Week 3 . Microsoft Agents Hack Online Events and Readiness Resources
Readiness and skilling events for Week 3: Microsoft AI Agents Hack Register Now at https://aka.ms/agentshack https://aka.ms/agentshack 2025 is the year of AI agents! But what exactly is an agent, and how can you build one? Whether you're a seasoned developer or just starting out, this FREE three-week virtual hackathon is your chance to dive deep into AI agent development. Register Now: https://aka.ms/agentshack 🔥 Learn from expert-led sessions streamed live on YouTube, covering top frameworks like Semantic Kernel, Autogen, the new Azure AI Agents SDK and the Microsoft 365 Agents SDK. Week 3: April 21st-25th LIVE & ONDEMAND Day/Time Topic Track 4/21 12:00 PM PT Knowledge-augmented agents with LlamaIndex.TS JS 4/22 06:00 AM PT Building a AI Agent with Prompty and Azure AI Foundry Python 4/22 09:00 AM PT Real-time Multi-Agent LLM solutions with SignalR, gRPC, and HTTP based on Semantic Kernel C# 4/22 10:30 AM PT Learn Live: Fundamentals of AI agents on Azure - 4/22 12:00 PM PT Demystifying Agents: Building an AI Agent from Scratch on Your Own Data using Azure SQL C# 4/22 03:00 PM PT VoiceRAG: talk to your data Python 4/23 09:00 AM PT Building Multi-Agent Apps on top of Azure PostgreSQL Python 4/23 12:00 PM PT Agentic RAG with reflection Python 4/23 03:00 PM PT Multi-source data patterns for modern RAG apps C# 4/24 06:00 AM PT Engineering agents that Think, Act, and Govern themselves C# 4/24 09:00 AM PT Extending AI Agents with Azure Functions Python, C# 4/24 12:00 PM PT Build real time voice agents with Azure Communication Services Python 🌟 Join the Conversation on Azure AI Foundry Discussions! 🌟 Have ideas, questions, or insights about AI? Don't keep them to yourself! Share your thoughts, engage with experts, and connect with a community that’s shaping the future of artificial intelligence. 🧠✨ 👉 Click here to join the discussion!Week 2 . Microsoft Agents Hack Online Events and Readiness Resources
https://aka.ms/agentshack 2025 is the year of AI agents! But what exactly is an agent, and how can you build one? Whether you're a seasoned developer or just starting out, this FREE three-week virtual hackathon is your chance to dive deep into AI agent development. Register Now: https://aka.ms/agentshack 🔥 Learn from expert-led sessions streamed live on YouTube, covering top frameworks like Semantic Kernel, Autogen, the new Azure AI Agents SDK and the Microsoft 365 Agents SDK. Week 2 Events: April 14th-18th Day/Time Topic Track 4/14 08:00 AM PT Building custom engine agents with Azure AI Foundry and Visual Studio Code Copilots 4/15 07:00 AM PT Your first AI Agent in JS with Azure AI Agent Service JS 4/15 09:00 AM PT Building Agentic Applications with AutoGen v0.4 Python 4/15 12:00 PM PT AI Agents + .NET Aspire C# 4/15 03:00 PM PT Prototyping AI Agents with GitHub Models Python 4/16 04:00 AM PT Multi-agent AI apps with Semantic Kernel and Azure Cosmos DB C# 4/16 06:00 AM PT Building declarative agents with Microsoft Copilot Studio & Teams Toolkit Copilots 4/16 07:00 AM PT Prompting is the New Scripting: Meet GenAIScript JS 4/16 09:00 AM PT Building agents with an army of models from the Azure AI model catalog Python 4/16 12:00 PM PT Multi-Agent API with LangGraph and Azure Cosmos DB Python 4/16 03:00 PM PT Mastering Agentic RAG Python 4/17 06:00 AM PT Build your own agent with OpenAI, .NET, and Copilot Studio C# 4/17 09:00 AM PT Building smarter Python AI agents with code interpreters Python 4/17 12:00 PM PT Building Java AI Agents using LangChain4j and Dynamic Sessions Java 4/17 03:00 PM PT Agentic Voice Mode Unplugged PythonElevate Your AI Expertise with Microsoft Azure: Learn Live Series for Developers
Unlock the power of Azure AI and master the art of creating advanced AI agents. Starting from April 15th, embark on a comprehensive learning journey designed specifically for professional developers like you. This series will guide you through the official Microsoft Learn Plan, focused on the latest agentic AI technologies and innovations. Generative AI has evolved to become an essential tool for crafting intelligent applications, and AI agents are leading the charge. Here's your opportunity to deepen your expertise in building powerful, scalable agent-based solutions using the Azure AI Foundry, Azure AI Agent Service, and the Semantic Kernel Framework. Why Attend? This Learn Live series will provide you with: In-depth Knowledge: Understand when to use AI agents, how they function, and the best practices for building them on Azure. Hands-On Experience: Gain practical skills to develop, deploy, and extend AI agents with Azure AI Agent Service and Semantic Kernel SDK. Expert Insights: Learn directly from Microsoft’s AI professionals, ensuring you're at the cutting edge of agentic AI technologies. Session Highlights Plan and Prepare AI Solutions | April 15th Explore foundational principles for creating secure and responsible AI solutions. Prepare your development environment for seamless integration with Azure AI services. Fundamentals of AI Agents | April 22nd Discover the transformative role of language models and generative AI in enabling intelligent applications. Understand Microsoft Copilot and effective prompting techniques for agent development. Azure AI Agent Service: Build and Integrate | April 29th Dive into the key features of Azure AI Agent Service. Build agents and learn how to integrate them into your applications for enhanced functionality. Extend with Custom Tools | May 6th Enhance your agents’ capabilities with custom tools, tailored to meet unique application requirements. Develop an AI agent with Semantic Kernel | May 8th Use Semantic Kernel to connect to an Azure AI Foundry project Create Azure AI Agent Service agents using the Semantic Kernel SDK Integrate plugin functions with your AI agent Orchestrate Multi-Agent Solutions with Semantic Kernel | May 13th Utilize the Semantic Kernel SDK to create collaborative multi-agent systems. Develop and integrate custom plugin functions for versatile AI solutions. What You’ll Achieve By the end of this series, you'll: Build AI agents using cutting-edge Azure technologies. Integrate custom tools to extend agent capabilities. Develop multi-agent solutions with advanced orchestration. How to Join Don't miss out on this opportunity to level up your development skills and lead the next wave of AI-driven applications. Register now and set yourself apart as a developer equipped to harness the full potential of Azure AI. 🔗 Register for the Learn Live Series 🗓️ Format: Livestream | Language: English | Topic: Core AI Development Take the leap and transform how you develop intelligent applications with Microsoft Azure AI. Does this revision align with your vision for the blog? Let me know if there's anything else you'd like to refine or add!AI Toolkit for Visual Studio Code Now Supports NVIDIA NIM Microservices for RTX AI PCs
AI Toolkit now supports NVIDIA NIM™ microservice-based foundation models for inference testing in the model playground and advanced features like bulk run, evaluation and building prompts. This collaboration helps AI Engineers streamline development processes with foundational AI models. About AI Toolkit AI Toolkit is a VS Code extension for AI engineers to build, deploy, and manage AI solutions. It includes model and prompt-centric features that allow users to explore and test different AI models, create and evaluate prompts, and perform model finetuning, all from within VS Code. Since its preview launch in 2024, AI Toolkit has helped developers worldwide learn about generative AI models and start building AI solutions. NVIDIA NIM Microservices This January, NVIDIA announced that state-of-the-art AI models spanning language, speech, animation and vision capabilities - offered as NVIDIA NIM microservices - can now run locally on NVIDIA RTX AI PCs. These microservices prepackage optimized AI models with all the necessary runtime components for deployment across NVIDIA GPUs. Developers can now develop and deploy anywhere with the same unified experience and software stack across RTX AI PCs and workstations to the cloud. Developers can jumpstart their AI development journey by downloading and running NIM containers quickly on Windows 11 PCs with GeForce RTX GPUs using Windows Subsystem for Linux (WSL2). The Power of Collaboration Integrating AI Toolkit with NIM provides AI engineers with a more cohesive and efficient workflow: Seamlessly integrate AI Toolkit with NIM to create a unified development environment without the need to switch context. Users can access any NIM supported models from AI Toolkit. Leverage the combined capabilities of both tools to streamline workflows and accelerate AI solution development process around foundation AI models, from within VS Code. How to get started Follow these steps to begin leveraging the power of NIM on AI Toolkit: Download and install the latest version of AI Toolkit for VS Code. Install NIM pre-requisites on RTX PCs using the instructions here. Select a NIM model from the model catalog on AI Toolkit and load it in Playground. Optionally, you can also add your NIM model hosted in the cloud to AI Toolkit by URL Explore NIM models from the playground Start developing prompts with new NIM models in AI Toolkit! Looking Forward We invite you to explore the possibilities of this integration and take your development projects to new heights! Try AI Toolkit today – and please continue sharing your feedback. Stay tuned for more updates and detailed tutorials on how to maximize the benefits of this exciting new collaboration. Together, we are shaping the future of AI development!Essential Microsoft Resources for MVPs & the Tech Community from the AI Tour
Unlock the power of Microsoft AI with redeliverable technical presentations, hands-on workshops, and open-source curriculum from the Microsoft AI Tour! Whether you’re a Microsoft MVP, Developer, or IT Professional, these expertly crafted resources empower you to teach, train, and lead AI adoption in your community. Explore top breakout sessions covering GitHub Copilot, Azure AI, Generative AI, and security best practices—designed to simplify AI integration and accelerate digital transformation. Dive into interactive workshops that provide real-world applications of AI technologies. Take it a step further with Microsoft’s Open-Source AI Curriculum, offering beginner-friendly courses on AI, Machine Learning, Data Science, Cybersecurity, and GitHub Copilot—perfect for upskilling teams and fostering innovation. Don’t just learn—lead. Access these resources, host impactful training sessions, and drive AI adoption in your organization. Start sharing today! Explore now: Microsoft AI Tour Resources.GitHub Copilot for Azure: Deploy an AI RAG App to ACA using AZD
Recently, I had to develop a Retrieval-Augmented Generation (RAG) prototype for an internal project. Since I enjoy working with LlamaIndex, I decided to use GitHub Copilot for Azure to quickly find an existing sample that I could use as a starting point and deploy it to Azure Container Apps. Getting Started with GitHub Copilot for Azure To begin, I installed the GitHub Copilot for Azure extension in VS Code. This extension allows me to interact with Azure directly using the azure command. I used this feature to ask my Copilot to help me locate a relevant sample to use as a foundation for my project. After querying available Azure resources, the extension found a LlamaIndex JavaScript sample, which was ideal for my needs. I then copied the Azure Developer CLI (azd) command to initialize my project and set up my environment. Deploying the Sample to Azure Container Apps With the sample files downloaded, the next step was to deploy the application as-is to ensure everything functioned correctly. I asked my Copilot how to proceed, and it suggested running the following command: azd up . After executing the command, my sample was successfully deployed to Azure Container Apps. Now, it was time to test it! Debugging Deployment Issues with Copilot To verify that everything was working, I interacted with the app by entering my prompt. However, I encountered an issue—there was a missing configuration in the container. To troubleshoot, I shared the error message with the extension and asked for guidance. My Copilot suggested adding a specific line to my main.bicep file. I applied the change and then wondered if I also needed to pass the variable to my container as a runtime configuration. Again, I consulted Copilot, which confirmed that I should add the variable to the container configuration. After vibe copying and pasting the suggested change into my Bicep file, I was ready to redeploy. Redeploying and Final Testing To redeploy my updated configuration, I executed: azd deploy . The new revision of the app was successfully deployed. Time for another test! Success! The application responded correctly, confirming that my configuration updates worked as expected. Conclusion Using GitHub Copilot for Azure significantly accelerated my RAG prototype development by helping me find relevant resources, debug issues, and deploy my app seamlessly. If you’re building Azure-based applications, I highly recommend trying out this extension. You can download the GitHub Copilot for Azure extension in VS Code and give it a go yourself. If you do, share your feedback in the repo—I’d love to hear how it improves your workflow!
Medallion Architecture in Microsoft Fabric: Leveraging OneLake for Scalable Data Management
Learn how to implement Microsoft's OneLake with Medallion Architecture in Microsoft Fabric. Discover how the Bronze, Silver, and Gold layers enhance data scalability, governance, and security for efficient data management and analytics.
Getting Started with the AI Dev Gallery
March Update: The Gallery is now available on the Microsoft Store! The AI Dev Gallery is a new open-source project designed to inspire and support developers in integrating on-device AI functionality into their Windows apps. It offers an intuitive UX for exploring and testing interactive AI samples powered by local models. Key features include: Quickly explore and download models from well-known sources on GitHub and HuggingFace. Test different models with interactive samples over 25 different scenarios, including text, image, audio, and video use cases. See all relevant code and library references for every sample. Switch between models that run on CPU and GPU depending on your device capabilities. Quickly get started with your own projects by exporting any sample to a fresh Visual Studio project that references the same model cache, preventing duplicate downloads. Part of the motivation behind the Gallery was exposing developers to the host of benefits that come with on-device AI. Some of these benefits include improved data security and privacy, increased control and parameterization, and no dependence on an internet connection or third-party cloud provider. Requirements Device Requirements Minimum OS Version: Windows 10, version 1809 (10.0; Build 17763) Architecture: x64, ARM64 Memory: At least 16 GB is recommended Disk Space: At least 20GB free space is recommended GPU: 8GB of VRAM is recommended for running samples on the GPU Using the Gallery The AI Dev Gallery has can be navigated in two ways: The Samples View The Models View Navigating Samples In this view, samples are broken up into categories (Text, Code, Image, etc.) and then into more specific samples, like in the Translate Text pictured below: On clicking a sample, you will be prompted to choose a model to download if you haven’t run this sample before: Next to the model you can see the size of the model, whether it will run on CPU or GPU, and the associated license. Pick the model that makes the most sense for your machine. You can also download new models and change the model for a sample later from the sample view. Just click the model drop down at the top of the sample: The last thing you can do from the Sample pane is view the sample code and export the project to Visual Studio. Both buttons are found in the top right corner of the sample, and the code view will look like this: Navigating Models If you would rather navigate by models instead of samples, the Gallery also provides the model view: The model view contains a similar navigation menu on the right to navigate between models based on category. Clicking on a model will allow you to see a description of the model, the versions of it that are available to download, and the samples that use the model. Clicking on a sample will take back over to the samples view where you can see the model in action. Deleting and Managing Models If you need to clear up space or see download details for the models you are using, you can head over the Settings page to manage your downloads: From here, you can easily see every model you have downloaded and how much space on your drive they are taking up. You can clear your entire cache for a fresh start or delete individual models that you are no longer using. Any deleted model can be redownload through either the models or samples view. Next Steps for the Gallery The AI Dev Gallery is still a work in progress, and we plan on adding more samples, models, APIs, and features, and we are evaluating adding support for NPUs to take the experience even further If you have feedback, noticed a bug, or any ideas for features or samples, head over to the issue board and submit an issue. We also have a discussion board for any other topics relevant to the Gallery. The Gallery is an open-source project, and we would love contribution, feedback, and ideation! Happy modeling!
