How to Build a Custom AI Agent for GitHub Copilot (Context Engineering Explained) скачать в хорошем качестве

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

Building serious AI agents for GitHub Copilot requires more than prompts. Guide: https://github.com/endorphin-ai/vs-co... 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 📌 Subscribe to Has Brains for deep dives into AI agents, autonomous systems, and modern AI engineering. #aiagents #githubcopilot #contextengineering #agentarchitecture #aiengineering