Drawing on principles from Essential DevOps, the book he co-authored with Ian Griffiths, Brian takes you on a full-day journey through building intelligent, production-grade AI-powered applications using C#, GitHub Copilot, Microsoft Foundry, and modern DevOps practices. This is a guided walkthrough rather than a hands-on lab. You'll watch, learn, and leave with working patterns and reference code you can run on your own. It's designed for developers who want a comprehensive overview with practical, defensible insights they can take back to work on Monday.

The day starts with the AI fundamentals every .NET developer needs in 2026, then moves directly into the security, privacy, and compliance foundations that frame every other decision in an AI application. From there, Brian works through the architectural patterns that matter most, including Retrieval-Augmented Generation, semantic search, and memory management, alongside the context and prompt engineering techniques that make those patterns work. You'll then see how to build agents and multi-agent workflows with Microsoft Agent Framework, the successor to Semantic Kernel and AutoGen, with industry-standard integration via MCP servers and Agent-to-Agent communication.

The afternoon turns to the DevOps and engineering disciplines that separate a demo from a shippable product. Brian shows how GitHub and Azure DevOps work together rather than against each other, with code and Copilot in GitHub and planning and releases in Azure DevOps for teams that already have that investment. You'll see how to build evaluations so you know when your AI app regresses, how to add observability for probabilistic systems with real per-token costs, and how to harden AI applications in practice using GitHub's security tooling and Microsoft Foundry's content safety features. The day closes with responsible AI practices for production and a tour of deployment options on Azure, from Container Apps to Functions to hybrid edge scenarios.

This workshop assumes you've used C# and have some exposure to AI tooling but doesn't assume you've shipped an AI app to production. By the end of the day, you'll have the conceptual frame and the practical patterns to do exactly that.

You will learn:

• Essential AI development concepts and architectural patterns for 2026
• How to bake security, privacy, and compliance into AI applications from day one
• Practical techniques for integrating AI into your development workflows
• How DevOps practices apply to secure AI-driven delivery across GitHub and Azure DevOps, including evaluations and observability
• How to deploy real-world, production-grade AI applications to Azure