Agentic AI Development with Agent Framework, MCP and .NET

"You're a Senior Architect, but AI makes you feel like a Junior again." If you have tried to learn Agentic AI, you have likely noticed a frustrating trend: everything is in Python, or it focuses on simplistic "Hello World" scripts that immediately crumble in a real-world enterprise environment.

Welcome to the definitive guide on building production-ready agentic AI systems in the .NET ecosystem. Moving beyond theory, this course focuses on the hands-on development of autonomous multi-agent orchestration for enterprise applications. Powered by the Microsoft Agent Framework, Microsoft Foundry, the Model Context Protocol (MCP), Aspire, AG-UI, DevUI and .NET, you will learn how to build robust AI workflows that solve complex business problems.

This course is designed to give you production-grade visibility and control from day one, integrating enterprise-grade AI agent patterns for real-world business workflows.

What You Will Master
In this comprehensive enterprise course, we move beyond basic prompt engineering into deep architectural implementation natively in C#:

• Microsoft Agent Framework (MAF): Deep dive into Microsoft's framework for building sophisticated, stateful AI systems, utilizing Microsoft Foundry and Azure OpenAI as your cognitive engines.

• Multi-Agent Orchestration: Design and implement complex workflow patterns (Sequential pipelines, Concurrent execution, dynamic Handoffs, and Group Chats) using Swarm Intelligence and the WorkflowBuilder.

• Agentic RAG Systems: Rethink traditional, rigid RAG pipelines. Build intelligent, intent-based retrieval systems using Qdrant vector databases and the TextSearchProvider, allowing the AI to autonomously decide when and how to search your enterprise knowledge.

• Protocols & Interoperability: Master the bleeding edge of integration: Agent-to-Agent (A2A) network communications, the Model Context Protocol (MCP) for tool exposure, and the AG-UI protocol for generative frontend streaming.

• Observability & Visual Testing: Achieve production-grade visibility using .NET Aspire and DevUI. Visually track JSON payloads, token usage, agent handoff latencies, and tool calls in real-time.

• Enterprise Microservices Integration: Scaffold a complete MinimalAgent microservices architecture, learning exactly how to integrate AI agents seamlessly into existing backends and web APIs.

Course Roadmap and Structure
This curriculum is structured across 4 comprehensive parts, designed to systematically take you from foundational agent anatomy to advanced multi-agent enterprise integration:

Part 1: Core Agent Development
We start by mastering the foundational anatomy of an AI Agent. You will establish your connections to the Azure OpenAI service and explore the agent invocation lifecycle. You will get hands-on by developing custom function tools—utilizing the AIFunctionFactory to automatically generate JSON schemas from native C# methods. Finally, we will implement the AgentSession class to give your agents the persistent context and memory required for meaningful enterprise interactions.

Part 2: Orchestrating Multi-Agent Systems
Once we understand the individual agent, we scale up to Swarm Intelligence. You will learn to design collaborative networks of highly specialized micro-agents (e.g., triage, finance, compliance). We will use the AgentWorkflowBuilder to architect distinct industry-standard topologies: Sequential, Concurrent, Group Chat, and Handoff patterns. We will integrate these swarms into our MinimalAgent .NET Aspire architecture, utilizing DevUI to visually test and watch real-time debates and handoffs unfold graphically.

Part 3: Advanced Reasoning: Agentic RAG
Move beyond traditional RAG. In this module, we build Agentic RAG—where the AI itself decides if it needs to query the database. To support enterprise scale, we will integrate .NET with Qdrant Vector Stores. You will learn how to generate embeddings, execute semantic searches, and tie it all together using the TextSearchProvider, turning your corporate data into a cognitive tool your agents can wield autonomously.

Part 4: Agent Communications and Protocols
Solve the enterprise interoperability challenge. We will expose your MAF agents as network-accessible Web APIs to establish secure A2A (Agent-to-Agent) architectures. We will dive deep into the Model Context Protocol (MCP), designing architectures that connect your C# agents to both local and hosted MCP servers—giving them autonomous access to external repositories and documentation. Finally, we will leverage the AG-UI Protocol to push interactive, generative UI components directly to the client frontend.

Technology Stack

• Languages & Frameworks: .NET 10, C#, ASP.NET Core, Blazor Server

• AI & Agents: Microsoft Agent Framework, Azure OpenAI (gpt-5-mini, text-embedding-3-small)

• Cloud & Deployment: Microsoft AI Foundry

• Frontend Protocol: AG-UI — Agentic UI streaming protocol

• Orchestration: .NET Aspire for Service Discovery and Container Lifecycle Management

• Observability: Aspire OpenTelemetry, Application Insights

• Vector Data & Storage: Qdrant Vector Databases

• Architecture: Microservices, Clean Architecture, Model Context Protocol (MCP)