Building Single-Agent .NET Applications with Semantic Kernel

AI integration is no longer a luxury for software developers; it is a core requirement for modern software. This written course guides you through the process of building intelligent, agent-driven applications using the Semantic Kernel framework for .NET. You will transition from writing standard procedural code to designing autonomous single-agent systems that can reason, call external APIs, and access private data. By understanding how to orchestrate AI models and manage chat histories, you will be prepared to implement secure and scalable AI features in your enterprise applications. What you'll learn: - Understand the core architecture of Semantic Kernel and how to initialize it within .NET applications. - Connect to various model providers, including cloud-based OpenAI endpoints and local models using Ollama and ONNX. - Build custom native plugins and OpenAPI plugins to extend your AI agent's capabilities with real-world tool calls. - Implement Retrieval-Augmented Generation (RAG) to ground your agent's responses in private data sources. - Integrate modern Model Context Protocol (MCP) servers to enable standardized data and tool interactions. - Apply modern .NET design patterns, such as dependency injection and asynchronous streams, to keep your AI workflows clean and efficient. The course begins with foundational concepts of Large Language Models (LLMs) and Semantic Kernel setup, before moving step-by-step through prompt engineering, custom plugin development, and advanced data integration. You will study practical code implementations and architectural patterns designed for real-world scenarios. This course is designed for .NET developers who are new to AI engineering and want to build intelligent applications. A basic understanding of C# and .NET development is recommended, but no prior experience with machine learning or AI is required. Start reading today to build your first intelligent .NET agent.