focused course. This Info Support course goes deeper and emphasizes applied usage in realistic projects.
Learning Goals
 Apply code completions using inline suggestions to accelerate development | Apply  |
| Use Copilot Chat in Ask, Agent, and Plan modes to interact with and modify codebases | Apply |
| Customize Copilot using custom agents, skills, instruction files, prompt files and the Model Context Protocol (MCP) | Apply |
| Use GitHub Copilot CLI for agentic planning and code generation from the terminal | Apply |
| Describe the best practices of using GitHub Copilot | Understand  |
Prior Knowledge
- Familiarity with AI and machine learning terminology
- Basic understanding of generative AI systems
- Experience writing prompts
Subjects
- Installing and configuring GitHub Copilot
- Using inline and next edit suggestions
- Copilot Chat: Ask, Agent, and Plan modes
- Copilot in the CLI: interactive sessions, one-shot prompts, and agentic capabilities
- Customization with custom agents, skills, instruction files and prompt files
- Copilot extensions and advanced tools with Model Context Protocol (MCP), including using your own
- Writing effective prompts and using AI responsibly
Schedule
| Start date | Duration | Location | |
|---|---|---|---|
May 20, 2026 | 1 day | Veenendaal / Remote This is a hybrid training and can be followed remotely. More information | Sign up |
June 30, 2026 | 1 day | Veenendaal / Remote This is a hybrid training and can be followed remotely. More information | Sign up |
July 9, 2026 | 1 day | Veenendaal / Remote This is a hybrid training and can be followed remotely. More information | Sign up |
All courses can also be conducted within your organization as customized or incompany training.
Our training advisors are happy to help you provide personal advice or find Incompany training within your organization.
Trainers
Prior knowledge courses
Follow-up courses
GitHub Copilot: Agentic Engineering Advanced
Build high-quality, maintainable software solutions by applying Agentic AI to requirements, implementation, and testing.
- AI-Powered Engineering
Code Modernization Using Agentic AI
Transform your legacy code into modern, maintainable, and efficient code, requirements, and tests using of Agentic AI.
- AI-Powered Engineering
Certification
The knowledge from this training aligns with these certifications.
"The instructor was clearly very experienced and had a lot of knowledge about the subject."Mika
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