New Certification for machine learning operations (MLOps) engineers
Do your co-workers rely on you to deploy, operationalize, and maintain machine learning and generative AI solutions in production? Are you working at the intersection of data science, DevOps, and generative AI? If so, the Microsoft Certified: Machine Learning Operations (MLOps) Engineer Associate Certification is designed for you. This Certification validates your ability to operationalize not only traditional machine learning but also generative AI solutions on Azure, reflecting how AI roles have evolved from model experimentation to enterprise-scale AI operations. To earn this Certification, you need to pass Exam AI-300: Operationalizing Machine Learning and Generative AI solutions, currently in beta. Key skills validated by this Certification To earn the MLOps engineer Certification, you must demonstrate your ability to: Design and implement secure, scalable MLOps infrastructure. Automate resource provisioning and deployments by using GitHub Actions, Bicep, and Azure CLI. Orchestrate training, manage model registration and versioning, and monitor production models. Deploy and operationalize generative AI solutions by using Microsoft Foundry. Implement quality assurance, observability, and safety evaluations for generative AI systems. Optimize retrieval-augmented generation (RAG) pipelines and fine-tuned models for performance, accuracy, and cost efficiency. This Certification replaces the Microsoft Certified: Azure Data Scientist Associate Certification (Exam DP-100), which is retiring on June 1, 2026, and reflects the evolution of AI in the enterprise. Exam DP-100 focused on validating your ability to design and implement data science solutions, including data exploration, model training, evaluation, and deployment. Exam AI-300 expands the scope significantly. It retains training and evaluation but places much stronger emphasis on validating your knowledge and experience in automation, infrastructure as code (IaC), continuous integration and continuous deployment (CI/CD), lifecycle governance, observability, drift detection, cost control, and the operationalization of generative AI systems. For more details on the retirement of Exam DP-100, and the latest cloud and AI Certification updates, read our recent blog post, The AI job boom is here. Are you ready to showcase your skills? Skill area Exam AI-300 (new) Exam DP-100 (old) MLOps infrastructure Full CI/CD, IaC (Bicep, Azure CLI), GitHub Actions Basic workspace and compute setup Model lifecycle management Core focus, including registration, versioning, rollout/rollback, monitoring Full lifecycle from training to deployment GenAIOps infrastructure End-to-end lifecycle, including security, automation, and model management with Foundry Basic generative AI setup and experimentation QA and observability Generative AI evaluation, tracing, safety metrics, drift detection, cost monitoring Model evaluation and responsible AI principles Generative AI performance optimization RAG optimization, embedding model selection and tuning, advanced fine-tuning, synthetic data management Basic prompt engineering and fine-tuning Ready to prove your skills? Take advantage of the discounted beta exam offer. The first 300 people who take Exam AI-300 (beta) on or before April 2, 2026, can get 80% off market price. To receive the discount, when you register for the exam and are prompted for payment, use code AI300Meridian. This is not a private access code. The seats are offered on a first-come, first-served basis. As noted, you must take the exam on or before April 2, 2026. Please note that this discount is not available in Turkey, Pakistan, India, or China. Get ready to take Exam AI-300 (beta): Review the Exam AI-300 (beta) exam page for details. The Exam AI-300 study guide explores key topics covered in the exam. Want even more in-depth instructor-led training? We’re creating new instructor-led training that will be released in late March 2026. Connect with Microsoft Training Services Partners in your area for in-person offerings. Need other preparation ideas? Check out Just How Does One Prepare for Beta Exams? You can take certification exams online, from home or the office. Learn what to expect in Online proctored exams: What to expect and how to prepare. The rescore process starts on the day an exam goes live, and final scores for beta exams are released approximately 10 days after that. For details on the timing of beta exam rescoring and results, check out Creating high-quality exams: The path from beta to live. Ready to get started? Remember, only the first 300 candidates can get 80% off Exam AI-300 (beta) with code AI300Meridian on or before April 2, 2026. Stay tuned for general availability of this Certification in May 2026. Learn more about Microsoft Credentials. Related announcements We recently migrated our subject matter expert (SME) database to LinkedIn. To be notified of beta exam availability or opportunities to help with the development of exam, assessment, or learning content, sign up today for the Microsoft Worldwide Learning SME Group for Credentials.Announcing the Microsoft AI Power Days Agent-a-thon winners
The February 2026 Microsoft AI Power Days Agent-a-thon brought together builders from around the world with one shared goal—to turn ideas into working AI agents that solve real problems. From first-time builders to advanced practitioners, the creativity, ambition, and practical impact on display exceeded all expectations. Over the course of the Agent-a-thon, participants explored what’s possible with agentic AI—learning, building, and iterating using Microsoft agent-building tools. The result? A powerful showcase of agents designed to streamline work, unlock insights, and reimagine how work gets done. Congratulations to the 12 Agent-a-thon grand prize winners! The winning submissions were selected based on innovation, usability, and impact. Innovation. These newly created agents go beyond replicating demos. Each winner tackled a real problem in a novel way—applying agentic AI creatively to scenarios we hadn’t seen before. Usability. Winners demonstrated that their agents are not only functional but also genuinely loved by users. Builders tested their ideas, gathered peer or user feedback, and iterated—showing clear evidence of why people would continue to use these agents in real-world settings. Impact. Every winning agent delivered measurable value. Whether saving meaningful amounts of time, improving efficiency or productivity, increasing revenue potential, or enhancing customer satisfaction, these agents showed clear qualitative or quantitative results. Together, these submissions represent what great agent-building looks like in practice—and how those agents can translate into practical impact right away. Grand prize winners To recognize their achievement and support their continued journey as an AI builder, each grand prize winner will receive a Microsoft Surface Pro. Winners (listed in alphabetical order) Agent names Built with David Díaz Merino LicitaAI Microsoft Foundry Angelo Fabrizio Dileo Aura | Buyer Persona Agent Agent Builder in Microsoft 365 Copilot Steve Glasspool Book Genie Agent Builder in Microsoft 365 Copilot Sunny Hwang NZ Free Course Finder Agent Builder in Microsoft 365 Copilot Rohit Jagadale CR Sentinel Agent Microsoft Copilot Studio Andrejs Karpovs EU-AI-Act-Compliance-Assessment Microsoft Foundry Salma Kudapali Internal Mobility & Skills Opportunity Broker Microsoft Foundry Arianna Manchia PackAgent Microsoft Copilot Studio Pablo Santisteban Fernández LeadMasterAI Microsoft Copilot Studio Sai Sirisha Return-to-Work Agent Builder in Microsoft 365 Copilot Abhishek Tarafdar Governed Compliance Intelligence System (GCIS) Microsoft Foundry Apoorva Vairagi Compliance Intake Orchestrator Microsoft Copilot Studio Whether built with Agent Builder in Microsoft 365 Copilot, Microsoft Copilot Studio, or Microsoft Foundry, each agent reflects a clear job to be done and a strong understanding of how agentic AI can create real-world benefits. Winner spotlight One of the most rewarding aspects of the Agent-a-thon was hearing directly from participants about what they built and the difference that their new agents can make. "I built a four‑agent pipeline on Microsoft Foundry that monitors Spain’s public procurement platform, scores tenders against our company profile, and generates a populated proposal document end‑to‑end in under two minutes... The multi‑agent architecture mapped to how BD [business development] teams actually work: one agent per role." —David Díaz Merino, AI Solution Architect, creator of LicitaAI This sentiment captures what the Agent-a-thon is all about—lowering the barriers to building, empowering experimentation, and helping people to realize that they can be capable AI creators, regardless of their starting point and skill level. Again, congratulations to all of our winners, and thank you to everyone who joined us in exploring what’s possible and driving innovation, one agent at a time. Sign up for the next Agent-a-thon If you missed this one, register for the next Microsoft Agent-a-thon, which begins on May 6, 2026, across three time zones, making it easier for builders everywhere to participate, learn, and create together. Whether you’re new to building agents or ready to deepen your existing expertise, this is your opportunity to take advantage of immersive learning, experiment with Copilot Studio and Foundry, and turn bold ideas into working agents. To get ready for the event, check out the training opportunities in AI Skills Navigator, our agentic learning space that brings together AI, cloud, and security training into one seamless, connected skilling experience to help you build career skills.What's New in Microsoft EDU - March 2026
Join us on Wednesday, March 25th, 2026 for our latest "What's New in Microsoft EDU" webinar! We will be covering all of the latest product updates from Microsoft Education. These 30-minute webinars are put on by the Microsoft Education Product Management group and happen once per month, this month both 8:00am Pacific Time and 4:00pm Pacific time to cover as many global time zones as possible around the world. And don’t worry – we’ll be recording these and posting on our Microsoft Education YouTube channel in the new “What’s New in Microsoft EDU” playlist, so you’ll always to able to watch later or share with others! Here is our March 2026 webinar agenda: 1) M365 Copilot and AI updates for Educators and Students - Modify Existing Content - Minecraft EDU Lesson Plans - New Learning Activities: Fill in the Blanks, Matching and Self-Quizzing - Study & Learn agent for studnets 2) Learning Zone General Availability and the Copilot+ PC 3) Microsoft 365 LTI and Teach Module for Learning Management Systems 4) AMA - Ask Microsoft EDU Anything (Q&A) We look forward to having you attend the event! How to sign up 📅 OPTION 1: March 25th, Wednesday @ 8:00am Pacific Time Register here 📅 OPTION 2: March 25th, Wednesday @ 4:00pm Pacific Time Register here This is what the webinar portal will look like when you register: We look forward to seeing you there! Mike Tholfsen Group Product Manager Microsoft EducationPartner Blog | Azure updates for partners: February 2026
AI is shifting quickly from experimentation to measurable impact. Customers are moving faster and asking for AI that scales profitably, reliably, and globally, without added risk or complexity. With the latest platform investments, Microsoft Azure provides a strong foundation for AI at scale, creating an opportunity for you to differentiate your services. Since the start of the year, we’ve shared updates that help you stay current on Azure, from expanding global AI data center capacity through our Community-First AI Infrastructure initiative, to introducing Maia 200, our latest AI inference accelerator designed to deliver competitive performance per dollar and support the move from AI pilot to production with confidence. To empower you to turn these platform investments into sharper customer conversations, join the March 10 partner webinar for an early preview of key announcements from the Microsoft Azure Summit: Migrate and Modernize with Agentic AI. You will get a clear view of what’s changing, why it matters, and how to engage customers with confidence as they move from migration to modernization and AI-driven growth. Continue reading hereNew Microsoft Certified: Azure Databricks Data Engineer Associate Certification
As a data engineer, you understand that AI performance depends directly on the quality of its data. If the data isn’t clean, well-managed, and accessible at scale, even the most sophisticated AI models won’t perform as expected. Introducing the Microsoft Certified: Azure Databricks Data Engineer Associate Certification, designed to prove that you have the skills required to build and operate reliable data systems by using Azure Databricks. To earn the Certification, you need to pass Exam DP-750: Implementing Data Engineering Solutions Using Azure Databricks, currently in beta. Is this Certification right for you? This Certification offers you the opportunity to prove your skills and validate your expertise in the following areas: Core technical skills Ingesting, transforming, and modeling data using SQL and Python Building production data pipelines on Azure Databricks Implementing software development lifecycle (SDLC) practices with Git-based workflows Integrating Azure Databricks with key Microsoft services, such as Azure Storage, Azure Data Factory, Azure Monitor, Azure Key Vault, and Microsoft Entra ID Governance and security Securing and governing data with Unity Catalog and Microsoft Purview Applying workspace, cluster, and data-level security best practices Performance and reliability Optimizing compute, caching, partitioning, and Delta Lake design patterns Troubleshooting and resolving issues with jobs and pipelines Managing workloads across development, staging, and production For engineers already familiar with Azure Databricks, this Certification bridges the gap between general Azure Databricks skills and the Azure‑specific architecture, security, and operational patterns that employers increasingly expect. Ready to prove your skills? The first 300 candidates can save 80% Take advantage of the discounted beta exam offer. The first 300 people who take Exam DP-750 (beta) on or before April 2, 2026, can get 80% off. To receive the discount, when you register for the exam and are prompted for payment, use code DP750Deltona. This is not a private access code. The seats are offered on a first-come, first-served basis. As noted, you must take the exam on or before April 2, 2026. Please note that this discount is not available in Turkey, Pakistan, India, or China. How to prepare Get ready to take Exam DP-750 (beta): Review the Exam DP-750 (beta) exam page for details. The Exam DP-750 study guide explores key topics covered in the exam. Work through the Plan on Microsoft Learn: Get Exam‑Ready for DP‑750: Azure Databricks Data Engineer Associate Certification. Need other preparation ideas? Check out Just How Does One Prepare for Beta Exams? You can take Certification exams online, from your home or office. Learn what to expect in Online proctored exams: What to expect and how to prepare. Interested in unlocking more Azure Databricks expertise? Grow your skills and take the next step by exploring Databricks credentials and show what you can do with Azure Databricks. Ready to get started? Remember, only the first 300 candidates can get 80% Exam DP-750 (beta) with code DP750Deltona on or before April 2, 2026. Beta exam rescoring begins when the exam goes live, with final results released approximately 10 days later. For more details, read Creating high-quality exams: The path from beta to live. Stay tuned for general availability of this Certification in early May 2026. Get involved: Help shape future Microsoft Credentials Join our Microsoft Worldwide Learning SME Group for Credentials on LinkedIn for beta exam alerts and opportunities to help shape future Microsoft learning and assessments. Additional information For more cloud and AI Certification updates, read our recent blog post, The AI job boom is here. Are you ready to showcase your skills? Explore Microsoft Credentials on AI Skills Navigator.Copilot Pages & Notebooks, Microsoft Loop: IT Admin Update – December 2025
For background, check out last year's Nov 2024 IT Admin update. Here's this year's progress and summary: Many key governance, lifecycle, and compliance features for Loop workspaces and Copilot Pages & Notebooks are now available. Learn more here Key deliverables remaining: M365 Group enforcement for shared Loop workspaces Departed User workflows for Copilot Pages, Notebooks, and the My workspace in Loop Multi-Geo Create in user's PDL for shared Loop workspaces Read the rest for details What’s Delivered (since Nov 2024) Sensitivity Labels for Loop workspaces Learn more here Guest Sharing for Loop (Entra B2B: Jul 2024 | for orgs with Sensitivity Labels: Mar 2025) Learn more here Retention Labels for Loop pages and components Learn more here Admin Management: Membership, ownership, deletion, restoration, search, filter, in SharePoint Embedded Admin Center and PowerShell for containers Learn more here Promote Members to Owners for Loop workspaces Learn more here M365 Group owned workspaces: managed by M365 Groups for workspaces created within Teams channels Learn more here Also, check out the latest from Ignite 2025 on Unlocking Productivity with Copilot Pages. What’s In Progress / Coming Soon Feature / Scenario Status Target Date Notes Enforce Microsoft 365 group-owned Loop workspaces In development Q1 CY'26 - 422725 IT policy to require Microsoft 365 groups for lifecycle management of shared Loop workspaces Multi-Geo Create In development Q4 CY'25 - 421616 All new Loop workspaces saved in creator’s PDL geo Departed User Workflow In development Q1 CY’26 - 421612 Temporary or permanent reassignment of existing user-owned containers, copy capability for data URL to Open Containers in app In development Q1 CY'26 - 421612 Application Redirect URL that opens in app when clicked if user has permissions User-Accessible Recycle Bin In development H1 CY’26 - 421615 Restore deleted Copilot Pages, Notebooks from Microsoft 365 Copilot app, restore deleted workspaces from Loop app Groups as Members (tenant-owned) In development H1 CY’26 Invite Microsoft 365 groups as members to Notebooks and workspaces Graph APIs for management In development H1 CY'26 For organizations with dev teams and in house management tools Read-only members Paused Due to lower overall feedback volumes, this work is paused Target date disclaimer: dates and features are estimates and may change. For the latest status, see the Microsoft 365 Public Roadmap links. Instead of creating and repeating content directly in the post this year, our IT Admin documentation on learn.microsoft.com and the Microsoft 365 Public Roadmap has been updated based on the above. We recognize that lack of some of these capabilities may still block your rollout. Please drop questions in the comments or reach out to us through your account team. We're excited to be enabling the rollouts of Copilot Pages, Notebooks, and Loop workspaces in your organization.Build a Fully Offline RAG App with Foundry Local: No Cloud Required
A practical guide to building an on-device AI support agent using Retrieval-Augmented Generation, JavaScript, and Microsoft Foundry Local. The Problem: AI That Can't Go Offline Most AI-powered applications today are firmly tethered to the cloud. They assume stable internet, low-latency API calls, and the comfort of a managed endpoint. But what happens when your users are in an environment with zero connectivity a gas pipeline in a remote field, a factory floor, an underground facility? That's exactly the scenario that motivated this project: a fully offline RAG-powered support agent that runs entirely on a laptop. No cloud. No API keys. No outbound network calls. Just a local model, a local vector store, and domain-specific documents all accessible from a browser on any device. The Gas Field Support Agent - running entirely on-device What is RAG and Why Should You Care? Retrieval-Augmented Generation (RAG) is a pattern that makes language models genuinely useful for domain-specific tasks. Instead of hoping the model "knows" the answer from pre-training, you: Retrieve relevant chunks from your own documents Augment the model's prompt with those chunks as context Generate a response grounded in your actual data The result: fewer hallucinations, traceable answers, and an AI that works with your content. If you're building internal tools, customer support bots, field manuals, or knowledge bases, RAG is the pattern you want. Why fully offline? Data sovereignty, air-gapped environments, field operations, latency-sensitive workflows, and regulatory constraints all demand AI that doesn't phone home. Running everything locally gives you complete control over your data and eliminates any external dependency. The Tech Stack This project is deliberately simple — no frameworks, no build steps, no Docker: Layer Technology Why AI Model Foundry Local + Phi-3.5 Mini Runs locally, OpenAI-compatible API, no GPU needed Backend Node.js + Express Lightweight, fast, universally known Vector Store SQLite via better-sqlite3 Zero infrastructure, single file on disk Retrieval TF-IDF + cosine similarity No embedding model required, fully offline Frontend Single HTML file with inline CSS No build step, mobile-responsive, field-ready The total dependency footprint is just four npm packages: express , openai , foundry-local-sdk , and better-sqlite3 . Architecture Overview The system has five layers — all running on a single machine: Five-layer architecture: Client → Server → RAG Pipeline → Data → AI Model Client Layer — A single HTML file served by Express, with quick-action buttons and responsive chat Server Layer — Express.js handles API routes for chat (streaming + non-streaming), document upload, and health checks RAG Pipeline — The chat engine orchestrates retrieval and generation; the chunker handles TF-IDF vectorization Data Layer — SQLite stores document chunks and their TF-IDF vectors; source docs live as .md files AI Layer — Foundry Local runs Phi-3.5 Mini Instruct on CPU/NPU, exposing an OpenAI-compatible API Getting Started in 5 Minutes You need two prerequisites: Node.js 20+ — nodejs.org Foundry Local — Microsoft's on-device AI runtime: Terminal winget install Microsoft.FoundryLocal Then clone, install, ingest, and run: git clone https://github.com/leestott/local-rag.git cd local-rag npm install npm run ingest # Index the 20 gas engineering documents npm start # Start the server + Foundry Local Open http://127.0.0.1:3000 and start chatting. Foundry Local auto-downloads Phi-3.5 Mini (~2 GB) on first run. How the RAG Pipeline Works Let's trace what happens when a user asks: "How do I detect a gas leak?" RAG query flow: Browser → Server → Vector Store → Model → Streaming response Step 1: Document Ingestion Before any queries happen, npm run ingest reads every .md file from the docs/ folder, splits each into overlapping chunks (~200 tokens, 25-token overlap), computes a TF-IDF vector for each chunk, and stores everything in SQLite. Chunking example docs/01-gas-leak-detection.md → Chunk 1: "Gas Leak Detection – Safety Warnings: Ensure all ignition..." → Chunk 2: "...sources are eliminated. Step-by-step: 1. Perform visual..." → Chunk 3: "...inspection of all joints. 2. Check calibration date..." The overlap ensures no information falls between chunk boundaries — a critical detail in any RAG system. Step 2: Query → Retrieval When the user sends a question, the server converts it into a TF-IDF vector, compares it against every stored chunk using cosine similarity, and returns the top-K most relevant results. For 20 documents (~200 chunks), this executes in under 10ms. src/vectorStore.js /** Retrieve top-K most relevant chunks for a query. */ search(query, topK = 5) { const queryTf = termFrequency(query); const rows = this.db.prepare("SELECT * FROM chunks").all(); const scored = rows.map((row) => { const chunkTf = new Map(JSON.parse(row.tf_json)); const score = cosineSimilarity(queryTf, chunkTf); return { ...row, score }; }); scored.sort((a, b) => b.score - a.score); return scored.slice(0, topK).filter((r) => r.score > 0); } Step 3: Prompt Construction The retrieved chunks are injected into the prompt alongside system instructions: Prompt structure System: You are an offline gas field support agent. Safety-first... Context: [Chunk 1: Gas Leak Detection – Safety Warnings...] [Chunk 2: Gas Leak Detection – Step-by-step...] [Chunk 3: Purging Procedures – Related safety...] User: How do I detect a gas leak? Step 4: Generation + Streaming The prompt is sent to Foundry Local via the OpenAI-compatible API. The response streams back token-by-token through Server-Sent Events (SSE) to the browser: Safety-first response with structured guidance Expandable sources with relevance scores Foundry Local: Your Local AI Runtime Foundry Local is what makes the "offline" part possible. It's a runtime from Microsoft that runs small language models (SLMs) on CPU or NPU — no GPU required. It exposes an OpenAI-compatible API and manages model downloads, caching, and lifecycle automatically. The integration code is minimal if you've used the OpenAI SDK before, this will feel instantly familiar: src/chatEngine.js import { FoundryLocalManager } from "foundry-local-sdk"; import { OpenAI } from "openai"; // Start Foundry Local and load the model const manager = new FoundryLocalManager(); const modelInfo = await manager.init("phi-3.5-mini"); // Use the standard OpenAI client — pointed at the local endpoint const client = new OpenAI({ baseURL: manager.endpoint, apiKey: manager.apiKey, }); // Chat completions work exactly like the cloud API const stream = await client.chat.completions.create({ model: modelInfo.id, messages: [ { role: "system", content: "You are a helpful assistant." }, { role: "user", content: "How do I detect a gas leak?" } ], stream: true, }); Portability matters Because Foundry Local uses the OpenAI API format, any code you write here can be ported to Azure OpenAI or OpenAI's cloud API with a single config change. You're not locked in. Why TF-IDF Instead of Embeddings? Most RAG tutorials use embedding models for retrieval. We chose TF-IDF for this project because: Fully offline — no embedding model to download or run Zero latency — vectorization is instantaneous (just math on word frequencies) Good enough — for a curated collection of 20 domain-specific documents, TF-IDF retrieves the right chunks reliably Transparent — you can inspect the vocabulary and weights, unlike neural embeddings For larger collections (thousands of documents) or when semantic similarity matters more than keyword overlap, you'd swap in an embedding model. But for this use case, TF-IDF keeps the stack simple and dependency-free. Mobile-Responsive Field UI Field engineers use this app on phones and tablets often wearing gloves. The UI is designed for harsh conditions with a dark, high-contrast theme, large touch targets (minimum 48px), and horizontally scrollable quick-action buttons. Desktop view Mobile view The entire frontend is a single index.html file — no React, no build step, no bundler. This keeps the project accessible and easy to deploy anywhere. Runtime Document Upload Users can upload new documents without restarting the server. The upload endpoint receives markdown content, chunks it, computes TF-IDF vectors, and inserts the chunks into SQLite — all in memory, immediately available for retrieval. Drag-and-drop document upload with instant indexing Adapt This for Your Own Domain This project is a scenario sample designed to be forked and customized. Here's the three-step process: 1. Replace the Documents Delete the gas engineering docs in docs/ and add your own .md files with optional YAML front-matter: docs/my-procedure.md --- title: Troubleshooting Widget Errors category: Support id: KB-001 --- # Troubleshooting Widget Errors ...your content here... 2. Edit the System Prompt Open src/prompts.js and rewrite the instructions for your domain: src/prompts.js export const SYSTEM_PROMPT = `You are an offline support agent for [YOUR DOMAIN]. Rules: - Only answer using the retrieved context - If the answer isn't in the context, say so - Use structured responses: Summary → Details → Reference `; 3. Tune the Retrieval Adjust chunking and retrieval parameters in src/config.js : src/config.js export const config = { model: "phi-3.5-mini", chunkSize: 200, // smaller = more precise, less context per chunk chunkOverlap: 25, // prevents info from falling between chunks topK: 3, // chunks per query (more = richer context, slower) }; Extending to Multi-Agent Architectures Once you have a working RAG agent, the natural next step is multi-agent orchestration where specialized agents collaborate to handle complex workflows. With Foundry Local's OpenAI-compatible API, you can compose multiple agent roles on the same machine: Multi-agent concept // Each agent is just a different system prompt + RAG scope const agents = { safety: { prompt: safetyPrompt, docs: "safety/*.md" }, diagnosis: { prompt: diagnosisPrompt, docs: "faults/*.md" }, procedure: { prompt: procedurePrompt, docs: "procedures/*.md" }, }; // Router determines which agent handles the query function route(query) { if (query.match(/safety|warning|hazard/i)) return agents.safety; if (query.match(/fault|error|code/i)) return agents.diagnosis; return agents.procedure; } // Each agent uses the same Foundry Local model endpoint const response = await client.chat.completions.create({ model: modelInfo.id, messages: [ { role: "system", content: selectedAgent.prompt }, { role: "system", content: `Context:\n${retrievedChunks}` }, { role: "user", content: userQuery } ], stream: true, }); This pattern lets you build specialized agent pipelines a triage agent routes to the right specialist, each with its own document scope and system prompt, all running on the same local Foundry instance. For production multi-agent systems, explore Microsoft Foundry for cloud-scale orchestration when connectivity is available. Local-first, cloud-ready Start with Foundry Local for development and offline scenarios. When your agents need cloud scale, swap to Azure AI Foundry with the same OpenAI-compatible API your agent code stays the same. Key Takeaways 1 RAG = Retrieve + Augment + Generate Ground your AI in real documents — dramatically reducing hallucination and making answers traceable. 2 Foundry Local makes local AI accessible OpenAI-compatible API running on CPU/NPU. No GPU required. No cloud dependency. 3 TF-IDF + SQLite is viable For small-to-medium document collections, you don't need a dedicated vector database. 4 Same API, local or cloud Build locally with Foundry Local, deploy with Azure OpenAI — zero code changes. What's Next? Embedding-based retrieval — swap TF-IDF for a local embedding model for better semantic matching Conversation memory — persist chat history across sessions Multi-agent routing — specialized agents for safety, diagnostics, and procedures PWA packaging — make it installable as a standalone app on mobile devices Hybrid retrieval — combine keyword search with semantic embeddings for best results Get the code Clone the repo, swap in your own documents, and start building: git clone https://github.com/leestott/local-rag.git github.com/leestott/local-rag — MIT licensed, contributions welcome. Open source under the MIT License. Built with Foundry Local and Node.js.On-demand webinar: Maximize the Cost Efficiency of AI Agents on Azure
AI agents are quickly becoming central to how organizations automate work, engage customers, and unlock new insights. But as adoption accelerates, so do questions about cost, ROI, and long-term sustainability. That’s exactly what the Maximize the Cost Efficiency of AI Agents on Azure webinar is designed to address. The webinar will provide practical guidance on building and scaling AI agents on Azure with financial discipline in mind. Rather than focusing only on technology, the session helps learners connect AI design decisions to real business outcomes—covering everything from identifying high-impact use cases and understanding cost drivers to forecasting ROI. Whether you’re just starting your AI journey or expanding AI agents across the enterprise, the session will equip you with strategies to make informed, cost-conscious decisions at every stage—from architecture and model selection to ongoing optimization and governance. Who should attend? If you are in one of these roles and are a decision maker or can influence decision makers in AI decisions or need to show ROI metrics on AI, this session is for you. Developer Administrator Solution Architect AI Engineer Business Analyst Business User Technology Manager Why attending the webinar? In the webinar, you’ll hear how to translate theory into real-world scenarios, walk through common cost pitfalls, and show how organizations are applying these principles in practice. Most importantly, the webinar helps you connect the dots faster, turning what you’ve learned into actionable insights you can apply immediately, ask questions live, and gain clarity on how to maximize ROI while scaling AI responsibly. If you care about building AI agents that are not only innovative but also efficient, governable, and financially sustainable, this training—and this webinar that complements it—are well worth your time. Missed it? Watch it on-demand Who will speak at the webinar? Your speakers will be: Carlotta Castelluccio: Carlotta is a Senior AI Advocate with the mission of helping every developer to succeed with AI, by building innovative solutions responsibly. To achieve this goal, she develops technical content, and she hosts skilling sessions, enabling her audience to take the most out of AI technologies and to have an impact on Microsoft AI products’ roadmap. Nitya Narasimhan: Nitya is a PhD and Polyglot with 25+ years of software research & development experience spanning mobile, web, cloud and AI. She is an innovator (12+ patents), a visual storyteller (@sketchtedocs), and an experienced community builder in the Greater New York area. As a senior AI Advocate on the Core AI Developer Relations team, she acts as "developer 0" for the Microsoft Foundry platform, providing product feedback and empowering AI developers to build trustworthy AI solutions with code samples, open-source curricula and content-initiatives like Model Mondays. Prior to joining Microsoft, she spent a decade in Motorola Labs working on ubiquitous & mobile computing research, founded Google Developer Groups in New York, and consulted for startups building real-time experiences for enterprise. Her current interests span Model understanding & customization, E2E Observability & Safety, and agentic AI workflows for maintainable software. Moderator Lee Stott is a Principal Cloud Advocate at Microsoft, working in the Core AI Developer Relations Team. He helps developers and organizations build responsibly with AI and cloud technologies through open-source projects, technical guidance, and global developer programs. Based in the UK, Lee brings deep hands-on experience across AI, Azure, and developer tooling. Useful resources Microsoft Learn Training Path: https://aka.ms/maximize-cost-efficiency-ai-agents-training Session Deck: https://aka.ms/maximize-cost-efficiency-ai-agents-deckMicrosoft Partners: Accelerate Your AI Journey at AgentCon 2026 (Free Community Event)
Recently, a customer asked me a question many Microsoft partners are hearing right now: “We have Copilot — how do we actually use AI to change the way we work?” That question captures where we are in the AI journey today. Organizations have moved past curiosity. Now they’re looking for trusted partners who can turn AI into real business outcomes. That’s why events like AgentCon 2026 matter. A free, community-led event built by practicioners AgentCon is not a traditional conference. It’s a free, community-driven global event organized by the Global AI Community together with Microsoft partners and ecosystem leaders. Simply put: it’s for the community, by the community. Across cities worldwide, developers, consultants, architects, and Microsoft partners come together to share practical experiences building with AI agents, Copilot, and the Microsoft platform. The focus isn’t theory — it’s implementation: What worked What didn’t What partners can apply immediately with customers This peer learning model reflects how many of us actually grow in the Microsoft ecosystem: by learning from other partners solving real problems. Why this matters for Microsoft partners The opportunity for partners is evolving quickly. Customers aren’t just asking about AI tools — they’re asking how to redesign processes, automate work, and unlock productivity using AI-powered solutions. The Microsoft AI Cloud Partner Program emphasizes partner skilling and helping customers realize value from AI investments. Community events like AgentCon accelerate that learning by bringing partners together to exchange proven approaches and practical insights. When partners upskill faster, customers succeed faster. Why attend AgentCon is designed to help partners move from AI awareness to AI delivery. As an attendee, you can expect: Practical sessions and demos from practitioners Real-world AI and agent scenarios Direct conversations with builders and peers New collaboration and co-sell opportunities You’ll leave with ideas and approaches you can bring directly into customer engagements. Why speak AgentCon thrives because partners share openly with one another. If you’ve implemented Copilot, explored AI agents, or learned lessons from customer deployments, your experience can help others accelerate their journey. Speaking at AgentCon allows you to: Share your expertise with the global partner community Build credibility within the Microsoft ecosystem Create new partnerships and opportunities Contribute to collective partner success You don’t need a perfect story — just an honest one others can learn from. Join the global AgentCon community AgentCon 2026 events takes place around the world including these upcoming events: March 9 - New York: https://aka.ms/AgentconNYC2026 April 11 - Hong Kong: https://aka.ms/AgentconHongKong2026 April 16 - Seoul: https://aka.ms/agentconLondon2026 April 22 - London: https://aka.ms/agentconSeoul2026 Each event is locally organized, community-led, and free to attend. Help shape the next phase of AI adoption AI transformation is happening now — and Microsoft partners play a critical role in guiding customers forward. AgentCon is an opportunity to learn together, share experiences, and strengthen the partner ecosystem driving AI innovation. 👉 Register or apply to speak: https://aka.ms/agentcon2026 We hope you’ll join us — and be part of the community helping customers turn AI potential into real impact.Learn to maximize your productivity at the proMX Project Operations + AI Summit 2026
As organizations accelerate AI adoption across business applications, mastering how Microsoft Dynamics 365 solutions, Copilot, and agents work together is becoming a strategic priority. Fortunately, businesses no longer need to rely on speculation — they can gain practical insights with fellow industry professionals during a unique two-day event: On April 21-22, 2026, Microsoft and proMX will jointly host the fourth edition of proMX Project Operations Summit at the Microsoft office in Munich, but this time with an AI edge. The summit brings together Dynamics 365 customers and Microsoft and proMX experts to explore how AI is reshaping project delivery, resource management, and operational decision‑making across industries. On day one, participants will discover how Dynamics 365 Project Operations, Copilot, Project Online, proMX 365 PPM, and Contact Center can strategically transform business processes and drive organizational growth. On day two, they can explore the technical side of these solutions. Secure your spot! What to expect from the summit Expert-led, actionable insights Join interactive sessions led by Microsoft and proMX experts to learn practical AI and Dynamics 365 skills you can use right away. Inspiring keynotes Gain future-focused perspectives on Dynamics 365, Copilot, and AI to prepare your organization for what’s next. In between our special guests we have Microsoft's Rupa Mantravadi, Chief Product Officer, Dynamics 365 Project Operations, Rob Nehrbas, Head of AI Business Solutions, Archana Prasad, Worldwide FastTrack Leader for Project Operations, and Mathias Klaas, Partner Development Manager. Hands-on AI workshops Take part in workshops where Sebastian Sieber, Global Technology Director (proMX) and Microsoft MVP will show the newest AI features in Dynamics 365, giving you real-world experience with innovative tools. Connect with industry leaders Engage with experts through Q&A sessions, round tables, and personalized Connect Meetings for tailored guidance on your business needs. Real customer success stories Hear case studies from proMX customers who are already using Dynamics 365 solutions and learn proven strategies for successful digital transformation. Who should attend? This summit is tailored for business and IT decision-makers that are using Dynamics 365 solutions and want to drive more business impact with AI, but also for those who might be planning to move away from other project management solutions such as Project Online and need practical guidance grounded in real-life implementations. Date: Apr 21 & 22, 2026 | 2 -Days event Location: Microsoft Munich, Walter-Gropius Straße 5, Munich, Bavaria, DE, 80807 Ready to maximize your productivity? Register here.
