This module covers the core concepts, principles, and capabilities of AI agents, exploring the paradigm shift from traditional microservices to goal-oriented AI orchestration in enterprise software.

This module explores the core components of the .NET AI ecosystem, detailing how the Microsoft Agent Framework, Azure OpenAI, and .NET Aspire work together to power enterprise-grade AI orchestration.

This module guides you through configuring your local development workspace, setting up .NET 10 and the Azure CLI, and preparing a console application for AI development.

This module walks you through the fundamentals of agent development, covering execution mechanics, structured JSON output generation, and interactive visual debugging.

This module focuses on extending agent capabilities through custom function tools, dynamic JSON schema generation, and implementing essential Human-in-the-Loop governance approvals.

This module examines how to build conversational continuity and memory inside stateless AI environments, leveraging sessions, context providers, and distributed state persistence.

This module introduces graph-based multi-agent workflow architecture in .NET, detailing how to coordinate specialized executors, nodes, and conditional edges to solve complex business tasks.

This module explores advanced enterprise multi-agent design patterns, teaching you to implement sequential, concurrent, and dynamic group chat routing strategies.

This module explains the concepts behind Agentic Retrieval-Augmented Generation, highlighting its active researcher loop and planning capabilities over traditional linear RAG.

This module guides you through building a production-grade Agentic RAG system in .NET using Qdrant vector databases, text search adapters, and semantic embeddings.

This module addresses the integration silos in AI systems by introducing standardized communication frameworks, including Agent Communication Protocol, Agent-to-Agent, AG-UI, and Model Context Protocol.

This module details how to implement secure, distributed Agent-to-Agent (A2A) network communications over Web APIs, using structured cards for capability discovery.

This module focuses on the Model Context Protocol (MCP) lifecycle, showing you how to establish secure transport connections and implement hosted tools for AI agents.

This module teaches you to architect and develop Agent-to-User Interface (AG-UI) integrations, enabling agents to dynamically push interactive, generative UI components to frontends.

This module concludes the course, offering final thoughts and next steps for continuing your journey in Agentic AI and .NET orchestration.