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If you have spent any time building AI agents, you have probably run into this moment: you tell your agent something important, it responds perfectly, and then one message later it acts like the conversation never happened. Blank slate. No memory. Total amnesia. This is not a quirk. It is how large language models work by design. Every call to the model is stateless. Without explicit history being passed back each time, the model starts fresh on every turn. Context Providers are how the Microsoft Agent Framework solves this — and they do a lot more than just memory. What Is a Context Provider? A Context Provider is a class that wraps around every LLM call your agent makes. It gets two hooks: one before the call and one after. Before the call fires, the provider can inject anything into the conversation — previous messages, today's menu, user preferences, business rules, tool definitions. The LLM sees all of it before generating a response. After the call fires, the provider can ext...

Most AI systems can read your documents — but very few can understand them. Traditional RAG pulls text chunks and hopes the answer makes sense. It works for simple questions, but the moment you ask something relational —  Who works with whom? How are these teams connected? Why does this project matter? — it breaks. Graph RAG fixes that by turning your documents into a knowledge graph. People become nodes. Teams become nodes. Projects, locations, responsibilities — all nodes. And the relationships between them become edges. Suddenly, your data isn’t flat anymore. It’s connected. Graph RAG doesn’t just retrieve text. It retrieves meaning. It understands relationships, clusters related entities into communities, and uses LLM ‑generated summaries to explain what each group represents. So when you ask a complex question, the system doesn’t guess — it reasons. If you want to see this visually — with examples, and a full breakdown of how the pipeline works — I’ve created a detailed vide...

Are you trying to build an app with Claude AI but not getting the results you want? Your prompts might be the problem — not Claude . Introducing App Brief Wizard — a simple, offline HTML tool that helps you generate precise, structured Claude prompts for any app idea. Answer 5 quick questions → get a tailored prompt → instantly better UI and app concepts. No installs. No accounts. No learning curve. Just better prompts — instantly. 🎯 Perfect for:   Freelancers • Designers • Product Managers • Entrepreneurs • AI Builders • Claude Users 💡 Why it works:   App Brief Wizard helps Claude understand your app’s purpose, audience, and design personality — so your outputs look professional, not generic. 🔥 Early Bird Offer:   First few copies are just $5 (use code FIRST10) After that, it’s $9 — grab yours before it’s gone! 👉 Get App Brief Wizard here: App Brief

If you’re anything like me, you probably spend a big part of your day typing — emails, stories, books, blogs, notes, messages, ideas, reminders… it never ends. And honestly, it gets tiring. Our hands get tired. Our minds get tired. And sometimes, typing just slows us down. That’s when I realized something simple:   Talking is easier than typing. We speak faster, more naturally, and with less effort. But here’s the problem — most voice‑to‑text tools send your voice to the cloud.   Some store it.  Some analyze it.   Some require monthly subscriptions.   And many people don’t feel comfortable with that.  So, I decided to build something different. Meet Speakly — A Private, Offline Voice‑to‑Text App for Windows Speakly is a small, simple tool that lets you talk , and your computer types everything for you .   But the best part is what it doesn’t  do: It doesn’t send your voice to the internet   It doesn’t store your...

I’m excited to share my most awaited video, where I break down how to build an MCP server completely from scratch using FastAPI —with no SDKs, no decorators, and absolutely no hidden magic.  This tutorial walks through the real MCP protocol step‑by‑step. You’ll see how the manifest, tools list, execution flow, and even streaming responses come together using pure Python. It’s a simple, transparent way to understand MCP at its core. 🎥 Watch the full video: 

Over the past few weeks, I’ve been exploring how to build practical, privacy‑first agentic AI workflows that run entirely on a local machine. In my latest project, I combined GitHub Copilot SDK with Foundry Local to create a fully offline agent capable of choosing and executing tools intelligently — without relying on any cloud model. In this demo, I walk through how I built: - A Foundry Local LLM tool for on‑device inference - Three lightweight Python tools  - A router prompt that lets Copilot SDK decide which tool to invoke - A clean async loop that ties everything together The result is a flexible, extensible agent that can reason, select tools, and produce polished answers — all running locally. If you’re interested in agent design, local LLMs, or practical orchestration patterns, this walkthrough will give you a clear, end‑to‑end example you can adapt to your own projects. 🎥 Watch the full video here:

If you’ve ever wanted to run an LLM directly on your laptop without relying on the cloud, this new video is for you. I just released a hands‑on walkthrough of Foundry Local, where I show you exactly how to download an AI model to your machine and use it completely offline. In the video, I break down both methods developers use: - CLI workflow — install Foundry Local, pull a model, and run inference offline - Python SDK workflow — load the model in your code and build real offline AI features Whether you're a developer, an AI enthusiast, or someone who wants more privacy and zero token costs, this tutorial will help you get started in minutes.

Every once in a while, a new AI tool appears that doesn’t just improve on what already exists — it completely changes your expectations. That’s exactly what happened when I tested Awaz AI , a voice agent designed to handle real phone calls with natural, human‑like conversation. I’ve tried many voice systems before, and most of them sound robotic, interrupt at the wrong time, or fall apart when you ask something unexpected. But Awaz AI surprised me from the very first “hello.” The pacing, the tone, the timing — everything felt unusually natural. It didn’t rush. It didn’t freeze. It didn’t sound scripted. It actually felt like a real conversation. To make sure I wasn’t imagining it, I recorded the entire interaction. No edits. No retakes. Just a raw, real phone call between me and the Awaz AI agent. If you’re curious about how far voice AI has come — or if you simply want to hear an AI that sounds more human than most customer service lines — you should watch this demo. It’s one of the ...

AI workflows were a great starting point. They helped us build early prototypes, automate simple tasks, and experiment with LLMs. But the future of AI is not workflows - it's agents. Agents are more flexible, more intelligent, and more aligned with how real‑world tasks work. If you want to understand this shift - and learn exactly how to convert your existing workflows into agents - my video will walk you through the entire process step by step. Watch it. Learn it. Build with it. Your future AI systems will thank you.

If you’ve been exploring Microsoft Foundry and wondering how to actually run those declarative agent workflows you keep hearing about… this video is for you. I break down exactly how to view, run, and test declarative workflows inside VS Code — no fluff, just practical steps. You’ll learn how to open workflow files, understand agent logic, and test everything with real-world examples. Whether you're building multi-agent systems or just curious about how Foundry works, this tutorial will help you get hands-on fast. 🎯 What’s inside the video: - How to open and explore workflows in VS Code - Running workflows with Microsoft Foundry - Testing agent logic and human-in-the-loop steps - Tips for debugging and refining your setup 👉 Watch now: Happy learning!

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...

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 .

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!