AI Upskilling Framework Level 3 Building
The Global AI Community is excited to bring you the latest updates on AI Upskilling Framework Level 3 Building, straight from Microsoft Ignite! This session dives deep into advanced concepts for building agentic workflows and showcases new announcements that will help developers accelerate their Agentic AI journey.
Transforming Data migration using Azure Copilot
Introduction Data migration is critical, yet it is one of the most complex tasks in any cloud adoption journey. Whether you’re moving workloads from on-premises environments, consolidating hybrid deployments, or transitioning from other cloud providers, the migration process involves multiple tools, intricate planning, and risk management. What’s New in Azure Copilot With the new “Storage Migration Solutions Advisor” capability in Azure Copilot, Microsoft is transforming this experience into a conversational, AI-driven workflow that accelerates decision-making and reduces operational friction. Why This Matters Traditionally, customers faced challenges such as: Weeks of advisory time spent choosing the right migration tool amongst the many (Azure Storage Mover, AzCopy, Data Box, File Sync etc., and various Partner solutions). High support overhead due to missteps during migration if a sub-optimal tool or service is used. The Storage Migration Solutions Advisor feature introduces: Conversational Guidance: Share your migration needs with Copilot, like talking with an Azure advisor. Scenario-Based Recommendations: Tailored suggestions based on transfer data size, protocol, and bandwidth. Expanded Coverage: Supports on-premises to Azure, cloud-to-cloud (AWS/GCP to Azure), and hybrid scenarios. Native and Partner solutions: Copilot can recommend Microsoft-native (1P) solutions and third-party (3P) tools for specialized scenarios —ensuring flexibility for enterprise needs. User Workflow: Step-by-Step Initiate Migration: Start with a prompt like “How can I migrate my data into Azure?” or “What’s the best tool for moving 1 PB from AWS S3 to Azure Blob?” Provide Details: Copilot will guide you by asking for details about your requirement, such as source type (e.g., NAS, SAN, AWS S3, GCS), protocol (e.g., NFS, SMB, S3 API), target (e.g., Azure Blob, Files, Elastic SAN), data size, and bandwidth. Azure and Partner Solutions: Based on your requirements, Copilot recommends the best-fit Azure solution. If a partner solution is better suited to your requirement, Copilot will also select and recommend the appropriate solution with links to its documentation and/or its Azure marketplace page. Examples Copilot generates recommendations for migrating an on-premises file share to Azure Files. Figure 1 Prompt from user invokes Copilot Migration recommendation workflow Figure 2 Copilot understanding protocols that customer environment has access to Figure 3 Copilot asking user's target Storage type Figure 4 Copilot gathering inputs on data size, network bandwidth availability and transfer direction Figure 5 Copilot recommendation for user scenario Copilot recommends Partner solutions for specialized migration scenarios Figure 1 Prompt from user invokes Copilot Migration recommendation workflow Figure 2 Copilot understanding protocols that customer environment has access to Figure 3 Copilot asking user's target Storage type Figure 4 Copilot gathering inputs on data size, network bandwidth availability and transfer direction Figure 5 Copilot recommendation for user scenario Pro Tips Run a small proof-of-concept migration to estimate throughput and timing, especially for large datasets or small file sizes. Combine Copilot’s recommendations with Azure Storage Discovery for visibility into your storage estate after migration. Getting Started Navigate to Azure Portal → Copilot. Try prompts like: o “Help me migrate an NFS share to Azure Files.” o “What’s the best tool for moving 1 PB from AWS S3 to Azure Blob?” Explore Manage and migrate storage accounts using Azure Copilot | Microsoft Learn for detailed guidance. Ready to simplify your migration journey? Start using Azure Copilot’s Storage Migration Solutions Advisor today and experience AI-driven efficiency for your cloud transformation.Exploring the Future of AI Agents with Microsoft Foundry
Why Agentic AI Matters AI agents are no longer a distant vision—they’re here and transforming how businesses operate. According to industry analysts: Over 1 billion AI agents are expected to be in use by 2028. 80% of organisations plan to integrate agents within the next 2–3 years. By 2026, 40% of enterprise apps will include task-specific AI agents. Why this surge? Agents address critical challenges such as inefficiencies in manual processes, human error, lack of visibility, and scalability issues. They enable autonomous decision-making, with projections suggesting that by 2028, half of day-to-day work decisions will be made autonomously. From Chatbots to Intelligent Agents As Mary Joe highlighted, early chatbots relied on rigid rules and regular expressions, often leading to frustrating user experiences. The introduction of large language models (LLMs) changed the game, making interactions more natural. But true autonomy, where systems act on our behalf, required more than conversational AI. Agentic AI combines: Reasoning and planning capabilities. Tools and APIs for real-world actions. Memory for learning and improving over time. This evolution moves us beyond simple input-output interactions to intelligent systems that can execute workflows, validate data, and deliver outcomes. Microsoft Foundry: Your Platform for Building Agents Microsoft Foundry offers a Platform-as-a-Service (PaaS) approach for creating AI agents, striking a balance between control and ease of use. Key components include: Model Catalogue: Access models from OpenAI, Anthropic, Mistral, and more. Foundry Agent Service: Build and customise agents with integrated tools. Foundry IQ: Knowledge grounding for accurate responses. Control Plane: Ensures safety, trust, and observability in production. Whether you need full control (Infrastructure-as-a-Service) or simplicity (Software-as-a-Service via Copilot Studio), Foundry provides flexibility for diverse scenarios. What Makes an AI Solution Agentic? Unlike traditional AI apps that perform narrow tasks (e.g., extracting text from receipts), agentic solutions: Analyse inputs using LLMs and system instructions. Integrate tools for actions like file search, code execution, or API calls. Retain memory for contextual learning. Operate autonomously across workflows. Real-World Use Cases Agentic AI unlocks new possibilities across industries: Expense Management: Automate claims and approvals. Employee Onboarding: Personalised learning paths and skills navigation. Customer Support: Intelligent assistants for FAQs and troubleshooting. Data Analytics: Interactive insights and reporting with Fabric agents. Multi-agent systems can coordinate complex tasks, with specialised agents handling subtasks under a central orchestrator. Getting Started with Microsoft Foundry Creating your first agent is simple: Sign in at https://ai.azure.com and create a Foundry project. Select a model (e.g., GPT-4.1 mini) and configure deployment options. Customise instructions to define your agent’s persona and tasks. Add tools like file search or code interpreter for extended functionality. Test and iterate using the agent playground, then export code to Visual Studio Code for deployment. For detailed guidance, explore the https://learn.microsoft.com/training. Follow the skilling plan for this series Plans | Microsoft Learn Get started with AI Agents https://aka.ms/ai-agents-fundamentals Join the Community Stay connected and keep learning: Discord: Engage with developers building agents. https://aka.ms/foundry/discord GitHub Discussions: Share ideas and troubleshoot. https://aka.ms/foundrydevs Office Hours: Get direct support from product teams. Final Thoughts Agentic AI is reshaping the way we work, enabling systems to act, learn, and collaborate. With Microsoft Foundry, developers have the tools to build secure, scalable, and intelligent agents today not tomorrow. Join the sessions at https://aka.ms/AzureSkilling-Ignite/25AI Dev Days 2025: Your Gateway to the Future of AI Development
What’s in Store? Day 1 – 10 December: Video Link Building AI Applications with Azure, GitHub, and Foundry Explore cutting-edge topics like: Agentic DevOps Azure SRE Agent Microsoft Foundry MCP Models for AI innovation Day 2 – 11 December Agenda: Video Link Using AI to Boost Developer Productivity Get hands-on with: Agent HQ VS Code & Visual Studio 2026 GitHub Copilot Coding Agent App Modernisation Strategies Why Join? Hands-on Labs: Apply the latest product features immediately. Highlights from Microsoft Ignite & GitHub Universe 2025: Stay ahead of the curve. Global Reach: Local-language workshops for LATAM and EMEA coming soon. You’ll recognise plenty of familiar faces in the lineup – don’t miss the chance to connect and learn from the best! 👉 Register now and share widely across your networks – there’s truly something for everyone! https://aka.ms/ai-dev-daysAzure API Management Your Auth Gateway For MCP Servers
The Model Context Protocol (MCP) is quickly becoming the standard for integrating Tools 🛠️ with Agents 🤖 and Azure API Management is at the fore-front, ready to support this open-source protocol 🚀. You may have already encountered discussions about MCP, so let's clarify some key concepts: Model Context Protocol (MCP) is a standardized way, (a protocol), for AI models to interact with external tools, (and either read data or perform actions) and to enrich context for ANY language models. AI Agents/Assistants are autonomous LLM-powered applications with the ability to use tools to connect to external services required to accomplish tasks on behalf of users. Tools are components made available to Agents allowing them to interact with external systems, perform computation, and take actions to achieve specific goals. Azure API Management: As a platform-as-a-service, API Management supports the complete API lifecycle, enabling organizations to create, publish, secure, and analyze APIs with built-in governance, security, analytics, and scalability. New Cool Kid in Town - MCP AI Agents are becoming widely adopted due to enhanced Large Language Model (LLM) capabilities. However, even the most advanced models face limitations due to their isolation from external data. Each new data source requires custom implementations to extract, prepare, and make data accessible for any model(s). - A lot of heavy lifting. Anthropic developed an open-source standard - the Model Context Protocol (MCP), to connect your agents to external data sources such as local data sources (databases or computer files) or remote services (systems available over the internet through e.g. APIs). MCP Hosts: LLM applications such as chat apps or AI assistant in your IDEs (like GitHub Copilot in VS Code) that need to access external capabilities MCP Clients: Protocol clients that maintain 1:1 connections with servers, inside the host application MCP Servers: Lightweight programs that each expose specific capabilities and provide context, tools, and prompts to clients MCP Protocol: Transport layer in the middle At its core, MCP follows a client-server architecture where a host application can connect to multiple servers. Whenever your MCP host or client needs a tool, it is going to connect to the MCP server. The MCP server will then connect to for example a database or an API. MCP hosts and servers will connect with each other through the MCP protocol. You can create your own custom MCP Servers that connect to your or organizational data sources. For a quick start, please visit our GitHub repository to learn how to build a remote MCP server using Azure Functions without authentication: https://aka.ms/mcp-remote Remote vs. Local MCP Servers The MCP standard supports two modes of operation: Remote MCP servers: MCP clients connect to MCP servers over the Internet, establishing a connection using HTTP and Server-Sent Events (SSE), and authorizing the MCP client access to resources on the user's account using OAuth. Local MCP servers: MCP clients connect to MCP servers on the same machine, using stdio as a local transport method. Azure API Management as the AI Auth Gateway Now that we have learned that MCP servers can connect to remote services through an API. The question now rises, how can we expose our remote MCP servers in a secure and scalable way? This is where Azure API Management comes in. A way that we can securely and safely expose tools as MCP servers. Azure API Management provides: Security: AI agents often need to access sensitive data. API Management as a remote MCP proxy safeguards organizational data through authentication and authorization. Scalability: As the number of LLM interactions and external tool integrations grows, API Management ensures the system can handle the load. Security remains to be a critical piece of building MCP servers, as agents will need to securely connect to protected endpoints (tools) to perform certain actions or read protected data. When building remote MCP servers, you need a way to allow users to login (Authenticate) and allow them to grant the MCP client access to resources on their account (Authorization). MCP - Current Authorization Challenges State: 4/10/2025 Recent changes in MCP authorization have sparked significant debate within the community. 🔍 𝗞𝗲𝘆 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀 with the Authorization Changes: The MCP server is now treated as both a resource server AND an authorization server. This dual role has fundamental implications for MCP server developers and runtime operations. 💡 𝗢𝘂𝗿 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻: To address these challenges, we recommend using 𝗔𝘇𝘂𝗿𝗲 𝗔𝗣𝗜 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 as your authorization gateway for remote MCP servers. 🔗For an enterprise-ready solution, please check out our azd up sample repo to learn how to build a remote MCP server using Azure API Management as your authentication gateway: https://aka.ms/mcp-remote-apim-auth The Authorization Flow The workflow involves three core components: the MCP client, the APIM Gateway, and the MCP server, with Microsoft Entra managing authentication (AuthN) and authorization (AuthZ). Using the OAuth protocol, the client starts by calling the APIM Gateway, which redirects the user to Entra for login and consent. Once authenticated, Entra provides an access token to the Gateway, which then exchanges a code with the client to generate an MCP server token. This token allows the client to communicate securely with the server via the Gateway, ensuring user validation and scope verification. Finally, the MCP server establishes a session key for ongoing communication through a dedicated message endpoint. Diagram source: https://aka.ms/mcp-remote-apim-auth-diagram Conclusion Azure API Management (APIM) is an essential tool for enterprise customers looking to integrate AI models with external tools using the Model Context Protocol (MCP). In this blog, we've emphasized the simplicity of connecting AI agents to various data sources through MCP, streamlining previously complex implementations. Given the critical role of secure access to platforms and services for AI agents, APIM offers robust solutions for managing OAuth tokens and ensuring secure access to protected endpoints, making it an invaluable asset for enterprises, despite the challenges of authentication. API Management: An Enterprise Solution for Securing MCP Servers Azure API Management is an essential tool for enterprise customers looking to integrate AI models with external tools using the Model Context Protocol (MCP). It is designed to help you to securely expose your remote MCP servers. MCP servers are still very new, and as the technology evolves, API Management provides an enterprise-ready solution that will evolve with the latest technology. Stay tuned for further feature announcements soon! Acknowledgments This post and work was made possible thanks to the hard work and dedication of our incredible team. Special thanks to Pranami Jhawar, Julia Kasper, Julia Muiruri, Annaji Sharma Ganti Jack Pa, Chaoyi Yuan and Alex Vieira for their invaluable contributions. Additional Resources MCP Client Server integration with APIM as AI gateway Blog Post: https://aka.ms/remote-mcp-apim-auth-blog Sequence Diagram: https://aka.ms/mcp-remote-apim-auth-diagram APIM lab: https://aka.ms/ai-gateway-lab-mcp-client-auth Python: https://aka.ms/mcp-remote-apim-auth .NET: https://aka.ms/mcp-remote-apim-auth-dotnet On-Behalf-Of Authorization: https://aka.ms/mcp-obo-sample 3rd Party APIs – Backend Auth via Credential Manager: Blog Post: https://aka.ms/remote-mcp-apim-lab-blog APIM lab: https://aka.ms/ai-gateway-lab-mcp YouTube Video: https://aka.ms/ai-gateway-lab-demo
🎉 Announcing General Availability of AI & RAG Connectors in Logic Apps (Standard)
We’re excited to share that a comprehensive set of AI and Retrieval-Augmented Generation (RAG) capabilities is now Generally Available in Azure Logic Apps (Standard). This release brings native support for document processing, semantic retrieval, embeddings, and grounded reasoning directly into the Logic Apps workflow engine. 🔌 Available AI Connectors in Logic Apps Standard Logic Apps (Standard) had previously previewed four AI-focused connectors that open the door for a new generation of intelligent automation across the enterprise. Whether you're processing large volumes of documents, enriching operational data with intelligence, or enabling employees to interact with systems using natural language, these connectors provide the foundation for building solutions that are smarter, faster, and more adaptable to business needs. These are now in GA. They allow teams to move from routine workflow automation to AI-assisted decisioning, contextual responses, and multi-step orchestration that reflects real business intent. Below is the full set of built-in connectors and their actions as they appear in the designer. 1. Azure OpenAI Actions Get an embedding Get chat completions Get chat completions using Prompt Template Get completion Get multiple chat completions Get multiple embeddings What this unlocks Bring natural language reasoning and structured AI responses directly into workflows. Common scenarios include guided decisioning, user-facing assistants, classification and routing, or preparing embeddings for semantic search and RAG workflows. 2. Azure AI Search Actions Delete a document Delete multiple documents Get agentic retrieval output (Preview) Index a document Index multiple documents Merge document Search vectors Search vectors with natural language What this unlocks Add vector, hybrid semantic, and natural language search directly to workflow logic. Ideal for retrieving relevant content from enterprise data, powering search-driven workflows, and grounding AI responses with context from your own documents. 3. Azure AI Document Intelligence Action Analyze document What this unlocks Document Intelligence serves as the entry point for document-heavy scenarios. It extracts structured information from PDFs, images, and forms, allowing workflows to validate documents, trigger downstream processes, or feed high-quality data into search and embeddings pipelines. 4. AI Operations Actions Chunk text with metadata Parse document with metadata What this unlocks Transform unstructured files into enriched, structured content. Enables token-aware chunking, page-level metadata, and clean preparation of content for embeddings and semantic search at scale. 🤖 Advanced AI & Agentic Workflows with AgentLoop Logic Apps (Standard) also supports AgentLoop (also Generally Available), allowing AI models to use workflow actions as tools and iterate until the task is complete. Combined with chunking, embeddings, and natural language search, this opens the door to advanced agentic scenarios such as document intelligence agents, RAG-based assistants, and iterative evaluators. Conclusion With these capabilities now built into Logic Apps Standard, teams can bring AI directly into their integration workflows without additional infrastructure or complexity. Whether you’re streamlining document-heavy processes, enabling richer search experiences, or exploring more advanced agentic patterns, these capabilities provide a strong foundation to start building today.On‑Device AI with Windows AI Foundry and Foundry Local
From “waiting” to “instant”- without sending data away AI is everywhere, but speed, privacy, and reliability are critical. Users expect instant answers without compromise. On-device AI makes that possible: fast, private and available, even when the network isn’t - empowering apps to deliver seamless experiences. Imagine an intelligent assistant that works in seconds, without sending a text to the cloud. This approach brings speed and data control to the places that need it most; while still letting you tap into cloud power when it makes sense. Windows AI Foundry: A Local Home for Models Windows AI Foundry is a developer toolkit that makes it simple to run AI models directly on Windows devices. It uses ONNX Runtime under the hood and can leverage CPU, GPU (via DirectML), or NPU acceleration, without requiring you to manage those details. The principle is straightforward: Keep the model and the data on the same device. Inference becomes faster, and data stays local by default unless you explicitly choose to use the cloud. Foundry Local Foundry Local is the engine that powers this experience. Think of it as local AI runtime - fast, private, and easy to integrate into an app. Why Adopt On‑Device AI? Faster, more responsive apps: Local inference often reduces perceived latency and improves user experience. Privacy‑first by design: Keep sensitive data on the device; avoid cloud round trips unless the user opts in. Offline capability: An app can provide AI features even without a network connection. Cost control: Reduce cloud compute and data costs for common, high‑volume tasks. This approach is especially useful in regulated industries, field‑work tools, and any app where users expect quick, on‑device responses. Hybrid Pattern for Real Apps On-device AI doesn’t replace the cloud, it complements it. Here’s how: Standalone On‑Device: Quick, private actions like document summarization, local search, and offline assistants. Cloud‑Enhanced (Optional): Large-context models, up-to-date knowledge, or heavy multimodal workloads. Design an app to keep data local by default and surface cloud options transparently with user consent and clear disclosures. Windows AI Foundry supports hybrid workflows: Use Foundry Local for real-time inference. Sync with Azure AI services for model updates, telemetry, and advanced analytics. Implement fallback strategies for resource-intensive scenarios. Application Workflow Code Example using Foundry Local: 1. Only On-Device: Tries Foundry Local first, falls back to ONNX if foundry_runtime.check_foundry_available(): # Use on-device Foundry Local models try: answer = foundry_runtime.run_inference(question, context) return answer, source="Foundry Local (On-Device)" except Exception as e: logger.warning(f"Foundry failed: {e}, trying ONNX...") if onnx_model.is_loaded(): # Fallback to local BERT ONNX model try: answer = bert_model.get_answer(question, context) return answer, source="BERT ONNX (On-Device)" except Exception as e: logger.warning(f"ONNX failed: {e}") return "Error: No local AI available" 2. Hybrid approach: On-device first, cloud as last resort def get_answer(question, context): """ Priority order: 1. Foundry Local (best: advanced + private) 2. ONNX Runtime (good: fast + private) 3. Cloud API (fallback: requires internet, less private) # in case of Hybrid approach, based on real-time scenario """ if foundry_runtime.check_foundry_available(): # Use on-device Foundry Local models try: answer = foundry_runtime.run_inference(question, context) return answer, source="Foundry Local (On-Device)" except Exception as e: logger.warning(f"Foundry failed: {e}, trying ONNX...") if onnx_model.is_loaded(): # Fallback to local BERT ONNX model try: answer = bert_model.get_answer(question, context) return answer, source="BERT ONNX (On-Device)" except Exception as e: logger.warning(f"ONNX failed: {e}, trying cloud...") # Last resort: Cloud API (requires internet) if network_available(): try: import requests response = requests.post( '{BASE_URL_AI_CHAT_COMPLETION}', headers={'Authorization': f'Bearer {API_KEY}'}, json={ 'model': '{MODEL_NAME}', 'messages': [{ 'role': 'user', 'content': f'Context: {context}\n\nQuestion: {question}' }] }, timeout=10 ) answer = response.json()['choices'][0]['message']['content'] return answer, source="Cloud API (Online)" except Exception as e: return "Error: No AI runtime available", source="Failed" else: return "Error: No internet and no local AI available", source="Offline" Demo Project Output: Foundry Local answering context-based questions offline : The Foundry Local engine ran the Phi-4-mini model offline and retrieved context-based data. : The Foundry Local engine ran the Phi-4-mini model offline and mentioned that there is no answer. Practical Use Cases Privacy-First Reading Assistant: Summarize documents locally without sending text to the cloud. Healthcare Apps: Analyze medical data on-device for compliance. Financial Tools: Risk scoring without exposing sensitive financial data. IoT & Edge Devices: Real-time anomaly detection without network dependency. Conclusion On-device AI isn’t just a trend - it’s a shift toward smarter, faster, and more secure applications. With Windows AI Foundry and Foundry Local, developers can deliver experiences that respect user specific data, reduce latency, and work even when connectivity fails. By combining local inference with optional cloud enhancements, you get the best of both worlds: instant performance and scalable intelligence. Whether you’re creating document summarizers, offline assistants, or compliance-ready solutions, this approach ensures your apps stay responsive, reliable, and user-centric. References Get started with Foundry Local - Foundry Local | Microsoft Learn What is Windows AI Foundry? | Microsoft Learn https://devblogs.microsoft.com/foundry/unlock-instant-on-device-ai-with-foundry-local/Unlocking Your First AI Solution on Azure: Practical Paths for Developers of All Backgrounds
Over the past several months, I’ve spent hundreds of hours working directly with teams—from small startups to mid-market innovators—who share the same aspiration: “We want to use AI, but where do we start?” This question comes up everywhere. It crosses industries, geographies, skill levels, and team sizes. And as developers, we often feel the pressure to “solve AI” end-to-end—model selection, prompt engineering, security, deployment pipelines, integration…. The list is long, and the learning curve can feel even longer. But here’s what we’ve learned through our work in the SMB space and what we recently shared at Microsoft Ignite (Session OD1210). The first mile of AI doesn’t have to be complex. You don’t need an army of engineers, and you don’t need to start from scratch. You just need the right path. In our Ignite on-demand session with UnifyCloud, we demonstrated two fast, developer-friendly ways to get your first AI workload running on Azure—both grounded in real-world patterns we see every day. Path 1: Build Quickly with Microsoft Foundry Templates Microsoft Foundry gives developers pre-built, customizable templates that dramatically reduce setup time. In the session, I walked through how to deploy a fully functioning AI chatbot using: Azure AI Foundry GitHub (via the Azure Samples “Get Started with AI Chat” repo) Azure Cloudshell for deployment And zero specialized infra prep With five lines of code and a few clicks, you can spin up a secure internal chatbot tailored for your business. Want responses scoped to your internal content? Want control over the model, costs, or safety filters? Want to plug in your own data sources like SharePoint, Blob Storage, or uploaded docs? You can do all of that—easily and on your terms. This “build fast” path is ideal for: Developers who want control and extensibility Teams validating AI use cases Scenarios where data governance matters Lightweight experimentation without heavy architecture upfront And most importantly, you can scale it later. Path 2: Buy a Production-Ready Solution from a Trusted Partner Not every team wants to build. Not every team has the time, the resources, or the desire to compose their own AI stack. That’s why we showcased the “buy” path with UnifyCloud’s AI Factory, a Marketplace-listed solution that lets customers deploy mature AI capabilities directly into their Azure environment, complete with optional support, management, and best practices. In the demo, UnifyCloud’s founder Vivek Bhatnagar walked through: How to navigate Microsoft Marketplace How to evaluate solution listings How to review pricing plans and support tiers How to deploy a partner-built AI app with just a few clicks How customers can accelerate their time to value without implementation overhead This path is perfect when you want: A production-ready AI solution A supported, maintained experience Minimal engineering lift Faster time to outcome Why Azure? Why Now? During the session, we also outlined three reasons developers are choosing Azure for their first AI workloads: 1. Secure, governed, safe by design Azure mitigates risk with always-on guardrails and built-in commitments to security, privacy, and policy-based control. 2. Built for production with a complete AI platform From models to agents to tools and data integrations, Azure provides an enterprise-grade environment developers can trust. 3. Developer-first innovation with agentic DevOps Azure puts developers at the center, integrating AI across the software development lifecycle to help teams build faster and smarter. The Session: Build or Buy—Two Paths, One Goal Whether you build using Azure AI Foundry or buy through Marketplace, the goal is the same: Help teams get to their first AI solution quickly, confidently, and securely. You don’t need a massive budget. You don’t need deep ML experience. You don’t need a full-time AI team. What you need is a path that matches your skills, your constraints, and your timeline. Watch the Full Ignite Session You can watch the full session on-demand now also on YouTube: OD1201 — “Unlock Your First AI Solution on Azure” It includes: The full build and buy demos Partner perspectives Deployment walkthroughs And guidance you can take back to your teams today If you want to explore the same build path we showed at Ignite: ➡️ Azure Samples – Get Started with AI Chat https://github.com/Azure-Samples/get-started-with-ai-chat Deploy it, customize it, attach your data sources, and extend it. It’s a great starting point. If you’re curious about the Marketplace path: ➡️ Search for “UnifyCloud AI Factory” on Microsoft Marketplace You’ll see support offerings, solution details, and deployment instructions. Closing Thought The gap between wanting to adopt AI and actually running AI in production is shrinking fast. Azure makes it possible for teams, especially those without deep AI experience, to take meaningful steps today. No perfect architecture required. No million-dollar budget. No wait for a future-state roadmap. Just two practical paths: Build quickly. Buy confidently. Start now. If you have questions, ideas, or want to share what you’re building, feel free to reach out here in the Developer Community. I’d love to hear what you’re creating. — Joshua Huang Microsoft AzureAzure Skilling at Microsoft Ignite 2025
The energy at Microsoft Ignite was unmistakable. Developers, architects, and technical decision-makers converged in San Francisco to explore the latest innovations in cloud technology, AI applications, and data platforms. Beyond the keynotes and product announcements was something even more valuable: an integrated skilling ecosystem designed to transform how you build with Azure. This year Azure Skilling at Microsoft Ignite 2025 brought together distinct learning experiences, over 150+ hands-on labs, and multiple pathways to industry-recognized credentials—all designed to help you master skills that matter most in today's AI-driven cloud landscape. Just Launched at Ignite Microsoft Ignite 2025 offered an exceptional array of learning opportunities, each designed to meet developers anywhere on the skilling journey. Whether you joined us in-person or on-demand in the virtual experience, multiple touchpoints are available to deepen your Azure expertise. Ignite 2025 is in the books, but you can still engage with the latest Microsoft skilling opportunities, including: The Azure Skills Challenge provides a gamified learning experience that lets you compete while completing task-based achievements across Azure's most critical technologies. These challenges aren't just about badges and bragging rights—they're carefully designed to help you advance technical skills and prepare for Microsoft role-based certifications. The competitive element adds urgency and motivation, turning learning into an engaging race against the clock and your peers. For those seeking structured guidance, Plans on Learn offer curated sets of content designed to help you achieve specific learning outcomes. These carefully assembled learning journeys include built-in milestones, progress tracking, and optional email reminders to keep you on track. Each plan represents 12-15 hours of focused learning, taking you from concept to capability in areas like AI application development, data platform modernization, or infrastructure optimization. The Microsoft Reactor Azure Skilling Series, running December 3-11, brings skilling to life through engaging video content, mixing regular programming with special Ignite-specific episodes. This series will deliver technical readiness and programming guidance in a livestream presentation that's more digestible than traditional documentation. Whether you're catching episodes live with interactive Q&A or watching on-demand later, you’ll get world-class instruction that makes complex topics approachable. Beyond Ignite: Your Continuous Learning Journey Here's the critical insight that separates Ignite attendees who transform their careers from those who simply collect swag: the real learning begins after the event ends. Microsoft Ignite is your launchpad, not your destination. Every module you start, every lab you complete, and every challenge you tackle connects to a comprehensive learning ecosystem on Microsoft Learn that's available 24/7, 365 days a year. Think of Ignite as your intensive immersion experience—the moment when you gain context, build momentum, and identify the skills that will have the biggest impact on your work. What you do in the weeks and months following determines whether that momentum compounds into career-defining expertise or dissipates into business as usual. For those targeting career advancement through formal credentials, Microsoft Certifications, Applied Skills and AI Skills Navigator, provide globally recognized validation of your expertise. Applied Skills focus on scenario-based competencies, demonstrating that you can build and deploy solutions, not simply answer theoretical questions. Certifications cover role-based scenarios for developers, data engineers, AI engineers, and solution architects. The assessment experiences include performance-based testing in dedicated Azure tenants where you complete real configuration and development tasks. And finally, the NEW AI Skills Navigator is an agentic learning space, bringing together AI-powered skilling experiences and credentials in a single, unified experience with Microsoft, LinkedIn Learning and GitHub – all in one spot Why This Matters: The Competitive Context The cloud skills race is intensifying. While our competitors offer robust training and content, Microsoft's differentiation comes not from having more content—though our 1.4 million module completions last fiscal year and 35,000+ certifications awarded speak to scale—but from integration of services to orchestrate workflows. Only Microsoft offers a truly unified ecosystem where GitHub Copilot accelerates your development, Azure AI services power your applications, and Azure platform services deploy and scale your solutions—all backed by integrated skilling content that teaches you to maximize this connected experience. When you continue your learning journey after Ignite, you're not just accumulating technical knowledge. You're developing fluency in an integrated development environment that no competitor can replicate. You're learning to leverage AI-powered development tools, cloud-native architectures, and enterprise-grade security in ways that compound each other's value. This unified expertise is what transforms individual developers into force-multipliers for their organizations. Start Now, Build Momentum, Never Stop Microsoft Ignite 2025 offered the chance to compress months of learning into days of intensive, hands-on experience, but you can still take part through the on-demand videos, the Global Ignite Skills Challenge, visiting the GitHub repos for the /Ignite25 labs, the Reactor Azure Skilling Series, and the curated Plans on Learn provide multiple entry points regardless of your current skill level or preferred learning style. But remember: the developers who extract the most value from Ignite are those who treat the event as the beginning, not the culmination, of their learning journey. They join hackathons, contribute to GitHub repositories, and engage with the Azure community on Discord and technical forums. The question isn't whether you'll learn something valuable from Microsoft Ignite 2025-that's guaranteed. The question is whether you'll convert that learning into sustained momentum that compounds over months and years into career-defining expertise. The ecosystem is here. The content is ready. Your skilling journey doesn't end when Ignite does—it accelerates.🎙️Public Preview: Azure Logic Apps Connectors as MCP Tools in Microsoft Foundry
At Ignite 2025, Foundry tools were introduced in Microsoft Foundry – a unified catalog of tools, connectors and MCP servers. We are excited to share more about Azure Logic Apps connectors now available as tools in Microsoft Foundry. This unlocks a seamless way for developers to give their agents secure, governed access to the systems they rely on—without writing boilerplate authentication code or managing API plumbing. With this feature, agents can now use any Logic Apps connector—including SAP, ServiceNow, Dynamics, Salesforce, SQL, GitHub, and hundreds more—as a first-class MCP tool inside Foundry. This builds on our recent Public Preview of Logic Apps MCP Servers, which enable both connectors and workflows to be exposed as MCP tools. How it works Step 1 — Select a Logic Apps connector as a tool In the Agent Tools catalog, you’ll now find Logic Apps connectors available alongside existing MCP tools. You can search by name or filter to show only Logic Apps connectors (marked with the Custom tag). After selecting a connector, you’ll move on to choosing a Logic App resource. Step 2 — Create or select a Standard Logic App Logic Apps connectors require a Standard Logic Apps resource as their host. If you don’t already have one, a new Logic App will be created automatically. If you do, you’ll see a dropdown allowing you to pick an existing Logic App. Step 3 — Configure the connector as an MCP server This is where Logic Apps provides its power and flexibility. You effectively generate an MCP server using your connector: Choose which operations you want to expose Optionally configure parameter behavior Specify whether parameters are provided by the model or the user Review or edit the autogenerated tool description based on the connector’s OpenAPI definition Parameters that require user input are clearly indicated in the UI. Step 4 — Register the MCP server as a tool in Foundry This flow begins in the Azure portal and completes in the Foundry portal. After registration completes, your tool appears in the Agent tool list—ready to be added to any agent. In the Connected Resources view, you’ll also see the Logic Apps resource that backs your MCP tool. In just a few steps, agents can now use Logic Apps connectors natively, unlocking secure enterprise connectivity without custom code. Foundry continues to support custom MCP servers, including those created from Logic Apps workflows themselves. If you want to expose a workflow as an MCP tool, you can do so with the same mechanism. (See our detailed document on converting workflows to MCP tools.) Roadmap Unified Experience Today, the flow spans the Azure portal and the Foundry portal. A fully Foundry-native experience is coming in an upcoming release. OAuth-based First-Party Connectors Some first-party connectors using OAuth are not yet supported. These will be enabled in the near future. Next Steps Watch this short demo video to see the feature in action. Get started with the documentation to try it yourself.
