How to Build a Custom AI Agent for GitHub Copilot (Context Engineering Explained)

Building serious AI agents for GitHub Copilot requires more than prompts.

In this video, I present a modular framework for building scalable and maintainable GitHub Copilot Custom Agents by separating responsibilities into three distinct file types:

• Agents – workflow orchestration and execution
• Trees – structured decision-making logic
• Knowledge – reference data and domain context

Instead of embedding complex rules directly into a single agent file, this approach emphasizes decomposition: breaking information into small, focused, reusable components.

You’ll learn:
• Why large monolithic prompts fail at scale
• How to structure Copilot agents using Agents, Trees, and Knowledge files
• How orchestrator patterns enable agent delegation
• How variable chaining connects agents and sub-agents
• How to design agents that work across VS Code and GitHub.com
• How strict syntax rules and structured data flow improve reliability

This video serves as a technical blueprint for developers and QA engineers who want to build real AI assistants — not demos — using GitHub Copilot.

If you’re interested in context engineering, agent orchestration, and production-grade AI systems, this channel is for you.

🧠 Topics covered:
GitHub Copilot custom agents
AI agent architecture
Context engineering
Agent orchestration
Decision trees
Knowledge files
Scalable AI systems

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