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Why Foundry Is a Big Deal AI is moving fast, and Microsoft Foundry is one of the biggest updates you should know about. It’s not just another platform—it’s a new way to build AI agents that can actually do things. Instead of being limited to answering questions, these agents can plan, reason, connect to apps, and pull knowledge from your company’s data. If you’ve ever wished your chatbot could act more like a teammate, Foundry is where that shift happens. Agents: Smarter Than Assistants Traditional assistants were reactive—they waited for you to ask something. Foundry agents are proactive. They can: - Understand intent beyond keywords. - Plug into tools and apps. - Pull info from knowledge bases like Foundry IQ . - Execute tasks without constant supervision. Think of them as digital interns who never get tired and don’t need coffee breaks. Tools: Giving Agents Superpowers Tools are what make agents useful. They’re the “hands” that let agents interact with the world. With Foundry Tools...

In this full tutorial, I’ll walk you through the complete workflow of creating an MCP (Model Context Protocol) server, deploying it on Azure App Service, and integrating it with AI agents using the AI Toolkit in Visual Studio Code. Whether you're a developer, AI enthusiast, or cloud architect, this video covers everything you need to: ✅ Build your MCP server ✅ Deploy it to Azure App Service  ✅ Secure and scale your server for production ✅ Connect it to AI agents for real-time tool invocation ✅ Troubleshoot, optimize, and monitor your deployment   If you're more interested in reading, then here is my detailed blog on MEDIUM

Big news in the AI world! Azure AI Foundry has officially been rebranded as Microsoft Foundry. This isn’t just a name change — it’s a shift in how developers and enterprises will build, deploy, and scale AI. In my latest video, I break down: 🔄 What’s new in Microsoft Foundry ❌ What’s been retired or changed 💡 Why these updates matter for your workflows 👉 Watch the full breakdown here: If you’re working with AI agents, cloud workflows, or enterprise integrations, this update is one you don’t want to miss. Alternatively, if you're more interested in reading, then here is my detailed blog on MEDIUM .

 🎬 Watch the Magic Happen — In Just Minutes Curious how you can turn plain English into a full-blown architecture diagram? In my latest video, I show you exactly how to auto-generate cloud diagrams using natural language and LLMs — no Visio, no manual layout, just smart markdown and AI. You’ll see: - How to describe your system in instructions.md - How the LLM interprets and builds the diagram - How to visualize Azure components, workflows, and tiers instantly 👉 Watch now and see how this technique can save you hours and make your documentation smarter: Auto-Generating Architecture Diagrams Using LLMs Once you try it, you’ll never diagram the old way again. If you're more interested in reading then here is my detailed blog: MEDIUM

Ever built an AI agent and thought,  “Wait… I don’t want it to actually run that tool yet”?  That’s where dry-run agents come in — and in this video, I show you exactly how to build one using the Microsoft Agent Framework. You’ll learn how to simulate tool usage without executing anything. It’s safe, smart, and perfect for testing workflows, debugging logic, or getting human approval before action. Whether you're a dev, a student, or just curious how AI agents “think before they act,” this tutorial breaks it down step-by-step — with real code, visuals, and a fun twist. 👉 Watch now and see how dry-run agents can transform your AI workflows — safely and brilliantly.

Ever wondered if your plain old Python function could do something smarter? Like… actually respond to prompts, act like a tool, and be part of an AI agent? It can. And I’ll show you how. 🎥 Watch the full video here In my latest YouTube demo, I take a simple Python function—generate_guid()—and turn it into a fully callable AI tool using the Microsoft Agent Framework. No LLMs. No fluff. Just clean, modular Python wrapped in something powerful. 🧠 What You’ll Learn: - How to wrap any Python function using FunctionTool - How to register it with an agent - How to trigger it with natural language (yes, really!) ⚡ Why You Should Watch: If you’re a developer, content creator, or just curious about AI agents, this is the fastest way to get started. You’ll see how to: - Build smarter tools with less code - Keep full control over logic - Scale your agent workflows without the LLM overhead 👉 Ready to see it in action? Click here to watch the video now and let me know what tool you’d build next!

If you’ve ever talked to a chatbot or used an AI assistant to answer a question, there’s a good chance it used something called RAG or was powered by AI agents behind the scenes. But what are these things, and how are they different? Let’s break it down in a way that’s super easy to understand. 🛠️✨ 📚 What is RAG? RAG stands for Retrieval-Augmented Generation . Think of it like this: Imagine you’re doing a school project on volcanoes. You know a little, but instead of guessing answers, you Google it first, grab info from a few trusted websites, and then write your project in your own words. That’s what RAG does: It retrieves useful information from a database or search system. Then it generates a response based on what it found. It’s like a super-smart librarian + writer combo! 📖✍️ 📌 Perfect for: Answering questions based on a LOT of documents (like customer support FAQs or legal documents). 🕹️ What is an AI Agent? An AI Agent is like a digital helper that can think, plan, and eve...