Transitioning to AI: Engineering the Next Paradigm

AI Agents in .NET: Tools, RAG, MCP and Workflows A practical guide to AI agent architecture for .NET developers, covering tool calling, RAG, MCP, workflows, security, testing, observability, and production design. If you write software for a living, you've probably already used an AI model. The more interesting question is what happens when the model needs to do something instead of simply answer something. Maybe it needs to search a knowledge base, inspect an order, call an API, create a support ticket, retrieve information from a database, request approval, or determine which step should happen next. That's where AI agents become useful. An agent is not simply a chatbot with a new label. An agent combines a model with instructions, context, tools, state, and an execution loop so that an application can work toward an objective. For .NET developers, this is particularly interesting because much of the difficult work still looks ...

Microsoft.Extensions.AI vs Semantic Kernel: What Is the Difference? If you're building AI applications with .NET, you've probably encountered two Microsoft technologies that appear to overlap: Microsoft.Extensions.AI Semantic Kernel Both can be used when building applications that communicate with large language models, work with tools, generate structured responses, retrieve information, and implement AI-powered workflows. That naturally leads to an important question: Are Microsoft.Extensions.AI and Semantic Kernel alternatives, or do they solve different problems? The most useful answer is that they operate at different architectural levels . Microsoft.Extensions.AI provides common .NET abstractions and building blocks for AI services. Its ecosystem includes abstractions such as IChatClient , IEmbeddingGenerator , AI functions, tool support, and chat-client pipelines. Semantic Kernel is a broader AI development SDK centered around the Kernel , ...

AI Engineering with .NET: A Practical Guide If you write C# for a living, there is a good chance someone has already asked you to "add AI" to an application. Maybe the requirement is document summarization, answering questions over company policies, classifying support tickets, generating content, extracting structured data, or building a chatbot. The interesting part is that most of these problems do not require you to become a machine-learning researcher. They require you to understand how AI models fit into a software system: how to call them, structure their output, provide application context, handle failures, control cost, evaluate responses, and keep the system observable in production. That is where AI engineering with .NET becomes useful. If you already work with C#, ASP.NET Core, dependency injection, APIs, databases, background services, logging, and distributed systems, you already have much of the foundation requir...