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Techorama 2026 - Building an agent into any app using the GitHub Copilot SDK

Techorama 2026 was a blast! Great content, great atmosphere and great people. And of course, the main conversation topic was AI and how it will shape our industry. Nobody knows what is coming next but at least we already got a glimpse into the future. I delivered a presentation about integrating the GitHub Copilot SDK into your application and hopefully proved how easy it is to get an agent running inside your systems. AI is moving from assistants we query to agents that collaborate with us inside our applications. By reusing the same agentic loop behind the Copilot CLI, the Copilot SDK makes that shift practical for every developer. In this session, we explore how to embed GitHub Copilot–powered agents directly into your apps, extend them with custom skills, and connect them to your own data and workflows. You’ll see how to design agent behaviors, orchestrate tool use, and create safe, reliable interactions that feel native to your product. Whether you’re building personal ...
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Getting started with the GitHub Copilot SDK in .NET

In the previous post talked about why the GitHub Copilot SDK matters: it gives you a production-grade agent harness out of the box, so you can skip building the infrastructure and focus on your actual product. Now let's make it concrete. This post walks through everything you need to get up and running with the SDK in .NET — from prerequisites to a working streaming agent with a custom tool. What we will build We’ll keep it simple. By the end of this post you'll have a console application that: Connects to Copilot's agent runtime Sends a prompt and receives a streaming response Has a multi-turn conversation with persistent context Calls a custom tool you define in C# Prerequisites You'll need three things before touching any code. 1. .NET 8 or later The SDK requires .NET 8+. Verify your version: dotnet --version 2. GitHub Copilot CLI, installed and authenticated The SDK communicates with the Copilot CLI running as a local process — it...
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You don't need to build your own agent harness

Building an agent sounds straightforward until you actually start. Before you write a single line of business logic, you're already deep in infrastructure decisions: How do you manage context across multiple turns? How do you orchestrate tool calls? How do you handle model routing, MCP server integration, permissions, failure modes, and safety boundaries? By the time you've answered all those questions, you've quietly built a small platform — and you haven't shipped anything yet. This is the tax that every team building agentic applications has been paying. Until now... Meet the GitHub Copilot SDK GitHub launched the Copilot SDK in technical preview in January 2026, and its core value proposition is refreshingly direct: stop building the harness, start building your product . The SDK gives you programmatic access to the same production-tested execution loop that powers GitHub Copilot CLI. That means the planning, tool invocation, multi-turn context management,...
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We are all beginners

While visiting multiple organizations and talking to colleagues about integrating AI into their software development lifecycle, I noticed something: The approaches couldn’t have been more different. Some teams were embedding AI deeply into every step of development—coding, testing, documentation, even architectural decision-making. Others were deliberately cautious, limiting AI to narrow, controlled use cases. Opposites. And yet, both felt… reasonable. That’s when it clicked for me: We are all beginners. Not in the dismissive sense. Not in a “we don’t know anything” kind of way. But in the ways as described inside the Dreyfus Model of Skill Acquisition .   The Dreyfus model, briefly The Dreyfus model describes how people acquire skills through five stages: Novice – Rely on rules and rigid guidelines Advanced Beginner – Start recognizing patterns, but still need support Competent – Can plan, prioritize, and make conscious de...
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How to use GitHub Copilot Agent Skills in Visual Studio

With the introduction of agent skills , we can teach our AI agent to handle our most repetitive and specialized workflows. After adding context through an agent.md file, integrating tool calls using MCP, creating our own Agents, this is a logical next step in defining your AI enabled software development lifecycle. Here's everything you need to know to get started. What are agent skills? Agent skills are folders of instructions, scripts, and resources that GitHub Copilot can load automatically when relevant to your prompt. Think of them as reusable "playbooks" you write once and invoke repeatedly — without having to re-explain the context every time. Unlike custom instructions , which set broad coding guidelines that apply across nearly every task, skills are meant for specialized, on-demand capabilities: things like running a specific test suite, converting file formats, generating components, or following a custom deployment checklist. How to create your firs...
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Supercharging GitHub Copilot CLI with Ollama: Local Models, Full Control

GitHub Copilot CLI is my 'go-to' coding agent when I work directly from your terminal. It understands my codebase, proposes edits, runs commands, and helps me move faster without leaving the command line. As I care about privacy, offline workflows, or custom model experimentation, I decided to try Copilot CLI entirely on local LLMs using Ollama. No cloud dependency. No API keys. Just my machine, a local model and my workflow. In this post, I’ll walk through how to set it up, and how to use it effectively. Why combine Copilot CLI with Ollama? Copilot CLI gives you a powerful agentic interface for your codebase. Ollama gives you a fast, local model runtime with support for dozens of open models. Together, you get: Local-first AI coding:  keep your code and prompts on your machine Predictable performance:  no rate limits or network delays Model flexibility : swap between Qwen, Llama, Mistral, Gemma, and more Agentic workflows:  Copilot CLI can edit...
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Fixing "Selected tag uses an invalid operating system " error when deploying to Azure Container Apps

Yesterday I tried deploying a Docker image to Azure Container Apps and hit a wall with a cryptic error about an invalid operating system.  It took me a lot of time to find the root cause and fix it.Here's what happened, why it happens, and the exact command to fix it. The error After pushing an image to Azure Container Registry and pointing a Container App at it, the deployment failed with this message: Selected tag uses an invalid operating system ''. Error when deploying an Azure Container App image. The error is frustrating because it gave me almost nothing to work with; an empty string where the OS name should be. The image built and pushed just fine, so what's going on? Root cause The culprit is provenance attestation — a feature that Docker BuildKit enables by default when using docker buildx . When provenance is enabled, Docker generates an extra manifest layer containing build metadata. This results in a multi-platform image manifest (an OCI image i...
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