Learn how to host your agents on Microsoft Foundry
We just concluded Host your agents on Foundry, a three-part livestream series where we explored how to deploy and host Python AI agents on Microsoft Foundry: Deploying Python agents to Foundry Hosted agents using the Azure Developer CLI Building hosted agents with Microsoft Agent Framework, including Foundry IQ integration and multi-agent workflows Building hosted agents with LangChain + LangGraph, including built-in tools like Bing Web Search Running quality and safety evaluations: bulk, scheduled, and continuous evals, guardrails, and red-teaming All of the materials from our series are available for you to keep learning from, and linked below: Video recordings of each stream PowerPoint slides that you can use for reviewing or even teaching the material to your own community Open-source code samples you can run yourself in your own Microsoft Foundry project Spanish speaker? Check out the Spanish version of the series. 🙋🏽‍♂️ Have follow up questions? Join the weekly Python+AI office hours on Foundry Discord. Host your agents on Foundry: Microsoft Agent Framework 📺 Watch YouTube recording In our first session, we deploy agents built with Microsoft Agent Framework (the successor of Autogen and Semantic Kernel). Starting with a simple agent, we add Foundry tools like Code Interpreter, ground the agent in enterprise data with Foundry IQ, and finally deploy multi-agent workflows. Along the way, we use the Foundry UI to interact with the hosted agent, testing it out in the playground and observing the traces from the reasoning and tool calls. 🖼️ Slides for this session 💻 Code repository with examples: foundry-hosted-agentframework-demos 📝 Write-up for this session Host your agents on Foundry: LangChain + LangGraph 📺 Watch YouTube recording In our second session, we deploy agents built with the popular open-source libraries LangChain and LangGraph. Starting with a simple agent, we add Foundry tools like Bing Web Search, ground the agent in Foundry IQ, then deploy more complex agents using the LangGraph orchestration framework. Along the way, we use the Foundry UI to interact with the hosted agent, testing it out in the playground and observing the traces from the reasoning and tool calls. 🖼️ Slides for this session 💻 Code repository with examples: foundry-hosted-langchain-demos 📝 Write-up for this session Host your agents on Foundry: Quality & safety evaluations 📺 Watch YouTube recording In our third session, we ensure that our AI agents are producing high-quality outputs and operating safely and responsibly. First we explore what it means for agent outputs to be high quality, using built-in evaluators to check overall task adherence and then building custom evaluators for domain-specific checks. With Foundry hosted agents, we run bulk evaluations on demand, set up scheduled evaluations, and even enable continuous evaluation on a subset of live agent traces. Next we discuss safety systems that can be layered on top of agents and audit agents for potential safety risks. To improve compliance with an organization's goals, we configure custom policies and guardrails that can be shared across agents. Finally, we ensure that adversarial inputs can't produce unsafe outputs by running automated red-teaming scans on agents, and even schedule those to run regularly as well. 🖼️ Slides for this session 💻 Code repository with examples: foundry-hosted-agentframework-demos 📝 Write-up for this sessionCopilot, Microsoft 365 & Power Platform Community call
💡 Microsoft 365 & Power Platform Development bi-weekly community call focuses on different use cases and features within the Microsoft 365 and Power Platform - across Microsoft 365 Copilot, Copilot Studio, SharePoint, Power Apps and more. Demos in this call are presented by the community members. 👏 Looking to catch up on the latest news and updates, including cool community demos, this call is for you! 📅 On 28th of May we'll have following agenda: Latest on SharePoint Framework (SPFx) Latest on Copilot prompt of the week PnPjs CLI for Microsoft 365 Dev Proxy Reusable Controls for SPFx SPFx Toolkit VS Code extension PnP Search Solution Demos this time Mike Fortgens (Ichicraft) – Copilot embedded in SharePoint pages Praveen Kumar R (Quadrasystems.net India) – IntelliLegal: AI-Driven Legal Document Review Demo Nello d’Andrea (die Mobiliar) – Generating page-grounded images in SharePoint with Microsoft Foundry's MAI-Image-2 📅 Download recurrent invite from https://aka.ms/community/m365-powerplat-dev-call-invite 📞 & 📺 Join the Microsoft Teams meeting live at https://aka.ms/community/m365-powerplat-dev-call-join 💡 Building something cool for Microsoft 365 or Power Platform (Copilot, SharePoint, Power Apps, etc)? We are always looking for presenters - Volunteer for a community call demo at https://aka.ms/community/request/demo 👋 See you in the call! 📖 Resources: Previous community call recordings and demos from the Microsoft Community Learning YouTube channel at https://aka.ms/community/youtube Microsoft 365 & Power Platform samples from Microsoft and community - https://aka.ms/community/samples Microsoft 365 & Power Platform community details - https://aka.ms/community/home 🧡 Sharing is caring!Copilot, Microsoft 365 & Power Platform product updates call
💡Microsoft 365 & Power Platform product updates call concentrates on the different use cases and features within the Microsoft 365 and in Power Platform. Call includes topics like Microsoft 365 Copilot, Copilot Studio, Microsoft Teams, Power Platform, Microsoft Graph, Microsoft Viva, Microsoft Search, Microsoft Lists, SharePoint, Power Automate, Power Apps and more. 👏 Weekly Tuesday call is for all community members to see Microsoft PMs, engineering and Cloud Advocates showcasing the art of possible with Microsoft 365 and Power Platform. 📅 On the 26th of May we'll have following agenda: News and updates from Microsoft Together mode group photo Vesa Juvonen & Alex Terentiev – Overriding list and library panes with SPFx Arnav Gupta – Scaling Microsoft Teams Pilots to Your Broader Frontline Organization Paolo Pialorsi – Creating your own secure MCP server 📞 & 📺 Join the Microsoft Teams meeting live at https://aka.ms/community/ms-speakers-call-join 🗓️ Download recurrent invite for this weekly call from https://aka.ms/community/ms-speakers-call-invite 👋 See you in the call! 💡 Building something cool for Microsoft 365 or Power Platform (Copilot, SharePoint, Power Apps, etc)? We are always looking for presenters - Volunteer for a community call demo at https://aka.ms/community/request/demo 📖 Resources: Previous community call recordings and demos from the Microsoft Community Learning YouTube channel at https://aka.ms/community/youtube Microsoft 365 & Power Platform samples from Microsoft and community - https://aka.ms/community/samples Microsoft 365 & Power Platform community details - https://aka.ms/community/home 🧡 Sharing is caring!Building AI Agents with Microsoft Foundry: A Progressive Lab from Hello World to Self-Hosted
AI agent development has a steep on-ramp. The combination of new SDKs, tool-calling patterns, model selection decisions, retrieval-augmented generation, and deployment concerns means most developers spend more time wiring things together than actually building anything useful. The Microsoft Foundry Agent Lab is a structured, open-source demo series designed to change that — nine self-contained demos, each adding exactly one new concept, all built on the same Microsoft Foundry SDK and a single model deployment. This post walks through what the lab contains, how each demo works under the hood, and the architectural decisions that make it a useful reference for AI engineers building production agents. Why a Progressive Lab? Agent frameworks can be overwhelming. A developer who opens a rich example with RAG, tool-calling, streaming, and a custom UI all at once has no clear line of sight to which parts are essential and which are embellishments. The Foundry Agent Lab takes the opposite approach: start with the absolute minimum and introduce one new primitive per demo. By the time you reach Demo 8, you have seen every major capability — not in one monolithic sample, but in a layered sequence where each addition is visible and understandable. # Demo New Concept Tool Used UX 0 hello-demo Agent creation, Responses API, conversations None Terminal 1 tools-demo Function calling, tool-calling loop, live API FunctionTool Terminal 2 desktop-demo UI decoupling — same agent, different surface None Desktop (Tkinter) 3 websearch-demo Server-side built-in tools, no client loop WebSearchTool Terminal 4 code-demo Code execution in sandbox, Gradio web UI CodeInterpreterTool Web (Gradio) 5 rag-demo Document upload, vector stores, RAG grounding FileSearchTool Terminal 6 mcp-demo MCP servers, human-in-the-loop approval MCPTool Terminal 7 toolbox-demo Centralized tool governance, Toolbox versioning Toolbox Terminal 8 hosted-demo Self-hosted agent with Responses protocol Custom server Terminal + Agent Inspector The Model Router: One Deployment to Rule Them All Before diving into the demos, it is worth understanding the one architectural decision that ties the entire lab together: every agent uses model-router as its model deployment. MODEL_DEPLOYMENT=model-router Model Router is a Microsoft Foundry capability that inspects each request at inference time and routes it to the optimal available model — weighing task complexity, cost, and latency. A simple factual question goes to a fast, cheap model. A complex tool-calling chain with code generation gets routed to a frontier model. You write zero routing logic. The lab's MODEL-ROUTER.md file contains empirical observations from running all nine demos. A sample of what the router selected: Demo Query Task Type Model Selected hello "What's the capital of WA state?" Factual recall grok-4-1-fast-reasoning hello "Summarize our conversation" Summarization gpt-5.2-chat-2025-12-11 tools "What's the weather in Seattle?" Tool-using gpt-5.4-mini-2026-03-17 code Data analysis with code generation Code generation + execution gpt-5.4-2026-03-05 rag HR policy document question Retrieval + synthesis gpt-5.3-chat-2026-03-03 This is the strongest signal in the lab: you do not need to reason about model selection. You declare what your agent needs to do; the router handles the rest, and it chooses correctly. Demo 0: The Minimum Viable Agent The hello-demo establishes the baseline pattern used by every subsequent demo. Two files: one to register the agent, one to chat with it. Registering the agent from azure.identity import DefaultAzureCredential from azure.ai.projects import AIProjectClient from azure.ai.projects.models import PromptAgentDefinition credential = DefaultAzureCredential() project = AIProjectClient(endpoint=PROJECT_ENDPOINT, credential=credential) agent = project.agents.create_version( agent_name=AGENT_NAME, definition=PromptAgentDefinition( model=MODEL_DEPLOYMENT, instructions="You are a helpful, friendly assistant.", ), ) Authentication uses DefaultAzureCredential , which works with az login locally and with managed identity in production — no API keys anywhere in the code. Chatting with the agent # Create a server-side conversation (persists history across turns) conversation = openai.conversations.create() # Each turn sends the user message; the agent sees full history response = openai.responses.create( input=user_input, conversation=conversation.id, extra_body={"agent_reference": {"name": AGENT_NAME, "type": "agent_reference"}}, ) print(response.output_text) The conversation object is server-side. You pass its ID on every turn; the history lives in Foundry, not in a local list. This is the Responses API pattern — distinct from the older Completions or Chat Completions APIs. Demo 1: Function Tools and the Tool-Calling Loop Demo 1 adds function calling against a real weather API. The key insight here is that the model does not execute the function — it requests the execution, and your code executes it locally, then feeds the result back. Declaring a function tool from azure.ai.projects.models import FunctionTool, PromptAgentDefinition func_tool = FunctionTool( name="get_weather", description="Get the current weather for a given city.", parameters={ "type": "object", "properties": {"city": {"type": "string", "description": "City name"}}, "required": ["city"], }, strict=True, ) agent = project.agents.create_version( agent_name=AGENT_NAME, definition=PromptAgentDefinition( model=MODEL_DEPLOYMENT, tools=[func_tool], instructions="You are a weather assistant...", ), ) The tool-calling loop response = openai.responses.create(input=user_input, conversation=conversation.id, ...) # Loop while the model is requesting tool calls while any(item.type == "function_call" for item in response.output): input_list = [] for item in response.output: if item.type == "function_call": args = json.loads(item.arguments) result = get_weather(args["city"]) # execute locally input_list.append(FunctionCallOutput(call_id=item.call_id, output=result)) # Send results back to the agent response = openai.responses.create(input=input_list, conversation=conversation.id, ...) print(response.output_text) The strict=True parameter on FunctionTool enforces structured outputs — the model must return arguments that match the declared JSON schema exactly. This eliminates argument parsing errors in production. Demo 2: UI Is Not Your Agent Demo 2 runs the exact same agent as Demo 1 but surfaces it in a Tkinter desktop window. The point is pedagogical: your agent definition, conversation management, and tool-calling logic are entirely independent of your UI layer. Swapping from terminal to desktop requires changing only the presentation code — nothing in the agent or conversation path changes. This is a principle worth internalising early: agent logic and UI logic should never be entangled. The lab enforces this separation structurally. Demo 3: Server-Side Built-In Tools The web search demo introduces a sharp contrast with Demo 1. With WebSearchTool , the tool-calling loop disappears entirely from client code: from azure.ai.projects.models import WebSearchTool agent = project.agents.create_version( agent_name="Search-Agent", definition=PromptAgentDefinition( model=MODEL_DEPLOYMENT, tools=[WebSearchTool()], instructions="You are a research assistant...", ), ) The agent decides when to search, executes the search server-side, and returns a grounded response with citations. Your client code looks identical to Demo 0 — a simple responses.create() call with no tool loop. The distinction matters architecturally: Function tools (Demo 1) — tool execution happens on your client; you control the code, the API call, the error handling. Built-in tools (Demo 3+) — tool execution happens inside Foundry; you get results without managing execution. Demo 4: Code Interpreter and the Gradio Web UI Demo 4 attaches CodeInterpreterTool , which gives the agent a sandboxed Python execution environment inside Foundry. The agent can write code, run it, observe output, and iterate — all server-side. Combined with a Gradio web interface, this demo shows an agent that can perform data analysis, generate charts, and explain results through a browser UI. Model Router is particularly interesting here: the empirical data shows it selects a more capable frontier model ( gpt-5.4-2026-03-05 ) for code-generation tasks, while simpler conversational turns stay on lighter models. Demo 5: Retrieval-Augmented Generation with FileSearchTool Demo 5 introduces RAG. The setup phase uploads a document, creates a vector store, and attaches it to the agent: # Upload document and create a vector store vector_store = openai.vector_stores.create(name="employee-handbook-store") with open("data/employee-handbook.md", "rb") as f: openai.vector_stores.files.upload_and_poll( vector_store_id=vector_store.id, file=f ) # Attach the vector store to the agent agent = project.agents.create_version( agent_name="RAG-Agent", definition=PromptAgentDefinition( model=MODEL_DEPLOYMENT, tools=[FileSearchTool(vector_store_ids=[vector_store.id])], instructions="Answer questions using only the provided documents...", ), ) At query time, the agent embeds the question, searches the vector store semantically, retrieves matching chunks, and generates an answer grounded in the retrieved content — entirely server-side. The client code remains a plain responses.create() call. An important detail: the .vector_store_id file is written to disk during setup and read back during the chat session, so the demo survives process restarts without re-uploading the document. The .gitignore excludes this file from source control. Demo 6: Model Context Protocol Demo 6 connects the agent to a GitHub MCP server, giving it access to repository and issue data via the open Model Context Protocol standard. MCP servers expose tools over a standardised wire protocol; the agent discovers and calls them without any client-side function declarations. The demo also demonstrates human-in-the-loop approval: before executing any MCP tool call, the agent surfaces the proposed action and waits for the user to confirm. This is an important safety pattern for agents that can trigger side effects on external systems. Demo 7: Toolbox — Centralised Tool Governance Where Demo 6 connects to a single MCP server directly, Demo 7 uses a Toolbox — a managed Microsoft Foundry resource that bundles multiple tools into a single, versioned, MCP-compatible endpoint. The Toolbox in this demo exposes both GitHub Issues and GitHub Repos tools, curated into an immutable versioned snapshot. This pattern is significant for production multi-agent systems: Centralised governance — one team owns the tool definitions; all agents consume them via a single endpoint. Versioned snapshots — promoting a new Toolbox version is explicit; agents pin to a version and upgrade intentionally. MCP compatibility — any MCP-capable agent or framework can connect, not just Foundry SDK agents. from azure.ai.projects.models import McpTool toolbox_tool = McpTool( server_label="toolbox", server_url=TOOLBOX_ENDPOINT, allowed_tools=[], # empty = all tools in the Toolbox version headers={"Authorization": f"Bearer {token}"}, ) Demo 8: Self-Hosted Agent with the Responses Protocol The final demo departs from the prompt-agent pattern. Instead of registering a declarative agent in Foundry, Demo 8 implements a custom agent server using the Responses protocol. The server exposes a streaming HTTP endpoint; Foundry's Agent Inspector can connect to it and route user turns to it just as it would to a hosted prompt agent. This demo includes a Dockerfile and an agent.yaml , enabling deployment to Foundry's container hosting service. It uses gpt-4.1-mini directly rather than the model router, because the custom server owns the entire inference path. When to consider this pattern: Your agent requires custom pre- or post-processing logic that cannot be expressed in a system prompt. You need to integrate with infrastructure that is not reachable through MCP or built-in tools. You want to own the inference call for cost control, A/B testing, or compliance reasons. You are building a multi-agent orchestrator that needs to expose itself as an agent to other orchestrators. Getting Started The lab requires Python 3.10 or higher, an Azure subscription with a Microsoft Foundry project, and the Azure CLI. 1. Clone and set up the virtual environment git clone https://github.com/microsoft-foundry/Foundry-Agent-Lab.git cd Foundry-Agent-Lab # Create and activate the virtual environment python -m venv .venv # Windows Command Prompt .venv\Scripts\activate.bat # Windows PowerShell .venv\Scripts\Activate.ps1 # macOS / Linux source .venv/bin/activate pip install -r requirements.txt 2. Configure a demo copy hello-demo\.env.sample hello-demo\.env # Edit hello-demo\.env and set PROJECT_ENDPOINT Your PROJECT_ENDPOINT is on the Overview page of your Foundry project in the Azure portal. It takes the form https://your-resource.ai.azure.com/api/projects/your-project . 3. Run the demo az login 0-hello-demo Each numbered batch file at the root activates the virtual environment, runs create_agent.py , and launches chat.py . Append log to capture the full session transcript: 0-hello-demo log Reset between runs hello-demo\reset.bat Every demo includes a reset.bat that deletes the registered agent and any associated resources (vector stores, uploaded files). Demos are fully repeatable. Architecture Principles Demonstrated Across the nine demos, the lab illustrates a set of design principles that apply directly to production agent systems: Keyless authentication throughout Every demo uses DefaultAzureCredential . No API keys appear anywhere in the code. Locally, az login provides credentials. In production, managed identity takes over automatically — same code, no secrets to rotate. Server-side conversation state The Responses API stores conversation history server-side. Your application passes a conversation ID; Foundry maintains the thread. This eliminates the common bug of truncating history due to local list management and makes multi-process or multi-instance deployments straightforward. Client-side vs server-side tool execution The lab makes the distinction explicit. Function tools execute in your process — you control the code, the external call, and the error handling. Built-in tools (WebSearch, CodeInterpreter, FileSearch) execute inside Foundry — you get results without managing execution infrastructure. MCP tools (Demo 6, 7) fall between these: they execute in a separately deployed server, with the protocol mediating the call. Progressive tool introduction Each demo's create_agent.py registers the agent once. The chat.py file handles the conversation loop. These two responsibilities are always separate, making it easy to update agent definitions without modifying conversation logic, and vice versa. Security Considerations When building agents for production, keep the following in mind: Never commit .env files. The .gitignore excludes them, but verify this before pushing. Use Azure Key Vault or environment variable injection in CI/CD pipelines. Use managed identity in production. DefaultAzureCredential automatically picks up managed identity when deployed to Azure, eliminating the need for any stored credentials. Apply human-in-the-loop for side-effecting tools. Demo 6 demonstrates this pattern for MCP tool calls. Any agent that can modify external state (create issues, send emails, write files) should surface proposed actions for confirmation. Validate tool outputs before use. Treat data returned by external tools (weather APIs, search results, document retrieval) as untrusted input. Prompt injection through tool results is a real attack surface; grounding instructions in your system prompt reduce but do not eliminate this risk. Scope Toolbox permissions narrowly. When using a Toolbox (Demo 7), use allowed_tools to restrict which tools the agent can call, rather than granting access to all tools in a Toolbox version. Key Takeaways Start with the minimum. A prompt agent with no tools requires fewer than 30 lines of code using the Foundry SDK. Add tools only when the use case demands them. Use model-router unless you have a specific reason not to. The empirical data in the lab shows the router selects appropriate models across all task types — factual, creative, tool-calling, RAG, and code generation. Understand the client/server tool boundary. Function tools give you control; built-in tools give you simplicity. MCP and Toolbox give you governance and interoperability. Choose based on where you need control and where you need scale. Conversation state belongs on the server. Do not maintain conversation history in application memory if you can avoid it. The Responses API conversation object is designed for this. The hosted-demo pattern is for when you need to own the inference path. For most use cases, a declarative prompt agent is sufficient and far simpler to operate. Next Steps Explore the repo: github.com/microsoft-foundry/Foundry-Agent-Lab Microsoft Foundry SDK documentation: learn.microsoft.com/azure/ai-studio/ Responses API quickstart: Prompt agent quickstart Model Router conceptual documentation: Model Router for Microsoft Foundry Model Context Protocol: modelcontextprotocol.io Azure Identity SDK (DefaultAzureCredential): azure-identity Python SDK The Foundry Agent Lab is open source under the MIT licence. Contributions, bug reports, and feature requests are welcome through GitHub Issues. See CONTRIBUTING.md for guidelines.Student Devs: Build AI Agents, Compete for $55K in Prizes
Student Devs: Build AI Agents, Compete for $55K in Prizes 🎮 AI Skills Fest • June 4–14, 2026 • Free to Enter $55K Prize Pool 3 Challenge Tracks 10 Days of Hacking Free To Enter Whether you're a first-year CS student or a final-year senior with a portfolio full of projects, Agents League is the best way to gain hands-on experience with agentic AI this summer and walk away with real skills employers are hiring for right now. What You'll Actually Learn Forget passive tutorials. Agents League is project-based learning at full speed. By the end of the hackathon, you'll have built a working AI agent and gained practical experience with the tools shaping the future of software development. 🤖 AI-Assisted Development Use GitHub Copilot to accelerate your coding workflow — from scaffolding to debugging — the way professional developers do today. 🧩 Multi-Step Reasoning Build agents with Microsoft Foundry that can plan, reason, and execute complex tasks — the core of agentic AI. 🏢 Enterprise AI Patterns Learn to build production-ready agents that integrate with Microsoft 365 and Copilot Studio — skills that translate directly to industry jobs. 🔧 Prompt Engineering Design effective prompts and orchestration flows that make AI agents reliable and useful in the real world. 📦 GitHub Workflows Submit your project through GitHub — practising version control, README writing, and open-source collaboration. 🎯 Competitive Problem-Solving Work under real constraints with deadlines, judging criteria, and peer competition — just like industry hackathons and sprints. Pick Your Track (or Try All Three) Agents League has three challenge tracks, each using different Microsoft AI tools. Choose based on your interests or stretch yourself by competing in multiple tracks. Track 01. Creative Apps Build an innovative application with AI-assisted development. This track rewards creativity, dream big and let GitHub Copilot help you bring ideas to life faster than ever. Tool: GitHub Copilot Track 02. Reasoning Agents Create intelligent agents that solve complex problems through multi-step reasoning. Think: agents that can research, plan, and act. This is the cutting edge of AI. Tool: Microsoft Foundry Track 03. Enterprise Agents Build knowledge agents that integrate with Microsoft 365 Copilot. Learn how businesses are deploying AI today and add enterprise AI to your skillset. Tool: Copilot Studio • M365 Opportunities You Won't Want to Miss Agents League isn't just a competition, it's a launchpad. Here's what's in it for you beyond the code: 💰 Win from a $55,000 USD Prize Pool Prizes are awarded across all three tracks smaller teams and solo hackers have a real shot. 📺 Watch Live Coding Battles at Microsoft Reactor See industry experts go head-to-head building AI agents live. Learn advanced techniques you can apply immediately to your own project. 🎓 Free Learning Resources on Microsoft Learn Access curated learning paths and the AI Skills Navigator, structured content designed to get you from zero to submission-ready. 🌍 Join a Global Developer Community Connect with thousands of developers on the Agents League Discord. Find teammates, ask questions, and build your professional network. 📂 Build Your Portfolio with a Real Project Every submission lives on GitHub. Walk away with a polished, public project that demonstrates your AI skills to future employers and grad schools. 🏆 Gain Recognition from Microsoft and the Community Top projects get visibility across the Microsoft developer ecosystem. Stand out from the crowd in internship and job applications. Key Dates to Remember Event Date Hacking Period Opens June 4, 2026 Registration Deadline June 12, 2026 — 12:00 PM PT Submission Deadline June 14, 2026 — 11:59 PM PT How to Get Started (Right Now) You don't have to wait until June 4th to start preparing. Here's your pre-hackathon game plan: Register for the hackathon it's free and open to everyone. Pick a track that matches your interests or curiosity. Explore the learning resources on Microsoft Learn and the AI Skills Navigator. Join the Discord community to find teammates and get early tips. Watch the Reactor event series for live coding battles and expert walkthroughs. Set up your GitHub repo and start experimenting before the hacking window opens. Helpful Links Register for Agents League Free entry, sign up now Microsoft Reactor Events Live coding battles & workshops AI Skills Fest The broader event Microsoft Learn Free learning paths The Arena Awaits 🏆 Ten days. Three tracks. $55K in prizes. Whether you go solo or squad up, this is your chance to build something real with AI and have a blast doing it. Register Now It's Free | Watch Reactor Events Agents League is part of AI Skills Fest and is open to the public at no cost. Review the Hackathon Rules and Regulations and the Microsoft Event Code of Conduct before participating.Agents League: The Esports-Inspired Hackathon Where AI Agents Battle for Glory
Ready to put your AI skills to the ultimate test? Agents League is here, a dynamic, esports-inspired developer challenge that brings the thrill of live competition to the world of agentic AI. Whether you're a seasoned AI developer or just getting started, this is your chance to build, compete, and win. What is Agents League? Agents League is a week-long hackathon running as part of AI Skills Fest (June 4–14, 2026). Unlike traditional hackathons, Agents League combines live AI coding battles, asynchronous project submissions, and a thriving Discord community all competing for a total prize pool of $55,000 USD. This isn't just about building it's about showcasing what's possible with agentic AI in a format that's fast, competitive, and globally accessible. Three Challenge Tracks Pick One or Compete in All 1. Creative Apps Build innovative applications using GitHub Copilot for AI-assisted development. Show off your creativity and demonstrate how AI can accelerate app creation from concept to code. 2. Reasoning Agents Create intelligent agents using Microsoft Foundry that solve complex problems through multi-step reasoning. This track is all about building agents that can think, plan, and execute. 3. Enterprise Agents Build business-ready knowledge agents integrated with Microsoft 365 Copilot, authored in Copilot Studio. Perfect for developers focused on real-world enterprise solutions. Live Microsoft Reactor Events—Don't Miss the Battles! The heart of Agents League beats through live Microsoft Reactor events. Watch experts go head-to-head in live coding battles, learn cutting-edge techniques, and get inspired for your own submissions: Event What You'll Learn Creative Apps Battle See GitHub Copilot in action as experts build innovative apps live Reasoning Agents Battle Watch multi-step reasoning agents come to life with Microsoft Foundry Enterprise Agents Battle Learn to build M365-integrated agents with Copilot Studio 👉 View the full event series Key Dates Registration Deadline: June 12, 2026, 12:00 PM PT Hacking Period: June 4–14, 2026 Submission Deadline: June 14, 2026, 11:59 PM PT What You Get Live coding battles with expert demonstrations Curated technical experiences and on-demand content Learning resources on Microsoft Learn and AI Skills Navigator Community support through Discord GitHub-based submissions for transparent, collaborative judging Why Participate? Agents League isn't just another hackathon. It's designed as a streamlined, competitive format that: ✅ Fits into your schedule with focused, time-boxed challenges ✅ Provides real-world product innovation experience ✅ Offers global accessibility—participate from anywhere ✅ Demonstrates the latest capabilities of agentic AI, including new IQ tools ✅ Connects you with a passionate developer community Ready to Enter the Arena? Register Now for Agents League Before you register: Review the Hackathon Rules and Regulations for prize categories and judging criteria Join the Microsoft Reactor event series for live battles and learning Check out the Microsoft Event Code of Conduct Join the Conversation Have questions? Want to connect with fellow competitors? Join the Agents League community on Discord and start strategizing with developers from around the world. Whether you're building creative apps, reasoning agents, or enterprise solutions—the arena awaits. May the best agent win! 🏆 Agents League hackathon is open to the public and offered at no cost. Government employees should check with their employers to ensure participation is permitted in accordance with applicable policies. Related Links: Agents League Hackathon Registration Microsoft Reactor Series AI Skills FestHow to Visualize Your Azure AI Workloads Usage for Observability
This article assumes you already have an Azure Foundry project and resource deployed in Microsoft Foundry. The options referenced here are documented in detail in the linked articles; this post serves as a consolidated step by step guide bringing them all together and explaining where each option is most useful. A Summary: Need Best Option Quick day-over-day visual, minimal setup Grafana Dashboard (Option 3) Custom growth % calculations App Insights + KQL in Log Analytics (Option 4) Shareable, interactive report Azure Workbooks (Option 5) Per-user/per-agent granularity APIM + App Insights (Option 6) Quick one-off chart, export to Excel Microsoft Foundry Monitor tab or App Insights Metrics Explorer (Option 1 and 2) Option 1. Within the Microsoft Foundry Portal (Quickest, No Setup) If you have models deployed in Microsoft Foundry and would like to monitor its usage, go to the New Foundry Portal → Build → Models → Monitor tab. View metrics such as: Estimated cost Total token usage Input vs. output tokens Number of requests This is the simplest way to monitor both model and agent usage. For PAYG plans: You can also view your total allocated quota (and figure out which Tier you are on) using the Quota Management Screen (New Foundry Portal → Operate → Quota tab). This screen shows how much your total allocated quota is, per model in a given subscription + region + Deployment Type (Global, Data Zones or Regional). For eg., in the image below, for gpt-4o, I am allocated 7M total TPM in my subscription. I am only using 150K TPM of the allocated 7M TPM amount. Which means, my requests will get throttled if I exceed the 150K TPM limit. To avoid throttling, I would need to increase my shared allocation limit. NOTE: you are charged for usage, so if you allow more capacity, you use more, so you pay more. Option 2: Azure Monitor Metrics Explorer This is already built into the Azure Portal and gives you time-series charts out of the box. Go to Azure Portal → your Azure OpenAI / Foundry resource → Monitoring → Metrics Select a metric like AzureOpenAIRequests or TokenTransaction Set Aggregation to Sum (total) or Max and Time granularity to 1 day Split by ModelDeploymentName to see per-model trends Adjust the time range (e.g., last 30 days) — you'll see day-over-day bars/lines Tip: You can pin these charts to an Azure Dashboard for a persistent view, or click Share → Download to Excel to get the raw data for your own analysis. Option 3: Azure Managed Grafana (Best Pre-Built Dashboard) This is the best option for a polished, real-time, day-over-day dashboard with no custom code. There's a pre-built AI Foundry dashboard ready to import. [grafana.com], [Create a M...ed Grafana] How to set it up: Create an Azure Managed Grafana workspace (if you don't have one) In Grafana, go to Dashboards → New → Import → enter dashboard ID 24039 (for Foundry) Select your Azure Monitor data source and point it to your Foundry resource Tip: You can also import this directly from the Azure Portal: Monitor → Dashboards with Grafana → AI Foundry. That's it — the dashboard gives you (per model deployment): Token trends over time (inference, prompt, completion — day over day) Request trends over time (AzureOpenAIRequests as a time series) Latency trends (bonus) NOTE: Default time range is 7 days — adjust to 30/60/90 days for growth trends Option 4: Application Insights + KQL Queries (Most Flexible, Custom Reports) If you want fully custom day-over-day growth calculations (e.g., % change day-to-day), this is the way. [azurefeeds.com] Setup: Ensure your Foundry project is connected to an Application Insights resource (Foundry → Settings → Connected Resources). Open up App Insights resource → Logs → New Query or choose a sample query. In the images below, we simply ran 'requests' and set the time range to 24 hours. There is also a Kusto Query Language (KQL) mode or Simple mode on the right-hand side: Simple mode will let you run out of the box samples. KQL mode will open up a query window for you to enter custom queries. Below are the results in grid view. Same view but showing a chart: Export options: Another way to get the above graphs are via Log Analytics. Simply enable Diagnostic Settings on your Azure OpenAI resource → send to a Log Analytics workspace. Open Log Analytics → Logs and try our your sample queries. Sample KQL for day-over-day token usage (adjust to your needs): AzureMetrics | where MetricName in ("TokenTransaction", "ProcessedPromptTokens", "GeneratedTokens") | where TimeGenerated > ago(30d) | summarize DailyTokens = sum(Total) by bin(TimeGenerated, 1d), MetricName | order by TimeGenerated asc | render timechart Result: Sample KQL for day-over-day growth % (adjust to your needs): AzureMetrics | where MetricName == "TokenTransaction" | where TimeGenerated > ago(30d) | summarize DailyTokens = sum(Total) by Day = bin(TimeGenerated, 1d) | sort by Day asc | extend PrevDay = prev(DailyTokens) | extend GrowthPct = round((DailyTokens - PrevDay) / PrevDay * 100, 2) | project Day, DailyTokens, GrowthPct Option 5: Azure Monitor Workbooks (Custom Dashboards, Shareable) Workbooks let you build interactive, parameterized dashboards that combine metrics and KQL logs. What's more, you can select resources from multiple subscriptions and visualize them all in one place using Workbooks! Go to Azure Portal → Monitor → Workbooks → New Add a Metrics query panel → select your Log Analytics or App Insights or Foundry resource -> Enter the same query you used in Option 4. Do a test run and view the graphs (this can be viewed as charts or a list (grid view)): 4. Save and share with your team. Option 6: APIM + Application Insights (Granular Per-Caller/Per-Agent Tracking) 1. If your app routes requests through Azure API Management, you can use the azure-openai-emit-token-metric policy to send per-request token metrics to Application Insights with custom dimensions (User ID, Subscription ID, Agent, etc.). [Azure API...osoft Docs] This is ideal for scenarios like: "Which agent consumed the most tokens last week?" "What's the token usage per API consumer/team?" NOTE: Microsoft Foundry resources do not track usage by users. So, fronting your Foundry resource with an APIM could be a way to track users provided you pass the username/id in the request context. How you implement this is upto your app design. Ref: AI-Gateway/labs/token-metrics-emitting/token-metrics-emitting.ipynb at main · Azure-Samples/AI-Gateway · GitHub Bonus: Check out all other APIM + AI related policies here: AI-Gateway/labs/semantic-caching at main · Azure-Samples/AI-Gateway AI-Gateway/labs/token-rate-limiting at main · Azure-Samples/AI-Gateway AI-Gateway/labs/token-metrics-emitting/token-metrics-emitting.ipynb at main · Azure-Samples/AI-Gateway · GitHubThree Tiers, One Platform: Building Agents Together with the Build-Along Series
The Agentic Opportunity Every partner has agent ambition, but a single workshop format cannot serve every builder. Business makers want a fast win without writing code. Citizen developers want to orchestrate real processes across enterprise systems. Pro developers want a code-first surface with evaluations, tracing, and managed identity. Run the wrong session for the wrong audience, and engagement collapses - too abstract for one group, too constrained for another. The Agent Build-Along Series solves that by meeting builders exactly where they are, across three tiers of the Microsoft platform stack. Behind the series sits a self-serve GitHub repository - a curated library of build-along sessions with easy-to-follow, step-by-step instructions, scoped to specific industry scenarios and business functions. Partners pick the scenario that fits the room, clone the assets, and run the session. No bespoke content build, no facilitator guesswork. A Self-Serve Repository for Build-Along Sessions Diagram illustrating three tiers of agent building The repo at Agent Build-Along Github is a self-serve catalog of ready-to-run workshops. Every session is curated for a specific industry and business function, with easy-to-follow instructions a facilitator can pick up and deliver. Pick the combination that matches the room and run the session - the content, scenario, and assets are already there. Sessions are organised across three axes so partners can find the right fit fast: Industry - Financial Services, Healthcare, Retail, Manufacturing, Public Sector, Energy, Professional Services Business function - Sales, Marketing, Finance, HR, Operations, IT, Customer Service Workload - Agent Builder, Copilot Studio and Azure AI Foundry Each session in the repo includes a real-world scenario, step-by-step build instructions, sample data, prompts and configuration, and a facilitator narrative. Clone, brief the room, build the agent - that is the loop. Three Tiers - Meet Builders Where They Are The pyramid is intentional. Builders start where their skills are today and move up as confidence grows. The tier defines platform, audience, duration, and approach - content stays consistent. Tier Platform Audience Duration Approach 1 - Foundation Microsoft 365 Copilot Agent Builder Business makers, end users, departmental teams 60 min No-code 2 - Extend Copilot Studio Citizen developers, power users, ops teams 90 min Low-code 3 - Pro-Code Azure AI Foundry Pro developers, architects, AI engineers 180+ min Code-first Tier 1 - M365 Copilot Agent Builder Author your first agent - no code required. Microsoft 365 Copilot Agent Builder virtual workshop overview, What your customers will build A custom agent scoped to a real workplace scenario Grounded with knowledge sources (SharePoint sites, files, web URLs) Configured with custom instructions and conversation starters Tested in the preview pane and shared with a team Session flow Define → Ground → Refine → Test & Share Prerequisites Microsoft 365 Copilot licence (or Copilot Chat free) Modern browser (Edge / Chrome) A workplace scenario in mind, or use our template Outcome Attendees leave with a working agent they can use immediately, plus a repeatable pattern for future use cases. This is the right entry point for departmental teams who want to operationalise Copilot without engineering involvement. Tier 2 - Copilot Studio Orchestrate multi-step agents across enterprise systems. Microsoft Copilot Studio virtual workshop overview What your customers will build A copilot with generative answers and authored topics Custom actions that call APIs and Power Platform connectors Knowledge sources spanning SharePoint, Dataverse, public web, and enterprise data Channel deployment to Microsoft Teams and a public web embed Session flow Design → Connect → Author → Publish Prerequisites M365 Copilot license with access to Copilot Studio Familiarity with Power Platform helpful, not required A multi-step workflow or process in mind Outcome Attendees leave with a published copilot running in Teams or on the web, plus a pattern for connecting copilots to enterprise systems. This is where citizen developers and ops teams turn manual processes into orchestrated, agent-driven workflows. Tier 3 - Azure AI Foundry Ship a code-first agent with evaluations and observability. What your customers will build A code-first agent with custom tools and function calling Grounding via Azure AI Search and the customer's own data Model selection from the Foundry catalog, with optional fine-tuning Evaluations, tracing, and managed-identity deployment Session flow Provision → Build → Evaluate → Deploy Prerequisites Azure subscription with Azure AI Foundry access VS Code, plus Python or .NET familiarity Sample data or a use case ready to ground on Outcome Attendees leave with a deployed agent - code, evaluations, and tracing in place - plus reusable SDK templates. This is the right tier for architects and AI engineers shipping agents into production with the governance bar enterprise customers expect. How the Repository Works The repo removes the heaviest lift in running these workshops: content creation. Every session is curated against the same three axes - industry, business function, tier - and shipped with step-by-step instructions a facilitator can follow with minimum prep. Industry/Business Function × Tier → Ready-to-run Build-Along Each session in the repo ships with: A real-world scenario grounded in the chosen industry and business function Step-by-step build instructions tuned to the selected tier Sample data the agent can ground against Prompts, conversation starters, and configuration snippets Facilitator narrative so anyone can run the session Need an enterprise agent that can handle integrate with CRM data? There is a curated Copilot Studio session with a deal-desk scenario and sample CRM data. Need a pro-code health care agent? Choose the Foundry session with a patient-flow scenario and a grounded dataset. Same repo, different curated paths - at the right depth for the room. What Attendees Walk Away With Agent-Builder - A working agent in Microsoft 365 Copilot, ready to share with colleagues. Copilot Studio - A published copilot running in Teams or on the web, connected to enterprise systems. Foundry - A deployed Foundry agent with evaluations, tracing, and SDK templates for the next build. In all cases attendees leave with assets - not slides - and the confidence to build the next one without facilitation. Next Steps - Host a Build-Along Pick your tier - Start where your skills are today. Move up the pyramid as confidence grows. Drive Registration – Socialise your Build-Along session to maximise attendance. Show up and build. Bring a real scenario. Customers leave with a working agent to share. Where to Next Browse the self-serve repository of Build-Along sessions - curated by industry and business function, with step-by-step instructions ready to run: Agent Build Along.
