Software Architecture
A Windows AI agent architecture is the set of parts that let an AI system understand a request, decide what to do, and then carry out those actions inside Windows apps, files, or enterprise services. In plain terms, it is the wiring that connects a model to tools, permissions, memory,
Windows 11
A local RAG app on Windows lets you ask questions over your own PDFs, notes, and work files without sending the documents to a cloud service. On Windows, the simplest practical setup is a local model runner, a Python app, and a local vector store. CPU-only can work for
Software Architecture
Windows AI Function Calling is Microsoft’s way of letting a model map a user’s request to an app action and fill in the arguments for that action. In a Windows app, you expose functions, describe what they do and what inputs they need, and the model chooses when
Software Architecture
ONNX Runtime is a common way to run AI models locally in a Windows app, especially when you want inference on the CPU, GPU, or supported accelerators without sending every request to a server. It fits into the Windows AI stack as the runtime that executes ONNX models; it is
Software Architecture
Windows AI Model Routing is the logic that decides whether an AI request stays on the device or goes to the cloud. It looks at the task, the device, policy, and connectivity, then sends the request to the model or service that seems best for that job. That choice affects
Software Architecture
Windows AI tool calling lets an AI model ask your app to run a specific function, then use the result to answer the user. In practice, the model decides when a tool is needed, your app executes that tool, and the result goes back to the model. It’s different
Software Architecture
MCP on Windows means using the Model Context Protocol on a Windows PC so an AI app can discover and use local or remote tools in a standard way. In practice, a Windows machine can run an MCP client, host an MCP server, or do both at once. The client
Software Architecture
RAG means retrieval-augmented generation: your app finds relevant local or enterprise content first, then gives that content to the model so it can answer with context instead of memory alone. On Windows, that usually means your app reads files, app data, or a search index, turns the content into
Software Architecture
Windows AI vector search is semantic search on a Windows app or device, usually built with embeddings and a vector index rather than with a special Windows-wide search feature. In plain terms, you turn content into numeric vectors, compare those vectors for similarity, and use that to find results
Decision Making
Windows AI model selection is usually about choosing where a request runs, not picking one universal model by name. In practice, Windows, the app, and the provider decide between an on-device model, a model hosted by a local app or provider, or a cloud model behind a service. Which
Software Architecture
Windows AI Embeddings turn text or other content into vectors, which are just lists of numbers a model can compare for meaning. In a Windows app, you use them behind features like semantic search, similarity matching, deduping, and recommendations, not as the feature the user directly sees. If you keep
Software Architecture
Windows AI application architecture is the way you structure a desktop app so AI features stay fast, secure, and maintainable instead of spreading through the UI. It usually separates the presentation layer, orchestration, model access, data flow, and storage so you can use either local or cloud AI without rewriting