Learn How to Build Smarter AI Agents with Microsoft’s MCP Resources Hub
If you've been curious about how to build your own AI agents that can talk to APIs, connect with tools like databases, or even follow documentation you're in the right place. Microsoft has created something called MCP, which stands for Model‑Context‑Protocol. And to help you learn it step by step, they’ve made an amazing MCP Resources Hub on GitHub. In this blog, I’ll Walk you through what MCP is, why it matters, and how to use this hub to get started, even if you're new to AI development. What is MCP (Model‑Context‑Protocol)? Think of MCP like a communication bridge between your AI model and the outside world. Normally, when we chat with AI (like ChatGPT), it only knows what’s in its training data. But with MCP, you can give your AI real-time context from: APIs Documents Databases Websites This makes your AI agent smarter and more useful just like a real developer who looks up things online, checks documentation, and queries databases. What’s Inside the MCP Resources Hub? The MCP Resources Hub is a collection of everything you need to learn MCP: Videos Blogs Code examples Here are some beginner-friendly videos that explain MCP: Title What You'll Learn VS Code Agent Mode Just Changed Everything See how VS Code and MCP build an app with AI connecting to a database and following docs. The Future of AI in VS Code Learn how MCP makes GitHub Copilot smarter with real-time tools. Build MCP Servers using Azure Functions Host your own MCP servers using Azure in C#, .NET, or TypeScript. Use APIs as Tools with MCP See how to use APIs as tools inside your AI agent. Blazor Chat App with MCP + Aspire Create a chat app powered by MCP in .NET Aspire Tip: Start with the VS Code videos if you’re just beginning. Blogs Deep Dives and How-To Guides Microsoft has also written blogs that explain MCP concepts in detail. Some of the best ones include: Build AI agent tools using remote MCP with Azure Functions: Learn how to deploy MCP servers remotely using Azure. Create an MCP Server with Azure AI Agent Service : Enables Developers to create an agent with Azure AI Agent Service and uses the model context protocol (MCP) for consumption of the agents in compatible clients (VS Code, Cursor, Claude Desktop). Vibe coding with GitHub Copilot: Agent mode and MCP support: MCP allows you to equip agent mode with the context and capabilities it needs to help you, like a USB port for intelligence. When you enter a chat prompt in agent mode within VS Code, the model can use different tools to handle tasks like understanding database schema or querying the web. Enhancing AI Integrations with MCP and Azure API Management Enhance AI integrations using MCP and Azure API Management Understanding and Mitigating Security Risks in MCP Implementations Overview of security risks and mitigation strategies for MCP implementations Protecting Against Indirect Injection Attacks in MCP Strategies to prevent indirect injection attacks in MCP implementations Microsoft Copilot Studio MCP Announcement of the Microsoft Copilot Studio MCP lab Getting started with MCP for Beginners 9 part course on MCP Client and Servers Code Repositories Try it Yourself Want to build something with MCP? Microsoft has shared open-source sample code in Python, .NET, and TypeScript: Repo Name Language Description Azure-Samples/remote-mcp-apim-functions-python Python Recommended for Secure remote hosting Sample Python Azure Functions demonstrating remote MCP integration with Azure API Management Azure-Samples/remote-mcp-functions-python Python Sample Python Azure Functions demonstrating remote MCP integration Azure-Samples/remote-mcp-functions-dotnet C# Sample .NET Azure Functions demonstrating remote MCP integration Azure-Samples/remote-mcp-functions-typescript TypeScript Sample TypeScript Azure Functions demonstrating remote MCP integration Microsoft Copilot Studio MCP TypeScript Microsoft Copilot Studio MCP lab You can clone the repo, open it in VS Code, and follow the instructions to run your own MCP server. Using MCP with the AI Toolkit in Visual Studio Code To make your MCP journey even easier, Microsoft provides the AI Toolkit for Visual Studio Code. This toolkit includes: A built-in model catalog Tools to help you deploy and run models locally Seamless integration with MCP agent tools You can install the AI Toolkit extension from the Visual Studio Code Marketplace. Once installed, it helps you: Discover and select models quickly Connect those models to MCP agents Develop and test AI workflows locally before deploying to the cloud You can explore the full documentation here: Overview of the AI Toolkit for Visual Studio Code – Microsoft Learn This is perfect for developers who want to test things on their own system without needing a cloud setup right away. Why Should You Care About MCP? Because MCP: Makes your AI tools more powerful by giving them real-time knowledge Works with GitHub Copilot, Azure, and VS Code tools you may already use Is open-source and beginner-friendly with lots of tutorials and sample code It’s the future of AI development connecting models to the real world. Final Thoughts If you're learning AI or building software agents, don’t miss this valuable MCP Resources Hub. It’s like a starter kit for building smart, connected agents with Microsoft tools. Try one video or repo today. Experiment. Learn by doing and start your journey with the MCP for Beginners curricula.A Recap of the Build AI Agents with Custom Tools Live Session
Artificial Intelligence is evolving, and so are the ways we build intelligent agents. On a recent Microsoft YouTube Live session, developers and AI enthusiasts gathered to explore the power of custom tools in AI agents using Azure AI Studio. The session walked through concepts, use cases, and a live demo that showed how integrating custom tools can bring a new level of intelligence and adaptability to your applications. 🎥 Watch the full session here: https://www.youtube.com/live/MRpExvcdxGs?si=X03wsQxQkkshEkOT What Are AI Agents with Custom Tools? AI agents are essentially smart workflows that can reason, plan, and act — powered by large language models (LLMs). While built-in tools like search, calculator, or web APIs are helpful, custom tools allow developers to tailor agents for business-specific needs. For example: Calling internal APIs Accessing private databases Triggering backend operations like ticket creation or document generation Learn Module Overview: Build Agents with Custom Tools To complement the session, Microsoft offers a self-paced Microsoft Learn module that gives step-by-step guidance: Explore the module Key Learning Objectives: Understand why and when to use custom tools in agents Learn how to define, integrate, and test tools using Azure AI Studio Build an end-to-end agent scenario using custom capabilities Hands-On Exercise: The module includes a guided lab where you: Define a tool schema Register the tool within Azure AI Studio Build an AI agent that uses your custom logic Test and validate the agent’s response Highlights from the Live Session Here are some gems from the session: Real-World Use Cases – Automating customer support, connecting to CRMs, and more Tool Manifest Creation – Learn how to describe a tool in a machine-understandable way Live Azure Demo – See exactly how to register tools and invoke them from an AI agent Tips & Troubleshooting – Best practices and common pitfalls when designing agents Want to Get Started? If you're a developer, AI enthusiast, or product builder looking to elevate your agent’s capabilities — custom tools are the next step. Start building your own AI agents by combining the power of: Microsoft Learn Module YouTube Live Session Final Thoughts The future of AI isn't just about smart responses — it's about intelligent actions. Custom tools enable your AI agent to do things, not just say things. With Azure AI Studio, building a practical, action-oriented AI assistant is more accessible than ever. Learn More and Join the Community Learn more about AI Agents with https://aka.ms/ai-agents-beginnersOpen Source Course and Building Agents. Join the Azure AI Foundry Discord Channel. Continue the discussion and learning: https://aka.ms/AI/discord Have questions or want to share what you're building? Let’s connect on LinkedIn or drop a comment under the YouTube video!Getting Started with the AI Toolkit: A Beginner’s Guide with Demos and Resources
If you're curious about building AI solutions but don’t know where to start, Microsoft’s AI Toolkit is a great place to begin. Whether you’re a student, developer, or just someone exploring AI for the first time, this toolkit helps you build real-world solutions using Microsoft’s powerful AI services. In this blog, I’ll Walk you through what the AI Toolkit is, how you can get started, and where you can find helpful demos and ready-to-use code samples. What is the AI Toolkit? The AI Toolkit is a collection of tools, templates, and sample apps that make it easier to build AI-powered applications and copilots using Microsoft Azure. With the AI Toolkit, you can: Build intelligent apps without needing deep AI expertise. Use templates and guides that show you how everything works. Quickly prototype and deploy apps with natural language, speech, search, and more. Watch the AI Toolkit in Action Microsoft has created a video playlist that covers the AI Toolkit and shows you how to build apps step-by-step. You can watch the full playlist here: It is especially useful for developers who want to bring AI into their projects, but also for beginners who want to learn by doing. AI Toolkit Playlist – https://aka.ms/AIToolkit/videos These videos help you understand the flow of building AI agents, using Azure OpenAI, and other cognitive services in a hands-on way. Explore Sample Projects on GitHub Microsoft also provides a public GitHub repository where you can find real code examples built using the AI Toolkit. Here’s the GitHub repo: AI Toolkit Samples – https://github.com/Azure-Samples/AI_Toolkit_Samples This repository includes: Sample apps using Azure AI services like OpenAI, Cognitive Search, and Speech. Instructions to deploy apps using Azure. Code that you can clone, test, and build on top of. You don’t have to start from scratch just open the code, understand the structure, and make small edits to experiment. How to Get Started Here’s a simple path if you’re just starting: Watch 2 or 3 videos from the AI Toolkit Playlist. Go to the GitHub repository and try running one of the examples. Make small changes to the code (like updating the prompt or output). Try deploying the solution on Azure by following the guide in the repo. Keep building and learning. Why This Toolkit is Worth Exploring As someone who is also learning and experimenting, I found this toolkit to be: Easy to understand, even for beginners. Focused on real-world applications, not just theory. Helpful for building responsible AI solutions with good documentation. It gives a complete picture — from writing code to deploying apps. Final Thoughts The AI Toolkit helps you start your journey in AI without feeling overwhelmed. It provides real code, real use cases, and practical demos. With the support of Microsoft Learn and Azure samples, you can go from learning to building in no time. If you’re serious about building with AI, this is a resource worth exploring. Continue the discussion in the Azure AI Foundry Discord community at Https://aka.ms/AI/discord Join the Azure AI Foundry Discord Server! References AI Toolkit Playlist (YouTube) https://aka.ms/AIToolkit/videos AI Toolkit GitHub Repository https://github.com/Azure-Samples/AI_Toolkit_Samples Microsoft Learn: AI Toolkit Documentation https://learn.microsoft.com/en-us/azure/ai-services/toolkit/ Azure AI Services https://azure.microsoft.com/en-us/products/ai-services/Model Mondays S2E01 Recap: Advanced Reasoning Session
About Model Mondays Want to know what Reasoning models are and how you can build advanced reasoning scenarios like a Deep Research agent using Azure AI Foundry? Check out this recap from Model Mondays Season 2 Ep 1. Model Mondays is a weekly series to help you build your model IQ in three steps: 1. Catch the 5-min Highlights on Monday, to get up to speed on model news 2. Catch the 15-min Spotlight on Monday, for a deep-dive into a model or tool 3. Catch the 30-min AMA on Friday, for a Q&A session with subject matter experts Want to follow along? Register Here- to watch upcoming livestreams for Season 2 Visit The Forum- to see the full AMA schedule for Season 2 Register Here - to join the AMA on Friday Jun 20 Spotlight On: Advanced Reasoning This week, the Model Mondays spotlight was on Advanced Reasoning with subject matter expert Marlene Mhangami. In this blog post, I'll talk about my five takeaways from this episode: Why Are Reasoning Models Important? What Is an Advanced Reasoning Scenario? How Can I Get Started with Reasoning Models ? Spotlight: My Aha Moment Highlights: What’s New in Azure AI 1. Why Are Reasoning Models Important? In today's fast-evolving AI landscape, it's no longer enough for models to just complete text or summarize content. We need AI that can: Understand multi-step tasks Make decisions based on logic Plan sequences of actions or queries Connect context across turns Reasoning models are large language models (LLMs) trained with reinforcement learning techniques to "think" before they answer. Rather than simply generating a response based on probability, these models follow an internal thought process producing a chain of reasoning before responding. This makes them ideal for complex problem-solving tasks. And they’re the foundation of building intelligent, context-aware agents. They enable next-gen AI workflows in everything from customer support to legal research and healthcare diagnostics. Reason: They allow AI to go beyond surface-level response and deliver solutions that reflect understanding, not just language patterning. 2. What does Advanced Reasoning involve? An advanced reasoning scenario is one where a model: Breaks a complex prompt into smaller steps Retrieves relevant external data Uses logic to connect dots Outputs a structured, reasoned answer Example: A user asks: What are the financial and operational risks of expanding a startup to Southeast Asia in 2025? This is the kind of question that requires extensive research and analysis. A reasoning model might tackle this by: Retrieving reports on Southeast Asia market conditions Breaking down risks into financial, political, and operational buckets Cross-referencing data with recent trends Returning a reasoned, multi-part answer 3. How Can I Get Started with Reasoning Models? To get started, you need to visit a catalog that has examples of these models. Try the GitHub Models Marketplace and look for the reasoning category in the filter. Try the Azure AI Foundry model catalog and look for reasoning models by name. Example: The o-series of models from Azure Open AI The DeepSeek-R1 models The Grok 3 models The Phi-4 reasoning models Next, you can use SDKs or Playground for exploring the model capabiliies. 1. Try Lab 331 - for a beginner-friendly guide. 2. Try Lab 333 - for an advanced project. 3. Try the GitHub Model Playground - to compare reasoning and GPT models. 4. Try the Deep Research Agent using LangChain - sample as a great starting project. Have questions or comments? Join the Friday AMA on Azure AI Foundry Discord: 4. Spotlight: My Aha Moment Before this session, I thought reasoning meant longer or more detailed responses. But this session helped me realize that reasoning means structured thinking — models now plan, retrieve, and respond with logic. This inspired me to think about building AI agents that go beyond chat and actually assist users like a teammate. It also made me want to dive deeper into LangChain + Azure AI workflows to build mini-agents for real-world use. 5. Highlights: What’s New in Azure AI Here’s what’s new in the Azure AI Foundry: Direct From Azure Models - Try hosted models like OpenAI GPT on PTU plans SORA Video Playground - Generate video from prompts via SORA models Grok 3 Models - Now available for secure, scalable LLM experiences DeepSeek R1-0528 - A reasoning-optimized, Microsoft-tuned open-source model These are all available in the Azure Model Catalog and can be tried with your Azure account. Did You Know? Your first step is to find the right model for your task. But what if you could have the model automatically selected for you_ based on the prompt you provide? That's the magic of Model Router a deployable AI chat model that dynamically selects the best LLM based on your prompt. Instead of choosing one model manually, the Router makes that choice in real time. Currently, this works with a fixed set of Azure OpenAI models, including a reasoning model option. Keep an eye on the documentation for more updates. Why it’s powerful: Saves cost by switching between models based on complexity Optimizes performance by selecting the right model for the task Lets you test and compare model outputs quickly Try it out in Azure AI Foundry or read more in the Model Catalog Coming Up Next Next week, we dive into Model Context Protocol, an open protocol that empowers agentic AI applications by making it easier to discover and integrate knowledge and action tools with your model choices. Register Here to get reminded - and join us live on Monday! Join The Community Great devs don't build alone! In a fast-pased developer ecosystem, there's no time to hunt for help. That's why we have the Azure AI Developer Community. Join us today and let's journey together! Join the Discord - for real-time chats, events & learning Explore the Forum - for AMA recaps, Q&A, and help! About Me. I'm Sharda, a Gold Microsoft Learn Student Ambassador interested in cloud and AI. Find me on Github, Dev.to,, Tech Community and Linkedin. In this blog series I have summarizef my takeaways from this week's Model Mondays livestream .Model Mondays S2E11: Exploring Speech AI in Azure AI Foundry
1. Weekly Highlights This week’s top news in the Azure AI ecosystem included: Lakuna — Copilot Studio Agent for Product Teams: A hackathon project built with Copilot Studio and Azure AI Foundry, Lakuna analyzes your requirements and docs to surface hidden assumptions, helping teams reflect, test, and reduce bias in product planning. Azure ND H200 v5 VMs for AI: Azure Machine Learning introduced ND H200 v5 VMs, featuring NVIDIA H200 GPUs (over 1TB GPU memory per VM!) for massive models, bigger context windows, and ultra-fast throughput. Agent Factory Blog Series: The next wave of agentic AI is about extensibility: plug your agents into hundreds of APIs and services using Model Connector Protocol (MCP) for portable, reusable tool integrations. GPT-5 Tool Calling on Azure AI Foundry: GPT-5 models now support free-form tool calling—no more rigid JSON! Output SQL, Python, configs, and more in your preferred format for natural, flexible workflows. Microsoft a Leader in 2025 Gartner Magic Quadrant: Azure was again named a leader for Cloud Native Application Platforms—validating its end-to-end runway for AI, microservices, DevOps, and more. 2. Spotlight On: Azure AI Foundry Speech Playground The main segment featured a live demo of the new Azure AI Speech Playground (now part of Foundry), showing how developers can experiment with and deploy cutting-edge voice, transcription, and avatar capabilities. Key Features & Demos: Speech Recognition (Speech-to-Text): Try real-time transcription directly in the playground—recognizing natural speech, pauses, accents, and domain terms. Batch and Fast transcription options for large files and blob storage. Custom Speech: Fine-tune models for your industry, vocabulary, and noise conditions. Text to Speech (TTS): Instantly convert text into natural, expressive audio in 150+ languages with 600+ neural voices. Demo: Listen to pre-built voices, explore whispering, cheerful, angry, and more styles. Custom Neural Voice: Clone and train your own professional or personal voice (with strict Responsible AI controls). Avatars & Video Translation: Bring your apps to life with prebuilt avatars and video translation, which syncs voice-overs to speakers in multilingual videos. Voice Live API: Voice Live API (Preview) integrates all premium speech capabilities with large language models, enabling real-time, proactive voice agents and chatbots. Demo: Language learning agent with voice, avatars, and proactive engagement. One-click code export for deployment in your IDE. 3. Customer Story: Hilo Health This week’s customer spotlight featured Helo Health—a healthcare technology company using Azure AI to boost efficiency for doctors, staff, and patients. How Hilo Uses Azure AI: Document Management: Automates fax/document filing, splits multi-page faxes by patient, reduces staff effort and errors using Azure Computer Vision and Document Intelligence. Ambient Listening: Ambient clinical note transcription captures doctor-patient conversations and summarizes them for easy EHR documentation. Genie AI Contact Center: Agentic voice assistants handle patient calls, book appointments, answer billing/refill questions, escalate to humans, and assist human agents—using Azure Communication Services, Azure Functions, FastAPI (community), and Azure OpenAI. Conversational Campaigns: Outbound reminders, procedure preps, and follow-ups all handled by voice AI—freeing up human staff. Impact: Hilo reaches 16,000+ physician practices and 180,000 providers, automates millions of communications, and processes $2B+ in payments annually—demonstrating how multimodal AI transforms patient journeys from first call to post-visit care. 4. Key Takeaways Here’s what you need to know from S2E11: Speech AI is Accessible: The Azure AI Foundry Speech Playground makes experimenting with voice recognition, TTS, and avatars easy for everyone. From Playground to Production: Fine-tune, export code, and deploy speech models in your own apps with Azure Speech Service. Responsible AI Built-In: Custom Neural Voice and avatars require application and approval, ensuring ethical, secure use. Agentic AI Everywhere: Voice Live API brings real-time, multimodal voice agents to any workflow. Healthcare Example: Hilo’s use of Azure AI shows the real-world impact of speech and agentic AI, from patient intake to after-visit care. Join the Community: Keep learning and building—join the Discord and Forum. Sharda's Tips: How I Wrote This Blog I organize key moments from each episode, highlight product demos and customer stories, and use GitHub Copilot for structure. For this recap, I tested the Speech Playground myself, explored the docs, and summarized answers to common developer questions on security, dialects, and deployment. Here’s my favorite Copilot prompt this week: "Generate a technical blog post for Model Mondays S2E11 based on the transcript and episode details. Focus on Azure Speech Playground, TTS, avatars, Voice Live API, and healthcare use cases. Add practical links for developers and students!" Coming Up Next Week Next week: Observability! Learn how to monitor, evaluate, and debug your AI models and workflows using Azure and OpenAI tools. Register For The Livestream – Sep 1, 2025 Register For The AMA – Sep 5, 2025 Ask Questions & View Recaps – Discussion Forum About Model Mondays Model Mondays is your weekly Azure AI learning series: 5-Minute Highlights: Latest AI news and product updates 15-Minute Spotlight: Demos and deep dives with product teams 30-Minute AMA Fridays: Ask anything in Discord or the forum Start building: Register For Livestreams Watch Past Replays Register For AMA Recap Past AMAs Join The Community Don’t build alone! The Azure AI Developer Community is here for real-time chats, events, and support: Join the Discord Explore the Forum About Me I'm Sharda, a Gold Microsoft Learn Student Ambassador focused on cloud and AI. Find me on GitHub, Dev.to, Tech Community, and LinkedIn. In this blog series, I share takeaways from each week’s Model Mondays livestream.Model Mondays S2E12: Models & Observability
1. Weekly Highlights This week’s top news in the Azure AI ecosystem included: GPT Real Time (GA): Azure AI Foundry now offers GPT Real Time (GA)—lifelike voices, improved instruction following, audio fidelity, and function calling, with support for image context and lower pricing. Read the announcement and check out the model card for more details. Azure AI Translator API (Public Preview): Choose between fast Neural Machine Translation (NMT) or nuanced LLM-powered translations, with real-time flexibility for multilingual workflows. Read the announcement then check out the Azure AI Translator documentation for more details. Azure AI Foundry Agents Learning Plan: Build agents with autonomous goal pursuit, memory, collaboration, and deep fine-tuning (SFT, RFT, DPO) - on Azure AI Foundry. Read the announcement what Agentic AI involves - then follow this comprehensive learning plan with step-by-step guidance. CalcLM Agent Grid (Azure AI Foundry Labs): Project CalcLM: Agent Grid is a prototype and open-source experiment that illustrates how agents might live in a grid-like surface (like Excel). It's formula-first and lightweight - defining agentic workflows like calculations. Try the prototype and visit Foundry Labs to learn more. Agent Factory Blog: Observability in Agentic AI: Agentic AI tools and workflows are gaining rapid adoption in the enterprise. But delivering safe, reliable and performant agents requires foundation support for Observability. Read the 6-part Agent Factory series and check out the Top 5 agent observability best practices for reliable AI blog post for more details. 2. Spotlight On: Observability in Azure AI Foundry This week’s spotlight featured a deep dive and demo by Han Che (Senior PM, Core AI/ Microsoft ), showing observability end-to-end for agent workflows. Why Observability? Ensures AI quality, performance, and safety throughout the development lifecycle. Enables monitoring, root cause analysis, optimization, and governance for agents and models. Key Features & Demos: Development Lifecycle: Leaderboard: Pick the best model for your agent with real-time evaluation. Playground: Chat and prototype agents, view instant quality and safety metrics. Evaluators: Assess quality, risk, safety, intent resolution, tool accuracy, code vulnerability, and custom metrics. Governance: Integrate with partners like Cradle AI and SideDot for policy mapping and evidence archiving. Red Teaming Agent: Automatically test for vulnerabilities and unsafe behavior. CI/CD Integration: Automate evaluation in GitHub Actions and Azure DevOps pipelines. Azure DevOps GitHub Actions Monitoring Dashboard: Resource usage, application analytics, input/output tokens, request latency, cost breakdown, and evaluation scores. Azure Cost Management SDKs & Local Evaluation: Run evaluations locally or in the cloud with the Azure AI Evaluation SDK. Demo Highlights: Chat with a travel planning agent, view run metrics and tool usage. Drill into run details, debugging, and real-time safety/quality scores. Configure and run large-scale agent evaluations in CI/CD pipelines. Compare agents, review statistical analysis, and monitor in production dashboards 3. Customer Story: Saifr Saifr is a RegTech company that uses artificial intelligence to streamline compliance for marketing, communications, and creative teams in regulated industries. Incubated at Fidelity Labs (Fidelity Investments’ innovation arm), Saifr helps enterprises create, review, and approve content that meets regulatory standards—faster and with less manual effort. What Saifr Offers AI-Powered Compliance: Saifr’s platform leverages proprietary AI models trained on decades of regulatory expertise to automatically detect potential compliance risks in text, images, audio, and video. Automated Guardrails: The solution flags risky or non-compliant language, suggests compliant alternatives, and provides explanations—all in real time. Workflow Integration: Saifr seamlessly integrates with enterprise content creation and approval workflows, including cloud platforms and agentic AI systems like Azure AI Foundry. Multimodal Support: Goes beyond text to check images, videos, and audio for compliance risks, supporting modern marketing and communications teams. 4. Key Takeaways Observability is Essential: Azure AI Foundry offers complete monitoring, evaluation, tracing, and governance for agentic AI—making production safe, reliable, and compliant. Built-In Evaluation and Red Teaming: Use leaderboards, evaluators, and red teaming agents to assess and continuously improve model safety and quality. CI/CD and Dashboard Integration: Automate evaluations in GitHub Actions or Azure DevOps, then monitor and optimize agents in production with detailed dashboards. Compliance Made Easy: Safer’s agents and models help financial services and regulated industries proactively meet compliance standards for content and communications. Sharda's Tips: How I Wrote This Blog I focus on organizing highlights, summarizing customer stories, and linking to official Microsoft docs and real working resources. For this recap, I explored the Azure AI Foundry Observability docs, tested CI/CD pipeline integration, and watched the customer demo to share best practices for regulated industries. Here’s my Copilot prompt for this episode: "Generate a technical blog post for Model Mondays S2E12 based on the transcript and episode details. Focus on observability, agent dashboards, CI/CD, compliance, and customer stories. Add correct, working Microsoft links!" Coming Up Next Week Next week: Open Source Models! Join us for the final episode with Hugging Face VP of Product, live demos, and open model workflows. Register For The Livestream – Sep 15, 2025 About Model Mondays Model Mondays is your weekly Azure AI learning series: 5-Minute Highlights: Latest AI news and product updates 15-Minute Spotlight: Demos and deep dives with product teams 30-Minute AMA Fridays: Ask anything in Discord or the forum Start building: Watch Past Replays Register For AMA Recap Past AMAs Join The Community Don’t build alone! The Azure AI Developer Community is here for real-time chats, events, and support: Join the Discord Explore the Forum About Me I'm Sharda, a Gold Microsoft Learn Student Ambassador focused on cloud and AI. Find me on GitHub, Dev.to, Tech Community, and LinkedIn. In this blog series, I share takeaways from each week’s Model Mondays livestream.Model Mondays S2E13: Open Source Models (Hugging Face)
1. Weekly Highlights 1. Weekly Highlights Here are the key updates we covered in the Season 2 finale: O1 Mini Reinforcement Fine-Tuning (GA): Fine-tune models with as few as ~100 samples using built-in Python code graders. Azure Live Interpreter API (Preview): Real-time speech-to-speech translation supporting 76 input languages and 143 locales with near human-level latency. Agent Factory – Part 5: Connecting agents using open standards like MCP (Model Context Protocol) and A2A (Agent-to-Agent protocol). Ask Ralph by Ralph Lauren: A retail example of agentic AI for conversational styling assistance, built on Azure OpenAI and Foundry’s agentic toolset. VS Code August Release: Brings auto-model selection, stronger safety guards for sensitive edits, and improved agent workflows through new agents.md support. 2. Spotlight – Open Source Models in Azure AI Foundry Guest: Jeff Boudier, VP of Product at Hugging Face Jeff showcased the deep integration between the Hugging Face community and Azure AI Foundry, where developers can access over 10 000 open-source models across multiple modalities—LLMs, speech recognition, computer vision, and even specialized domains like protein modeling and robotics. Demo Highlights Discover models through Azure AI Foundry’s task-based catalog filters. Deploy directly from Hugging Face Hub to Azure with one-click deployment. Explore Use Cases such as multilingual speech recognition and vision-language-action models for robotics. Jeff also highlighted notable models, including: SmoLM3 – a 3 B-parameter model with hybrid reasoning capabilities Qwen 3 Coder – a mixture-of-experts model optimized for coding tasks Parakeet ASR – multilingual speech recognition Microsoft Research protein-modeling collection MAGMA – a vision-language-action model for robotics Integration extends beyond deployment to programmatic access through the Azure CLI and Python SDKs, plus local development via new VS Code extensions. 3. Customer Story – DraftWise (BUILD 2025 Segment) The finale featured a customer spotlight on DraftWise, where CEO James Ding shared how the company accelerates contract drafting with Azure AI Foundry. Problem Legal contract drafting is time-consuming and error-prone. Solution DraftWise uses Azure AI Foundry to fine-tune Hugging Face language models on legal data, generating contract drafts and redline suggestions. Impact Faster drafting cycles and higher consistency Easy model management and deployment with Foundry’s secure workflows Transparent evaluation for legal compliance 4. Community Story – Hugging Face & Microsoft The episode also celebrated the ongoing collaboration between Hugging Face and Microsoft and the impact of open-source AI on the global developer ecosystem. Community Benefits Access to State-of-the-Art Models without licensing barriers Transparent Performance through public leaderboards and benchmarks Rapid Innovation as improvements and bug fixes spread quickly Education & Empowerment via tutorials, docs, and active forums Responsible AI Practices encouraged through community oversight 5. Key Takeaways Open Source AI Is Here to Stay Azure AI Foundry and Hugging Face make deploying, fine-tuning, and benchmarking open models easier than ever. Community Drives Innovation: Collaboration accelerates progress, improves transparency, and makes AI accessible to everyone. Responsible AI and Transparency: Open-source models come with clear documentation, licensing, and community-driven best practices. Easy Deployment & Customization: Azure AI Foundry lets you deploy, automate, and customize open models from a single, unified platform. Learn, Build, Share: The open-model ecosystem is a great place for students, developers, and researchers to learn, build, and share their work. Sharda's Tips: How I Wrote This Blog For this final recap, I focused on capturing the energy of the open source AI movement and the practical impact of Hugging Face and Azure AI Foundry collaboration. I watched the livestream, took notes on the demos and interviews, and linked directly to official resources for models, docs, and community sites. Here’s my Copilot prompt for this episode: "Generate a technical blog post for Model Mondays S2E13 based on the transcript and episode details. Focus on open source models, Hugging Face, Azure AI Foundry, and community workflows. Include practical links and actionable insights for developers and students! Learn & Connect Explore Open Models in Azure AI Foundry Hugging Face Leaderboard Responsible AI in Azure Machine Learning Llama-3 by Meta Hugging Face Community Azure AI Documentation About Model Mondays Model Mondays is your weekly Azure AI learning series: 5-Minute Highlights: Latest AI news and product updates 15-Minute Spotlight: Demos and deep dives with product teams 30-Minute AMA Fridays: Ask anything in Discord or the forum Start building: Watch Past Replays Register For AMA Recap Past AMAs Join The Community Don’t build alone! The Azure AI Developer Community is here for real-time chats, events, and support: Join the Discord Explore the Forum About Me I'm Sharda, a Gold Microsoft Learn Student Ambassador focused on cloud and AI. Find me on GitHub, Dev.to, Tech Community, and LinkedIn. In this blog series, I share takeaways from each week’s Model Mondays livestream.Model Mondays S2:E7 · AI-Assisted Azure Development
Welcome to Episode 7! This week, we explore how AI is transforming Azure development. We’ll break down two key tools—Azure MCP Server and GitHub Copilot for Azure—and see how they make working with Azure resources easier for everyone. We’ll also look at a real customer story from SightMachine, showing how AI streamlines manufacturing operations.Model Mondays S2E8: On-Device & Local AI
Model Mondays S2E8: On-Device & Local AI Welcome to Episode 8! This week, we explored how AI is moving from the cloud to your own device, making it faster, more private, and more accessible. We also saw a real-world customer story from Xander Glasses, showing how AI can help people with hearing loss. RFD Observability tools in Azure AI Foundry: Real-time model telemetry, auto evals, quick evals, Python grader. GitHub Copilot Pro with Spark: AI pair programmer for code explanation and workflow suggestions. Synthetic Data for Vision Models: Training accurate models with procedurally generated data. Agent-Friendly Websites: Making sites accessible to AI agents via APIs, semantic markup, and OpenAPI specs. MCP (Model Context Protocol): Standardizing agent memory and context for scalable AI.Model Mondays S2E10: Automating Document Processing with AI
1. Weekly Highlights We kicked off with the top news and updates in the Azure AI ecosystem: Agent Factory Blog Series: A new 6-part blog series on designing reliable, agentic AI—exploring multi-step, collaborative agents that reflect, plan, and adapt using tool integrations and design patterns. Text PII Preview in Azure AI Language: Now redacts PII (like date of birth, license plates) in major European languages, with better accuracy for UK bank entities. Claude Opus 4.1 in Copilot Pro & Enterprise: Public preview brings smarter summaries, tool assistant thinking, and "Ask Mode" in VS Code.Now leverages stronger computer vision algorithms for table parsing—achieving 94-97% accuracy across Latin, Chinese, Japanese, and Korean—with sub-10ms latency. Mistral Document AI in Azure Foundry: Instantly turn PDFs, contracts, and scanned docs into structured JSON with tables, headings, and LaTeX support. Serverless, multilingual, secure, and perfect for regulated industries. 2. Spotlight On: Document Intelligence with Azure & Mistral This week’s spotlight was a hands-on exploration of document processing, featuring both Microsoft and Mistral AI experts. Why Document Processing? Unstructured data—receipts, forms, handwritten notes—are everywhere. Modern document AI can extract, structure, and even annotate this data, fueling everything from search to RAG pipelines. Azure Document Intelligence: State-of-the-art OCR and table extraction with super-high accuracy and speed. Handles multi-language, complex layouts, and returns structured outputs ready for programmatic use. Mistral Document AI: Transforms PDFs and scanned docs into JSON, retaining complex formatting, tables, images, and even LaTeX. Supports custom schema extraction, image/document annotations, and returns everything in one API call. Integrates seamlessly with Azure AI Foundry and developer workflows. Demo Highlights: Extracting Receipts: OCR accurately pulls out store, date, and transaction details from photos. Handwriting Recognition: Even historical documents (like Thomas Jefferson’s letters) are parsed with surprising accuracy. Tables & Structured Data: Financial statements and reports converted into structured markdown and JSON—ready for downstream apps. Advanced Annotations: Define your own schema (via JSON Schema or Pydantic), extract custom fields, classify images, summarize documents, and even translate summaries—all in a single call. 3. Customer Story: Oracle Health Oracle Health shared how agentic AI and fine-tuned models are revolutionizing clinical workflows: Problem: Clinicians spend hours on documentation, searching records, and manual data entry—reducing time for patient care. Solution: Oracle’s clinical AI agents automate chart reviews, data extraction, and even conversational Q&A—while keeping humans in the loop for safety. Technical Highlights: Multi-agent architecture understands provider specialty and context. Orchestrator model "routes" requests to the right agent or plugin, extracting needed arguments from context. Fine-tuning was key: For low latency, Oracle used lightweight models (like GPT-4 Mini) and fine-tuned on their data—achieving sub-800ms responses, with accuracy matching larger models. Fine-tuning also allowed for nuanced tool selection, argument extraction, and rule-based orchestration—better than prompt engineering alone. Used LoRA for efficient, targeted fine-tuning without erasing base model knowledge. Live Demo: Agent summarizes patient history, retrieves lab results, filters for abnormals, and answers follow-up questions—all conversationally. Fine-tuned orchestrator chooses the right tool and context for each doctor’s workflow. Result: 1-2 hours saved per day, more time for patients, and happier doctors! 4. Key Takeaways Here are the key learnings from this episode: Document AI is Production-Ready: Azure Document Intelligence and Mistral Document AI offer fast, accurate, and customizable document parsing for real enterprise needs. Schema-Driven Extraction & Annotation: Define your own schemas and extract exactly what you want—no more one-size-fits-all. Fine-Tuning Unlocks Performance: For low latency and high accuracy, fine-tuning lightweight models beats prompt engineering in complex, rule-based agent workflows. Agentic Workflows in Action: Multi-agent systems can automate complex tasks, route requests, and keep humans in control, especially in regulated domains like healthcare. Community & Support: Join the Discord and Forum to ask questions, share use cases, and connect with the team. Sharda's Tips: How I Wrote This Blog Writing this recap is all about sharing what I learned and making it practical for the community! I start by organizing the key highlights, then walk through customer stories and demos, using simple language and real-world examples. Copilot helps me structure and clarify my notes, especially when summarizing technical sections. Here’s the prompt I used for Copilot this week: "Generate a technical blog post for Model Mondays S2E10 based on the transcript and episode details. Focus on document processing with Azure AI and Mistral, include customer demos, and highlight practical workflows and fine-tuning. Make it clear and approachable for developers and students." Every episode inspires me to try these tools myself, and I hope this blog makes it easy for you to start, too. If you have questions or want to share your own experience, I’d love to hear from you! Coming Up Next Week Next week: Text & Speech AI Playgrounds! Learn how to build and test language and speech models, with live demos and expert guests. | Register For The Livestream – Aug 25, 2025 | Register For The AMA – Aug 29, 2025 | Ask Questions & View Recaps – Discussion Forum About Model Mondays Model Mondays is a weekly series to build your Azure AI IQ with: 5-Minute Highlights: News & updates on Mondays 15-Minute Spotlight: Deep dives into new features, models, and protocols 30-Minute AMA Fridays: Live Q&A with product teams and experts Get started: Register For Livestreams Watch Past Replays Register For AMA Recap Past AMAs Join The Community Don’t build alone! Join the Azure AI Developer Community for real-time chats, events, support, and more: Join the Discord Explore the Forum About Me I'm Sharda, a Gold Microsoft Learn Student Ambassador focused on cloud and AI. Find me on GitHub, Dev.to, Tech Community, and LinkedIn. In this blog series, I share takeaways from each week’s Model Mondays livestream.
