Technical Issue during AB-900 Beta Exam - Pearson VUE Queue System Failure - Case #14052082
Hello Support Team, I am writing to request assistance regarding a technical issue I experienced during my AB-900 Beta Exam appointment on January 2nd, 2026. I had a voucher providing an 80% discount valid exclusively for that date. I successfully completed the check-in process on time, and my system passed all pre-exam requirements. However, I was placed in a waiting queue with 24 people ahead of me. After waiting for over 15 minutes, the system displayed a "Reschedule" button. When I clicked it, no dates were available, and I was unable to return to the exam session. I immediately contacted Pearson VUE support via chat. Despite following their instructions to restart the check-in process, the system blocked my access, stating it was past the allowed start time. I opened a formal case with Pearson VUE, and their final resolution (Case #14052082) was that I should reach out to Microsoft Support for further guidance and a "new authorization," as they claimed they could not provide a new voucher for this expired promotion. I prepared extensively for this certification and I was unable to take it due to a failure in the Pearson VUE proctoring queue system. Since the original voucher has now expired, I kindly ask for a new voucher or authorization so I can reschedule and take the AB-900 exam at the discounted rate I previously paid for. Exam: AB-900 Date: January 2nd, 2026 Pearson VUE Case Number: 14052082 Thank you for your time and assistance.
Building a Multi-Agent System with Azure AI Agent Service: Campus Event Management
Personal Background My name is Peace Silly. I studied French and Spanish at the University of Oxford, where I developed a strong interest in how language is structured and interpreted. That curiosity about syntax and meaning eventually led me to computer science, which I came to see as another language built on logic and structure. In the academic year 2024–2025, I completed the MSc Computer Science at University College London, where I developed this project as part of my Master’s thesis. Project Introduction Can large-scale event management be handled through a simple chat interface? This was the question that guided my Master’s thesis project at UCL. As part of the Industry Exchange Network (IXN) and in collaboration with Microsoft, I set out to explore how conversational interfaces and autonomous AI agents could simplify one of the most underestimated coordination challenges in campus life: managing events across multiple departments, societies, and facilities. At large universities, event management is rarely straightforward. Rooms are shared between academic timetables, student societies, and one-off events. A single lecture theatre might host a departmental seminar in the morning, a society meeting in the afternoon, and a careers talk in the evening, each relying on different systems, staff, and communication chains. Double bookings, last-minute cancellations, and maintenance issues are common, and coordinating changes often means long email threads, manual spreadsheets, and frustrated users. These inefficiencies do more than waste time; they directly affect how a campus functions day to day. When venues are unavailable or notifications fail to reach the right people, even small scheduling errors can ripple across entire departments. A smarter, more adaptive approach was needed, one that could manage complex workflows autonomously while remaining intuitive and human for end users. The result was the Event Management Multi-Agent System, a cloud-based platform where staff and students can query events, book rooms, and reschedule activities simply by chatting. Behind the scenes, a network of Azure-powered AI agents collaborates to handle scheduling, communication, and maintenance in real time, working together to keep the campus running smoothly. The user scenario shown in the figure below exemplifies the vision that guided the development of this multi-agent system. Starting with Microsoft Learning Resources I began my journey with Microsoft’s tutorial Build Your First Agent with Azure AI Foundry which introduced the fundamentals of the Azure AI Agent Service and provided an ideal foundation for experimentation. Within a few weeks, using the Azure Foundry environment, I extended those foundations into a fully functional multi-agent system. Azure Foundry’s visual interface was an invaluable learning space. It allowed me to deploy, test, and adjust model parameters such as temperature, system prompts, and function calling while observing how each change influenced the agents’ reasoning and collaboration. Through these experiments, I developed a strong conceptual understanding of orchestration and coordination before moving to the command line for more complex development later. When development issues inevitably arose, I relied on the Discord support community and the GitHub forum for troubleshooting. These communities were instrumental in addressing configuration issues and providing practical examples, ensuring that each agent performed reliably within the shared-thread framework. This early engagement with Microsoft’s learning materials not only accelerated my technical progress but also shaped how I approached experimentation, debugging, and iteration. It transformed a steep learning curve into a structured, hands-on process that mirrored professional software development practice. A Decentralised Team of AI Agents The system’s intelligence is distributed across three specialised agents, powered by OpenAI’s GPT-4.1 models through Azure OpenAI Service. They each perform a distinct role within the event management workflow: Scheduling Agent – interprets natural language requests, checks room availability, and allocates suitable venues. Communications Agent – notifies stakeholders when events are booked, modified, or cancelled. Maintenance Agent – monitors room readiness, posts fault reports when venues become unavailable, and triggers rescheduling when needed. Each agent operates independently but communicates through a shared thread, a transparent message log that serves as the coordination backbone. This thread acts as a persistent state space where agents post updates, react to changes, and maintain a record of every decision. For example, when a maintenance fault is detected, the Maintenance Agent logs the issue, the Scheduling Agent identifies an alternative venue, and the Communications Agent automatically notifies attendees. These interactions happen autonomously, with each agent responding to the evolving context recorded in the shared thread. Interfaces and Backend The system was designed with both developer-focused and user-facing interfaces, supporting rapid iteration and intuitive interaction. The Terminal Interface Initially, the agents were deployed and tested through a terminal interface, which provided a controlled environment for debugging and verifying logic step by step. This setup allowed quick testing of individual agents and observation of their interactions within the shared thread. The Chat Interface As the project evolved, I introduced a lightweight chat interface to make the system accessible to staff and students. This interface allows users to book rooms, query events, and reschedule activities using plain language. Recognising that some users might still want to see what happens behind the scenes, I added an optional toggle that reveals the intermediate steps of agent reasoning. This transparency feature proved valuable for debugging and for more technical users who wanted to understand how the agents collaborated. When a user interacts with the chat interface, they are effectively communicating with the Scheduling Agent, which acts as the primary entry point. The Scheduling Agent interprets natural-language commands such as “Book the Engineering Auditorium for Friday at 2 PM” or “Reschedule the robotics demo to another room.” It then coordinates with the Maintenance and Communications Agents to complete the process. Behind the scenes, the chat interface connects to a FastAPI backend responsible for core logic and data access. A Flask + HTMX layer handles lightweight rendering and interactivity, while the Azure AI Agent Service manages orchestration and shared-thread coordination. This combination enables seamless agent communication and reliable task execution without exposing any of the underlying complexity to the end user. Automated Notifications and Fault Detection Once an event is scheduled, the Scheduling Agent posts the confirmation to the shared thread. The Communications Agent, which subscribes to thread updates, automatically sends notifications to all relevant stakeholders by email. This ensures that every participant stays informed without any manual follow-up. The Maintenance Agent runs routine availability checks. If a fault is detected, it logs the issue to the shared thread, prompting the Scheduling Agent to find an alternative room. The Communications Agent then notifies attendees of the change, ensuring minimal disruption to ongoing events. Testing and Evaluation The system underwent several layers of testing to validate both functional and non-functional requirements. Unit and Integration Tests Backend reliability was evaluated through unit and integration tests to ensure that room allocation, conflict detection, and database operations behaved as intended. Automated test scripts verified end-to-end workflows for event creation, modification, and cancellation across all agents. Integration results confirmed that the shared-thread orchestration functioned correctly, with all test cases passing consistently. However, coverage analysis revealed that approximately 60% of the codebase was tested, leaving some areas such as Azure service integration and error-handling paths outside automated validation. These trade-offs were deliberate, balancing test depth with project scope and the constraints of mocking live dependencies. Azure AI Evaluation While functional testing confirmed correctness, it did not capture the agents’ reasoning or language quality. To assess this, I used Azure AI Evaluation, which measures conversational performance across metrics such as relevance, coherence, fluency, and groundedness. The results showed high scores in relevance (4.33) and groundedness (4.67), confirming the agents’ ability to generate accurate and context-aware responses. However, slightly lower fluency scores and weaker performance in multi-turn tasks revealed a retrieval–execution gap typical in task-oriented dialogue systems. Limitations and Insights The evaluation also surfaced several key limitations: Synthetic data: All tests were conducted with simulated datasets rather than live campus systems, limiting generalisability. Scalability: A non-functional requirement in the form of horizontal scalability was not tested. The architecture supports scaling conceptually but requires validation under heavier load. Despite these constraints, the testing process confirmed that the system was both technically reliable and linguistically robust, capable of autonomous coordination under normal conditions. The results provided a realistic picture of what worked well and what future iterations should focus on improving. Impact and Future Work This project demonstrates how conversational AI and multi-agent orchestration can streamline real operational processes. By combining Azure AI Agent Services with modular design principles, the system automates scheduling, communication, and maintenance while keeping the user experience simple and intuitive. The architecture also establishes a foundation for future extensions: Predictive maintenance to anticipate venue faults before they occur. Microsoft Teams integration for seamless in-chat scheduling. Scalability testing and real-user trials to validate performance at institutional scale. Beyond its technical results, the project underscores the potential of multi-agent systems in real-world coordination tasks. It illustrates how modularity, transparency, and intelligent orchestration can make everyday workflows more efficient and human-centred. Acknowledgements What began with a simple Microsoft tutorial evolved into a working prototype that reimagines how campuses could manage their daily operations through conversation and collaboration. This was both a challenging and rewarding journey, and I am deeply grateful to Professor Graham Roberts (UCL) and Professor Lee Stott (Microsoft) for their guidance, feedback, and support throughout the project.Edge AI for Beginners : Getting Started with Foundry Local
In Module 08 of the EdgeAI for Beginners course, Microsoft introduces Foundry Local a toolkit that helps you deploy and test Small Language Models (SLMs) completely offline. In this blog, I’ll share how I installed Foundry Local, ran the Phi-3.5-mini model on my windows laptop, and what I learned through the process. What Is Foundry Local? Foundry Local allows developers to run AI models locally on their own hardware. It supports text generation, summarization, and code completion — all without sending data to the cloud. Unlike cloud-based systems, everything happens on your computer, so your data never leaves your device. Prerequisites Before starting, make sure you have: Windows 10 or 11 Python 3.10 or newer Git Internet connection (for the first-time model download) Foundry Local installed Step 1 — Verify Installation After installing Foundry Local, open Command Prompt and type: foundry --version If you see a version number, Foundry Local is installed correctly. Step 2 — Start the Service Start the Foundry Local service using: foundry service start You should see a confirmation message that the service is running. Step 3 — List Available Models To view the models supported by your system, run: foundry model list You’ll get a list of locally available SLMs. Here’s what I saw on my machine: Note: Model availability depends on your device’s hardware. For most laptops, phi-3.5-mini works smoothly on CPU. Step 4 — Run the Phi-3.5 Model Now let’s start chatting with the model: foundry model run phi-3.5-mini-instruct-generic-cpu:1 Once it loads, you’ll enter an interactive chat mode. Try a simple prompt: Hello! What can you do? The model replies instantly — right from your laptop, no cloud needed. To exit, type: /exit How It Works Foundry Local loads the model weights from your device and performs inference locally.This means text generation happens using your CPU (or GPU, if available). The result: complete privacy, no internet dependency, and instant responses. Benefits for Students For students beginning their journey in AI, Foundry Local offers several key advantages: No need for high-end GPUs or expensive cloud subscriptions. Easy setup for experimenting with multiple models. Perfect for class assignments, AI workshops, and offline learning sessions. Promotes a deeper understanding of model behavior by allowing step-by-step local interaction. These factors make Foundry Local a practical choice for learning environments, especially in universities and research institutions where accessibility and affordability are important. Why Use Foundry Local Running models locally offers several practical benefits compared to using AI Foundry in the cloud. With Foundry Local, you do not need an internet connection, and all computations happen on your personal machine. This makes it faster for small models and more private since your data never leaves your device. In contrast, AI Foundry runs entirely on the cloud, requiring internet access and charging based on usage. For students and developers, Foundry Local is ideal for quick experiments, offline testing, and understanding how models behave in real-time. On the other hand, AI Foundry is better suited for large-scale or production-level scenarios where models need to be deployed at scale. In summary, Foundry Local provides a flexible and affordable environment for hands-on learning, especially when working with smaller models such as Phi-3, Qwen2.5, or TinyLlama. It allows you to experiment freely, learn efficiently, and better understand the fundamentals of Edge AI development. Optional: Restart Later Next time you open your laptop, you don’t have to reinstall anything. Just run these two commands again: foundry service start foundry model run phi-3.5-mini-instruct-generic-cpu:1 What I Learned Following the EdgeAI for Beginners Study Guide helped me understand: How edge AI applications work How small models like Phi 3.5 can run on a local machine How to test prompts and build chat apps with zero cloud usage Conclusion Running the Phi-3.5-mini model locally with Foundry Localgave me hands-on insight into edge AI. It’s an easy, private, and cost-free way to explore generative AI development. If you’re new to Edge AI, start with the EdgeAI for Beginners course and follow its Study Guide to get comfortable with local inference and small language models. Resources: EdgeAI for Beginners GitHub Repo Foundry Local Official Site Phi Model Link
Microsoft’s A-Grade Azure AI Stack: From Dissertation Prototype to Smart Campus Pilot
This post isn't just about the Student Support Agent (SSA) I built, which earned me a Distinction. It's about how Microsoft's tools made it possible to go from a rough concept to a robust pilot, proving their developer stack is one of the most convenient and powerful options for building intelligent, ethical, and scalable educational systems. The Vision: Cutting Through Campus Complexity University life is full of fragmented systems. Students constantly juggle multiple logins, websites, and interfaces just to check a timetable, book a room, or find a policy. My goal was simple: reduce that cognitive load by creating a unified assistant that could manage all these tasks through a single, intelligent conversation. The Stack That Made It Possible The core of the system relied on a few key, interconnected technologies: Technology Core Function Impact Azure AI Search Hybrid Data Retrieval Anchored responses in official documents. Azure OpenAI Natural Language Generation Created human-like, accurate answers. Semantic Kernel (SK) Multi-Agent Orchestration Managed complex workflows and memory. Azure Speech SDK Multimodal Interface Enabled accessible voice input and output. The foundation was built using Streamlit and FastAPI for rapid prototyping. Building a system that's context-aware, accessible, and extensible is a huge challenge, but it's exactly where the Microsoft AI stack shined. From Simple Chatbot to Multi-Agent Powerhouse Early campus chatbots are often single-agent models, great for basic FAQs, but they quickly fail when tasks span multiple services. I used Semantic Kernel (SK) Microsoft's powerful, open-source framework to build a modular, hub-and-spoke multi-agent system. A central orchestrator routes a request (like "book a study room") to a specialist agent (the Booking Agent), which knows exactly how to handle that task. This modularity was a game-changer: I could add new features (like an Events Agent) without breaking the core system, ensuring the architecture stayed clean and ready for expansion. Agentic Retrieval-Augmented Generation (Agentic RAG): Trust and Transparency To ensure the assistant was trustworthy, I used Agentic RAG to ground responses in real campus (Imperial College London) documentation. This included everything from admission fee payments to campus shuttle time. Azure AI Search indexed all handbooks and policies, allowing the assistant to pull relevant chunks of data and then cite the sources directly in its response. Result: The system avoids common hallucinations by refusing to answer when confidence is low. Students can verify every piece of advice, dramatically improving trust and transparency. Results: A Foundation for Scalable Support A pilot study with 15 students was highly successful: 100% positive feedback on the ease of use and perceived benefit. 93% satisfaction with the voice features. High trust was established due to transparent citations. The SSA proved it could save students time by centralising tasks like booking rooms, checking policies and offering study tips! Final Thoughts Microsoft’s AI ecosystem didn’t just support my dissertation; it shaped it. The tools were reliable, well-documented, and flexible enough to handle real-world complexity. More importantly, they allowed me to focus on student experience, ethics, and pedagogy, rather than wrestling with infrastructure. If you’re a student, educator, or developer looking to build intelligent systems that are transparent, inclusive, and scalable, Microsoft’s AI stack is a great place to start! 🙋🏽♀️ About Me I’m Tyana Tshiota, a postgraduate student in Applied Computational Science and Engineering at Imperial College London. Leveraging Microsoft’s AI stack and the extensive documentation on Microsoft Learn played a key role in achieving a Distinction in my dissertation. Moving forward, I’m excited to deepen my expertise by pursuing Azure certifications. I’d like to extend my sincere gratitude to my supervisor, Lee_Stott , for his invaluable mentorship and support throughout this project. If you haven’t already, check out his insightful posts on the Educator Developer Blog, or try building your own agent with the AI Agents for Beginners curriculum developed by Lee and his team! You can reach out via my LinkedIn if you’re interested in smart campus systems, AI in education, collaborative development, or would like to discuss opportunities.Getting Started with AI Agents: A Student Developer’s Guide to the Microsoft Agent Framework
AI agents are becoming the backbone of modern applications, from personal assistants to autonomous research bots. If you're a student developer curious about building intelligent, goal-driven agents, Microsoft’s newly released Agent Framework is your launchpad. In this post, we’ll break down what the framework offers, how to get started, and why it’s a game-changer for learners and builders alike. What Is the Microsoft Agent Framework? The Microsoft Agent Framework is a modular, open-source toolkit designed to help developers build, orchestrate, and evaluate AI agents with minimal friction. It’s part of the AI Agents for Beginners curriculum, which walks you through foundational concepts using reproducible examples. At its core, the framework helps you: Define agent goals and capabilities Manage memory and context Route tasks through tools and APIs Evaluate agent performance with traceable metrics Whether you're building a research assistant, a coding helper, or a multi-agent system, this framework gives you the scaffolding to do it right. What’s Inside the Framework? Here’s a quick look at the key components: Component Purpose AgentRuntime Manages agent lifecycle, memory, and tool routing AgentConfig Defines agent goals, tools, and memory settings Tool Interface Lets you plug in custom tools (e.g., web search, code execution) MemoryProvider Supports semantic memory and context-aware responses Evaluator Tracks agent performance and goal completion The framework is built with Python and .NET and designed to be extensible, perfect for experimentation and learning. Try It: Your First Agent in 10 Minutes Here’s a simplified walkthrough to get you started: Clone the repo git clone https://github.com/microsoft/ai-agents-for-beginners Open the Sample cd ai-agents-for-beginners/14-microsoft-agent-framework Install dependencies pip install -r requirements.txt Run the sample agent python main.py You’ll see a basic agent that can answer questions using a web search tool and maintain context across turns. From here, you can customize its goals, memory, and tools. Why Student Developers Should Care Modular Design: Learn how real-world agents are structured—from memory to evaluation. Reproducible Workflows: Build agents that can be debugged, traced, and improved over time. Open Source: Contribute, fork, and remix with your own ideas. Community-Ready: Perfect for hackathons, research projects, or portfolio demos. Plus, it aligns with Microsoft’s best practices for agent governance, making it a solid foundation for enterprise-grade development. Why Learn? Here are a few ideas to take your learning further: Build a custom tool (e.g., a calculator or code interpreter) Swap in a different memory provider (like a vector DB) Create an evaluation pipeline for multi-agent collaboration Use it in a class project or student-led workshop Join the Microsoft Azure AI Foundry Discord https://aka.ms/Foundry/discord share your project and build your AI Engineer and Developer connections. Star and Fork the AI Agents for Beginners repo for updates and new modules. Final Thoughts The Microsoft Agent Framework isn’t just another library, it’s a teaching tool, a playground, and a launchpad for the next generation of AI builders. If you’re a student developer, this is your chance to learn by doing, contribute to the community, and shape the future of agentic systems. So fire up your terminal, fork the repo, and start building. Your first agent is just a few lines of code away.Como começar e crescer no mercado de tecnologia
A #JornadaTech é uma maratona de mentorias online do Microsoft Reactor São Paulo, sobre carreira na tecnologia, em que você poderá aprender mais sobre as áreas de Cloud, Segurança, Programação e Dados. Neste artigo, você encontrará algumas dicas e recursos para começar e crescer na carreira de tecnologia.Edge AI for Student Developers: Learn to Run AI Locally
AI isn’t just for the cloud anymore. With the rise of Small Language Models (SLMs) and powerful local inference tools, developers can now run intelligent applications directly on laptops, phones, and edge devices—no internet required. If you're a student developer curious about building AI that works offline, privately, and fast, Microsoft’s Edge AI for Beginners course is your perfect starting point. What Is Edge AI? Edge AI refers to running AI models directly on local hardware—like your laptop, mobile device, or embedded system—without relying on cloud servers. This approach offers: ⚡ Real-time performance 🔒 Enhanced privacy (no data leaves your device) 🌐 Offline functionality 💸 Reduced cloud costs Whether you're building a chatbot that works without Wi-Fi or optimizing AI for low-power devices, Edge AI is the future of intelligent, responsive apps. About the Course Edge AI for Beginners is a free, open-source curriculum designed to help you: Understand the fundamentals of Edge AI and local inference Explore Small Language Models like Phi-2, Mistral-7B, and Gemma Deploy models using tools like Llama.cpp, Olive, MLX, and OpenVINO Build cross-platform apps that run AI locally on Windows, macOS, Linux, and mobile The course is hosted on GitHub and includes hands-on labs, quizzes, and real-world examples. You can fork it, remix it, and contribute to the community. What You’ll Learn Module Focus 01. Introduction What is Edge AI and why it matters 02. SLMs Overview of small language models 03. Deployment Running models locally with various tools 04. Optimization Speeding up inference and reducing memory 05. Applications Building real-world Edge AI apps Each module is beginner-friendly and includes practical exercises to help you build and deploy your own local AI solutions. Who Should Join? Student developers curious about AI beyond the cloud Hackathon participants looking to build offline-capable apps Makers and builders interested in privacy-first AI Anyone who wants to explore the future of on-device intelligence No prior AI experience required just a willingness to learn and experiment. Why It Matters Edge AI is a game-changer for developers. It enables smarter, faster, and more private applications that work anywhere. By learning how to deploy AI locally, you’ll gain skills that are increasingly in demand across industries—from healthcare to robotics to consumer tech. Plus, the course is: 💯 Free and open-source 🧠 Backed by Microsoft’s best practices 🧪 Hands-on and project-based 🌐 Continuously updated Ready to Start? Head to aka.ms/edgeai-for-beginners and dive into the modules. Whether you're coding in your dorm room or presenting at your next hackathon, this course will help you build smarter AI apps that run right where you need them on the edge.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.Preparing for Your Organization’s AI Workloads – Student Learning Pathways
This structured plan helps students: Plans | Microsoft Learn Build foundational knowledge of AI in the cloud. Learn how enterprise-level infrastructure supports responsible, scalable AI deployments. Explore governance and monitoring strategies to ensure security and compliance. And the best part? It’s built using Microsoft’s existing training resources plus some brand-new modules to give you an edge. Your AI Readiness Journey on Azure 🎯 Milestone 1: Getting Started with AI on Azure https://learn.microsoft.com/training/paths/introduction-to-ai-on-azure/ Begin with the basics—from machine learning concepts to practical uses of Azure AI services. 🛡️ Milestone 2: Infrastructure Essentials https://learn.microsoft.com/training/paths/manage-iam-for-ai-workloads-on-azure/ https://learn.microsoft.com/training/paths/manage-network-access-ai-workloads/ Learn how enterprises secure access and manage identities—critical for real-world applications. 📊 Milestone 3: Monitoring AI Services https://learn.microsoft.com/training/paths/monitor-ai-workloads-on-azure/ Discover how businesses ensure their models perform safely and consistently at scale. 🏛️ Milestone 4: Advanced Management & Governance https://learn.microsoft.com/training/paths/ai-workloads-governance/ Master how organizations prevent data leaks and enforce responsible AI usage. 🆕 New Training Content Just for You To make this roadmap even more student-friendly, Microsoft has introduced updated and brand-new modules, including: Azure ML Authentication & Authorization Secure Azure AI Services Restrict Workspace Network Traffic Monitor Azure ML Prevent Data Exfiltration Govern AI Services with Azure Policy 🔗 Ready to Dive In? Whether you're exploring a career in AI or just getting started with Azure, these learning paths will level up your skills while helping you understand how real-world teams manage complex AI workloads. Start your journey on Microsoft Learn and become the architect of tomorrow’s intelligent systems. 💡 Would you like a version formatted for your internal newsletter or maybe something more conversational for social media? I can easily tailor it to fit the tone or medium you're aiming for.Multi-Agent Systems and MCP Tools Integration with Azure AI Foundry
The Power of Connected Agents: Building Multi-Agent Systems Imagine trying to build an AI system that can handle complex workflows like managing support tickets, analyzing data from multiple sources, or providing comprehensive recommendations. Sounds challenging, right? That's where multi-agent systems come in! The Develop a multi-agent solution with Azure AI Foundry Agent Services module introduces you to the concept of connected agents a game changing approach that allows you to break down complex tasks into specialized roles handled by different AI agents. Why Connected Agents Matter As a student developer, you might wonder why you'd need multiple agents when a single agent can handle many tasks. Here's why this approach is transformative: 1. Simplified Complexity: Instead of building one massive agent that does everything (and becomes difficult to maintain), you can create smaller, specialized agents with clearly defined responsibilities. 2. No Custom Orchestration Required: The main agent naturally delegates tasks using natural language - no need to write complex routing logic or orchestration code. 3. Better Reliability and Debugging: When something goes wrong, it's much easier to identify which specific agent is causing issues rather than debugging a monolithic system. 4. Flexibility and Extensibility: Need to add a new capability? Just create a new connected agent without modifying your main agent or other parts of the system. How Multi-Agent Systems Work The architecture is surprisingly straightforward: 1. A main agent acts as the orchestrator, interpreting user requests and delegating tasks 2. Connected sub-agents perform specialized functions like data retrieval, analysis, or summarization 3. Results flow back to the main agent, which compiles the final response For example, imagine building a ticket triage system. When a new support ticket arrives, your main agent might: - Delegate to a classifier agent to determine the ticket type - Send the ticket to a priority-setting agent to determine urgency - Use a team-assignment agent to route it to the right department All this happens seamlessly without you having to write custom routing logic! Setting Up a Multi-Agent Solution The module walks you through the entire process: 1. Initializing the agents client 2. Creating connected agents with specialized roles 3. Registering them as tools for the main agent 4. Building the main agent that orchestrates the workflow 5. Running the complete system Taking It Further: Integrating MCP Tools with Azure AI Agents Once you've mastered multi-agent systems, the next level is connecting your agents to external tools and services. The Integrate MCP Tools with Azure AI Agents module teaches you how to use the Model Context Protocol (MCP) to give your agents access to a dynamic catalog of tools. What is Dynamic Tool Discovery? Traditionally, adding new tools to an AI agent meant hardcoding each one directly into your agent's code. But what if tools change frequently, or if different teams manage different tools? This approach quickly becomes unmanageable. Dynamic tool discovery through MCP solves this problem by: 1. Centralizing Tool Management: Tools are defined and managed in a central MCP server 2. Enabling Runtime Discovery: Agents discover available tools during runtime through the MCP client 3. Supporting Automatic Updates: When tools are updated on the server, agents automatically get the latest versions The MCP Server-Client Architecture The architecture involves two key components: 1. MCP Server: Acts as a registry for tools, hosting tool definitions decorated with `@mcp.tool`. Tools are exposed over HTTP when requested. 2. MCP Client: Acts as a bridge between your MCP server and Azure AI Agent. It discovers available tools, generates Python function stubs to wrap them, and registers those functions with your agent. This separation of concerns makes your AI solution more maintainable and adaptable to change. Setting Up MCP Integration The module guides you through the complete process: 1. Setting up an MCP server with tool definitions 2. Creating an MCP client to connect to the server 3. Dynamically discovering available tools 4. Wrapping tools in async functions for agent use 5. Registering the tools with your Azure AI agent Once set up, your agent can use any tool in the MCP catalog as if it were a native function, without any hardcoding required! Practical Applications for Student Developers As a student developer, how might you apply these concepts in real projects? Classroom Projects: - Build a research assistant that delegates to specialized agents for different academic subjects - Create a coding tutor that uses different agents for explaining concepts, debugging code, and suggesting improvements Hackathons: - Develop a sustainability app that uses connected agents to analyze environmental data from different sources - Create a personal finance advisor with specialized agents for budgeting, investment analysis, and financial planning Personal Portfolio Projects: - Build a content creation assistant with specialized agents for brainstorming, drafting, editing, and SEO optimization - Develop a health and wellness app that uses MCP tools to connect to fitness APIs, nutrition databases, and sleep tracking services Getting Started Ready to dive in? Both modules include hands-on exercises where you'll build real working examples: - A ticket triage system using connected agents - An inventory management assistant that integrates with MCP tools The prerequisites are straightforward: - Experience with deploying generative AI models in Azure AI Foundry - Programming experience with Python or C# Conclusion Multi-agent systems and MCP tools integration represent the next evolution in AI application development. By mastering these concepts, you'll be able to build more sophisticated, maintainable, and extensible AI solutions - skills that will make you stand out in internship applications and job interviews. The best part? These modules are designed with practical, hands-on learning in mind - perfect for student developers who learn by doing. So why not give them a try? Your future AI applications (and your resume) will thank you for it! Want to learn more about Model Context Protocol 'MCP' see MCP for Beginners Happy coding!
