Agents are easy to demo, hard to ship. Most teams can put together a convincing prototype quickly. The harder part starts afterwards: shaping deterministic tools, validating behaviour with t...

If you want to start building AI applications on your own machine, the Microsoft Foundry Local Lab is one of the most useful places to begin. It is a practical workshop that takes you from first-time...

If you have been building AI agents with LangChain, you already know how powerful its tool and chain abstractions are. But when it comes to deploying those agents to production — with real infrastruc...

If you are building local AI applications, basic retrieval augmented generation is often only the starting point. This sample shows a more practical pattern: combine lexical retrieval, ONNX base...

Why your model runs great on your laptop but fails in the real world You have trained a model. It scores well on your test set. It runs fine on your development machine with a beefy GPU. Then someo...

Problem Most schools use a smart‑card‑based attendance system where students tap their cards on a reader. However, this method is unreliable because students can give their cards to friends or...

If you’re learning to build AI agents, you’ve probably hit a familiar wall: your agent can generate text, but it doesn’t actually know anything about your data. It can’t look up your documents, searc...

Introduction When building AI-powered applications on Azure, one of the most impactful decisions you make isn't about which model to use, it's about how your application selects models at runtime. ...

A practical guide to building an on-device AI support agent using Retrieval-Augmented Generation, JavaScript, and Microsoft Foundry Local. The Problem: AI That Can't Go Offline Most AI-powe...