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 ...

Retrieval-Augmented Generation in .NET: A Practical RAG Guide If you're building AI applications with .NET, there is a point where simply sending a prompt to an LLM stops being enough. Generating a summary is easy. Generating an answer from your company's documents, policies, product information, support knowledge, database records, or internal documentation is a different problem. The model does not automatically know your private data. And even when information is available somewhere in a model's training data, you generally do not want an application to depend on the model remembering it correctly or knowing the latest version of a business policy. This is where Retrieval-Augmented Generation (RAG) becomes useful. Instead of asking an LLM to answer entirely from its general knowledge, your application first retrieves relevant information from an external knowledge source and provides that information to the model as context. RAG = retrieve releva...

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 , ...