Generative AI for Beginners - Full Videos Series Released!
With so many new technologies, tools and terms in the world of Generative AI, it can be hard to know where to start or what to learn next. "Generative AI for Beginners" is designed to help you on your learning journey no matter where you are now. We are happy announce that the "Generative AI for Beginners" course has received a major refresh - 18 new videos for each lesson.Essential Microsoft Resources for MVPs & the Tech Community from the AI Tour
Unlock the power of Microsoft AI with redeliverable technical presentations, hands-on workshops, and open-source curriculum from the Microsoft AI Tour! Whether you’re a Microsoft MVP, Developer, or IT Professional, these expertly crafted resources empower you to teach, train, and lead AI adoption in your community. Explore top breakout sessions covering GitHub Copilot, Azure AI, Generative AI, and security best practices—designed to simplify AI integration and accelerate digital transformation. Dive into interactive workshops that provide real-world applications of AI technologies. Take it a step further with Microsoft’s Open-Source AI Curriculum, offering beginner-friendly courses on AI, Machine Learning, Data Science, Cybersecurity, and GitHub Copilot—perfect for upskilling teams and fostering innovation. Don’t just learn—lead. Access these resources, host impactful training sessions, and drive AI adoption in your organization. Start sharing today! Explore now: Microsoft AI Tour Resources.Responsible AI Resources for Developers
In the rapidly evolving world of technology, AI stands at the forefront of innovation. However, with great power comes great responsibility. As developers, we play a pivotal role in shaping the future of AI, ensuring it aligns with ethical standards and societal values. Microsoft is committed to guiding developers on this journey with resources and tools designed to develop responsible AI.Building your own copilot – yes, but how? (Part 1 of 2)
Today, there’s a wide range of built-in services and features designed to enable organizations and developers to build their own copilots, able to answer questions based on their own knowledge bases and data sources. But how to choose the most suitable one for each scenario? This blog post wants to provide an overview of some of the main choices you have in the Microsoft technology ecosystem. Part 1 will look into low-code tools and out-of-the-box features, while part 2 will focus on code-heavy and extensible options.Agents League: Meet the Winners
Agents League brought together developers from around the world to build AI agents using Microsoft's developer tools. With 100+ submissions across three tracks, choosing winners was genuinely difficult. Today, we're proud to announce the category champions. 🎨 Creative Apps Winner: CodeSonify View project CodeSonify turns source code into music. As a genuinely thoughtful system, its functions become ascending melodies, loops create rhythmic patterns, conditionals trigger chord changes, and bugs produce dissonant sounds. It supports 7 programming languages and 5 musical styles, with each language mapped to its own key signature and code complexity directly driving the tempo. What makes CodeSonify stand out is the depth of execution. CodeSonify team delivered three integrated experiences: a web app with real-time visualization and one-click MIDI export, an MCP server exposing 5 tools inside GitHub Copilot in VS Code Agent Mode, and a diff sonification engine that lets you hear a code review. A clean refactor sounds harmonious. A messy one sounds chaotic. The team even built the MIDI generator from scratch in pure TypeScript with zero external dependencies. Built entirely with GitHub Copilot assistance, this is one of those projects that makes you think about code differently. 🧠 Reasoning Agents Winner: CertPrep Multi-Agent System View project CertPrep Multi-Agent System team built a production-grade 8-agent system for personalized Microsoft certification exam preparation, supporting 9 exam families including AI-102, AZ-204, AZ-305, and more. Each agent has a distinct responsibility: profiling the learner, generating a week-by-week study schedule, curating learning paths, tracking readiness, running mock assessments, and issuing a GO / CONDITIONAL GO / NOT YET booking recommendation. The engineering behind the scene here is impressive. A 3-tier LLM fallback chain ensures the system runs reliably even without Azure credentials, with the full pipeline completing in under 1 second in mock mode. A 17-rule guardrail pipeline validates every agent boundary. Study time allocation uses the Largest Remainder algorithm to guarantee no domain is silently zeroed out. 342 automated tests back it all up. This is what thoughtful multi-agent architecture looks like in practice. 💼 Enterprise Agents Winner: Whatever AI Assistant (WAIA) View project WAIA is a production-ready multi-agent system for Microsoft 365 Copilot Chat and Microsoft Teams. A workflow agent routes queries to specialized HR, IT, or Fallback agents, transparently to the user, handling both RAG-pattern Q&A and action automation — including IT ticket submission via a SharePoint list. Technically, it's a showcase of what serious enterprise agent development looks like: a custom MCP server secured with OAuth Identity Passthrough, streaming responses via the OpenAI Responses API, Adaptive Cards for human-in-the-loop approval flows, a debug mode accessible directly from Teams or Copilot, and full OpenTelemetry integration visible in the Foundry portal. Franck also shipped end-to-end automated Bicep deployment so the solution can land in any Azure environment. It's polished, thoroughly documented, and built to be replicated. Thank you To every developer who submitted and shipped projects during Agents League: thank you 💜 Your creativity and innovation brought Agents League to life! 👉 Browse all submissions on GitHubBuilding your own copilot – yes, but how? (Part 2 of 2)
With the term copilot we refer to a virtual assistant solution hosted in the Cloud, using an LLM as a chat engine, which is fed with business data and custom prompts and eventually integrated with 3 rd party services and plugins. In the first blog of this series , we covered how to build a copilot on custom data using low code tools and Azure out-of-the-box features. In this blog post we’ll focus on a code-first experience.
Building Knowledge-Grounded AI Agents with Foundry IQ
Foundry IQ now integrates with Foundry Agent Service via MCP (Model Context Protocol), enabling developers to build AI agents grounded in enterprise knowledge. This integration combines Foundry IQ’s intelligent retrieval capabilities with Foundry Agent Service’s orchestration, enabling agents to retrieve and reason over enterprise data. Key capabilities include: Auto-chunking of documents Vector embedding generation Permission-aware retrieval Semantic reranking Citation-backed responses Together, these capabilities allow AI agents to retrieve enterprise knowledge and generate responses that are accurate, traceable, and aligned with organizational permissions. Why Use Foundry IQ with Foundry Agent Service? Intelligent Retrieval Foundry IQ extends beyond traditional vector search by introducing: LLM-powered query decomposition Parallel retrieval across multiple sources Semantic reranking of results This enables agents to retrieve the most relevant enterprise knowledge even for complex queries. Permission-Aware Retrieval Agents only access content users are authorized to see. Access control lists from sources such as: SharePoint OneLake Azure Blob Storage are automatically synchronized and enforced at query time. Auto-Managed Indexing Foundry IQ automatically manages: Document chunking Vector embedding generation Indexing This eliminates the need to manually build and maintain complex ingestion pipelines. The Three Pillars of Foundry IQ 1. Knowledge Sources Foundry IQ connects to enterprise data wherever it lives — SharePoint, Azure Blob Storage, OneLake, and more. When you add a knowledge source: Auto-chunking — Documents are automatically split into optimal segments Auto-embedding — Vector embeddings are generated without manual pipelines Auto-ACL sync — Access permissions are synchronized from supported sources (SharePoint, OneLake) Auto-Purview integration — Sensitivity labels are respected from supported sources2. Knowledge Bases 2. Knowledge Bases A Knowledge Base unifies multiple sources into a single queryable index. Multiple agents can share the same knowledge base, ensuring consistent answers across your organization 3. Agentic Retrieval Agentic retrieval is an LLM-assisted retrieval pipeline that: Decomposes complex questions into subqueries Executes searches in parallel across sources Applies semantic reranking Returns a unified response with citations Agent → MCP Tool Call → Knowledge Base → Grounded Response with Citations The retrievalReasoningEffort parameter controls LLM processing: minimal — Fast queries low — Balanced reasoning medium — Complex multi-part questions Project Architecture ┌─────────────────────────────────────────────────────────────────────┐ │ FOUNDRY AGENT SERVICE │ │ ┌─────────────┐ ┌─────────────┐ ┌─────────────────────────┐ │ │ │ Agent │───▶│ MCP Tool │───▶│ Project Connection │ │ │ │ (gpt-4.1) │ │ (knowledge_ │ │ (RemoteTool + MI Auth) │ │ │ └─────────────┘ │ base_retrieve) └─────────────────────────┘ │ └─────────────────────────────│───────────────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────────────────┐ │ FOUNDRY IQ (Azure AI Search) │ │ ┌─────────────────────────────────────────────────────────────┐ │ │ │ MCP Endpoint: │ │ │ │ /knowledgebases/{kb-name}/mcp?api-version=2025-11-01-preview│ │ │ └─────────────────────────────────────────────────────────────┘ │ │ │ │ │ ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────────┐ │ │ │ Knowledge │ │ Knowledge │ │ Indexed Documents │ │ │ │ Sources │──│ Base │──│ (auto-chunked, │ │ │ │ (Blob, SP, etc) │ │ (unified index) │ │ auto-embedded) │ │ │ └─────────────────┘ └─────────────────┘ └─────────────────────┘ │ └─────────────────────────────────────────────────────────────────────┘ Prerequisites Enable RBAC on Azure AI Search az search service update --name your-search --resource-group your-rg \ --auth-options aadOrApiKey Assign Role to Project's Managed Identity az role assignment create --assignee $PROJECT_MI \ --role "Search Index Data Reader" \ --scope "/subscriptions/.../Microsoft.Search/searchServices/{search}" Install Dependencies pip install azure-ai-projects>=2.0.0b4 azure-identity python-dotenv requests Connecting a Knowledge Base to an Agent The integration requires three steps. Connect Knowledge Base to Agent via MCP The integration requires three steps: Create a project connection — Links your AI Foundry project to the knowledge base using ProjectManagedIdentity authentication Create an agent with MCPTool — The agent uses knowledge_base_retrieve to query the knowledge base Chat with the agent — Use the OpenAI client to have grounded conversations Step 1: Create Project Connection import requests from azure.identity import DefaultAzureCredential, get_bearer_token_provider credential = DefaultAzureCredential() PROJECT_RESOURCE_ID = "/subscriptions/.../providers/Microsoft.CognitiveServices/accounts/.../projects/..." MCP_ENDPOINT = "https://{search}.search.windows.net/knowledgebases/{kb}/mcp?api-version=2025-11-01-preview" def create_project_connection(): """Create MCP connection to knowledge base.""" bearer = get_bearer_token_provider(credential, "https://management.azure.com/.default") response = requests.put( f"https://management.azure.com{PROJECT_RESOURCE_ID}/connections/kb-connection?api-version=2025-10-01-preview", headers={"Authorization": f"Bearer {bearer()}"}, json={ "name": "kb-connection", "properties": { "authType": "ProjectManagedIdentity", "category": "RemoteTool", "target": MCP_ENDPOINT, "isSharedToAll": True, "audience": "https://search.azure.com/", "metadata": {"ApiType": "Azure"} } } ) response.raise_for_status() Step 2: Create Agent with MCP Tool from azure.ai.projects import AIProjectClient from azure.ai.projects.models import PromptAgentDefinition, MCPTool def create_agent(): client = AIProjectClient(endpoint=PROJECT_ENDPOINT, credential=credential) # MCP tool connects agent to knowledge base mcp_kb_tool = MCPTool( server_label="knowledge-base", server_url=MCP_ENDPOINT, require_approval="never", allowed_tools=["knowledge_base_retrieve"], project_connection_id="kb-connection" ) # Create agent with knowledge base tool agent = client.agents.create_version( agent_name="enterprise-assistant", definition=PromptAgentDefinition( model="gpt-4.1", instructions="""You MUST use the knowledge_base_retrieve tool for every question. Include citations from sources.""", tools=[mcp_kb_tool] ) ) return agent, client Step 3: Chat with the Agent def chat(agent, client): openai_client = client.get_openai_client() conversation = openai_client.conversations.create() while True: question = input("You: ").strip() if question.lower() == "quit": break response = openai_client.responses.create( conversation=conversation.id, input=question, extra_body={ "agent_reference": { "name": agent.name, "type": "agent_reference" } } ) print(f"Assistant: {response.output_text}") More Information Azure AI Search Knowledge Stores Foundry Agent Service Model Context Protocol (MCP) Azure AI Projects SDK Summary The integration of Foundry IQ with Foundry Agent Service enables developers to build knowledge-grounded AI agents for enterprise scenarios. By combining: MCP-based tool calling Permission-aware retrieval Automatic document processing Semantic reranking organizations can build secure, enterprise-ready AI agents that deliver accurate, traceable responses backed by source data.Integrating Microsoft Foundry with OpenClaw: Step by Step Model Configuration
Step 1: Deploying Models on Microsoft Foundry Let us kick things off in the Azure portal. To get our OpenClaw agent thinking like a genius, we need to deploy our models in Microsoft Foundry. For this guide, we are going to focus on deploying gpt-5.2-codex on Microsoft Foundry with OpenClaw. Navigate to your AI Hub, head over to the model catalog, choose the model you wish to use with OpenClaw and hit deploy. Once your deployment is successful, head to the endpoints section. Important: Grab your Endpoint URL and your API Keys right now and save them in a secure note. We will need these exact values to connect OpenClaw in a few minutes. Step 2: Installing and Initializing OpenClaw Next up, we need to get OpenClaw running on your machine. Open up your terminal and run the official installation script: curl -fsSL https://openclaw.ai/install.sh | bash The wizard will walk you through a few prompts. Here is exactly how to answer them to link up with our Azure setup: First Page (Model Selection): Choose "Skip for now". Second Page (Provider): Select azure-openai-responses. Model Selection: Select gpt-5.2-codex , For now only the models listed (hosted on Microsoft Foundry) in the picture below are available to be used with OpenClaw. Follow the rest of the standard prompts to finish the initial setup. Step 3: Editing the OpenClaw Configuration File Now for the fun part. We need to manually configure OpenClaw to talk to Microsoft Foundry. Open your configuration file located at ~/.openclaw/openclaw.json in your favorite text editor. Replace the contents of the models and agents sections with the following code block: { "models": { "providers": { "azure-openai-responses": { "baseUrl": "https://<YOUR_RESOURCE_NAME>.openai.azure.com/openai/v1", "apiKey": "<YOUR_AZURE_OPENAI_API_KEY>", "api": "openai-responses", "authHeader": false, "headers": { "api-key": "<YOUR_AZURE_OPENAI_API_KEY>" }, "models": [ { "id": "gpt-5.2-codex", "name": "GPT-5.2-Codex (Azure)", "reasoning": true, "input": ["text", "image"], "cost": { "input": 0, "output": 0, "cacheRead": 0, "cacheWrite": 0 }, "contextWindow": 400000, "maxTokens": 16384, "compat": { "supportsStore": false } }, { "id": "gpt-5.2", "name": "GPT-5.2 (Azure)", "reasoning": false, "input": ["text", "image"], "cost": { "input": 0, "output": 0, "cacheRead": 0, "cacheWrite": 0 }, "contextWindow": 272000, "maxTokens": 16384, "compat": { "supportsStore": false } } ] } } }, "agents": { "defaults": { "model": { "primary": "azure-openai-responses/gpt-5.2-codex" }, "models": { "azure-openai-responses/gpt-5.2-codex": {} }, "workspace": "/home/<USERNAME>/.openclaw/workspace", "compaction": { "mode": "safeguard" }, "maxConcurrent": 4, "subagents": { "maxConcurrent": 8 } } } } You will notice a few placeholders in that JSON. Here is exactly what you need to swap out: Placeholder Variable What It Is Where to Find It <YOUR_RESOURCE_NAME> The unique name of your Azure OpenAI resource. Found in your Azure Portal under the Azure OpenAI resource overview. <YOUR_AZURE_OPENAI_API_KEY> The secret key required to authenticate your requests. Found in Microsoft Foundry under your project endpoints or Azure Portal keys section. <USERNAME> Your local computer's user profile name. Open your terminal and type whoami to find this. Step 4: Restart the Gateway After saving the configuration file, you must restart the OpenClaw gateway for the new Foundry settings to take effect. Run this simple command: openclaw gateway restart Configuration Notes & Deep Dive If you are curious about why we configured the JSON that way, here is a quick breakdown of the technical details. Authentication Differences Azure OpenAI uses the api-key HTTP header for authentication. This is entirely different from the standard OpenAI Authorization: Bearer header. Our configuration file addresses this in two ways: Setting "authHeader": false completely disables the default Bearer header. Adding "headers": { "api-key": "<key>" } forces OpenClaw to send the API key via Azure's native header format. Important Note: Your API key must appear in both the apiKey field AND the headers.api-key field within the JSON for this to work correctly. The Base URL Azure OpenAI's v1-compatible endpoint follows this specific format: https://<your_resource_name>.openai.azure.com/openai/v1 The beautiful thing about this v1 endpoint is that it is largely compatible with the standard OpenAI API and does not require you to manually pass an api-version query parameter. Model Compatibility Settings "compat": { "supportsStore": false } disables the store parameter since Azure OpenAI does not currently support it. "reasoning": true enables the thinking mode for GPT-5.2-Codex. This supports low, medium, high, and xhigh levels. "reasoning": false is set for GPT-5.2 because it is a standard, non-reasoning model. Model Specifications & Cost Tracking If you want OpenClaw to accurately track your token usage costs, you can update the cost fields from 0 to the current Azure pricing. Here are the specs and costs for the models we just deployed: Model Specifications Model Context Window Max Output Tokens Image Input Reasoning gpt-5.2-codex 400,000 tokens 16,384 tokens Yes Yes gpt-5.2 272,000 tokens 16,384 tokens Yes No Current Cost (Adjust in JSON) Model Input (per 1M tokens) Output (per 1M tokens) Cached Input (per 1M tokens) gpt-5.2-codex $1.75 $14.00 $0.175 gpt-5.2 $2.00 $8.00 $0.50 Conclusion: And there you have it! You have successfully bridged the gap between the enterprise-grade infrastructure of Microsoft Foundry and the local autonomy of OpenClaw. By following these steps, you are not just running a chatbot; you are running a sophisticated agent capable of reasoning, coding, and executing tasks with the full power of GPT-5.2-codex behind it. The combination of Azure's reliability and OpenClaw's flexibility opens up a world of possibilities. Whether you are building an automated devops assistant, a research agent, or just exploring the bleeding edge of AI, you now have a robust foundation to build upon. Now it is time to let your agent loose on some real tasks. Go forth, experiment with different system prompts, and see what you can build. If you run into any interesting edge cases or come up with a unique configuration, let me know in the comments below. Happy coding!
