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Over the past few weeks, I’ve been exploring how to build practical, privacy‑first agentic AI workflows that run entirely on a local machine. In my latest project, I combined GitHub Copilot SDK with Foundry Local to create a fully offline agent capable of choosing and executing tools intelligently — without relying on any cloud model. In this demo, I walk through how I built: - A Foundry Local LLM tool for on‑device inference - Three lightweight Python tools  - A router prompt that lets Copilot SDK decide which tool to invoke - A clean async loop that ties everything together The result is a flexible, extensible agent that can reason, select tools, and produce polished answers — all running locally. If you’re interested in agent design, local LLMs, or practical orchestration patterns, this walkthrough will give you a clear, end‑to‑end example you can adapt to your own projects. 🎥 Watch the full video here:

AI agents are quickly becoming a core part of modern development workflows, and the GitHub Copilot SDK makes it surprisingly straightforward to build your own. Instead of relying on prompt engineering alone, the SDK lets you define structured tools, give your agent explicit capabilities, and execute real code through LLM‑driven reasoning. In my latest demo, I walk through the full process of creating an agent from scratch — setting up the project, defining the agent, building custom tools, and running everything locally. You’ll see how the SDK handles tool invocation, schema validation, and natural‑language responses, all while keeping your logic deterministic and maintainable. If you're exploring agentic workflows or want to understand how Copilot can power real execution paths, this walkthrough will give you a clear, practical starting point. 🎥 Watch the full step‑by‑step video here: 👉  This is just the beginning — once you understand the pattern, you can extend your agent with...