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 FestWe Gave Ourselves 20 Minutes to Build an AI Agent for a Lumber Company. The Timer's Still on Screen.
Here's a confession: most "build with AI" webinars are 60 minutes of slides, 5 minutes of a polished demo someone rehearsed for a week, and a closing CTA. You leave inspired but not really sure what you saw. So we tried something different. We put a visible countdown timer on the screen and gave ourselves 20 minutes to do two things, live: Build an AI agent that solves a real business problem Deploy a working AI application to Azure No edits to hide the awkward parts. No "and here's one I prepared earlier." Just the timer, the screen, and a working app at the end. The on-demand recording is up now. Here's what's in it and why you should carve out 20 minutes for it this week. The setup: why lumber? 🏘️ We needed a real business problem, not a toy one. So for the demo, we role-play as the owner of Contoso Lumber — a regional lumber business with a very specific, very real headache: Should we sell our inventory now, or hold it longer? Sell too early, miss a better price. Hold too long, eat storage costs. Lumber prices fluctuate with global competition, macro shifts, even the weather. In the past, decisions like this came from morning meetings and gut instinct, or maybe the occasional ad-hoc spreadsheet that nobody could reuse a month later. It's the kind of decision that should have an analyst behind it — except most growing businesses can't afford to hire one full-time. So we build the AI agent that does. (Yes, lumber. We know. Stick with us — the boring industry is exactly the point. If it works here, it works for your business too.) What we actually build (in 20 minutes flat) The webinar walks through the entire flow, end to end: Part 1 — The agent. We open Microsoft Foundry at ai.azure.com, browse the model leaderboard (there are over 11,000 models to choose from — we compare a few on the cost-vs-quality chart), pick one, write a plain-English instruction for the agent, upload a CSV of historical lumber pricing, and ask it a real question: "If I cannot sell one of my products today unless I offer my clients a 35% discount, and knowing the historical pricing data, should I still sell it?" The agent runs a break-even analysis and comes back with a reasoned recommendation — hold for 3–6 months, here's the math on why, here's where storage costs start eating the upside. Then we add voice mode (now you can ask the agent for pricing recs from a coffee shop on your phone), and lock down guardrails to block jailbreaks, prompt injection, data leakage, and — because we're feeling fancy — profanity in responses. Part 2 — The app. With the agent done, we pivot to deploying a full AI chat application to Azure. From scratch. Using exactly five commands in Azure Cloud Shell: azd auth login git clone <repo> cd <folder> azd up azd down # (this one's for when you're done — kills everything to avoid surprise bills) That's it. The template handles the Container Apps setup, the architecture-aligned-to-Well-Architected-Framework stuff, all the boilerplate that usually eats half a sprint. By the end of the segment, there's a working AI chatbot running on a real Azure URL. We even pause the timer when we're just explaining things, so you know the 20-minute clock is honest about build time, not talk time. Why this format is more useful than another slide deck A few things this webinar shows that a written tutorial can't: The Foundry UI is super navigable. You watch someone do it. You see where the buttons are. You see what the leaderboard looks like when you're comparing GPT-5.3 Codex against Kimi K2.5 on a cost-to-quality chart. (Spoiler: Kimi wins this particular trio. Your mileage will vary depending on your workload.) The "no-stitching" claim is real. Models, data, agents, guardrails, deployment — all in one place. You don't need to leave Foundry to wire seven products together. The webinar makes that concrete by showing you the actual flow without cutting. Five commands really is five commands. This is the part people are most skeptical about until they see it. azd up does the work. The infrastructure provisioning, the container app, the AI service hookup — all of it. You can delete it just as fast. azd down tears everything back down. Useful when you're experimenting and don't want a $40 surprise on your Azure bill next month. What's on screen at the end By the 20-minute mark: A published AI agent named for the lumber business, with guardrails, voice mode enabled, ready to be called from Teams, Microsoft 365 Copilot, or any application via endpoint A separate AI chat application deployed to Azure Container Apps, with a live URL Logs, observability, the full Foundry control plane — all available out of the box And in the closing minutes, four very concrete next steps for what you do next if this sparked an idea for your own business — including Azure Accelerate (if you want Microsoft experts in the room with you), the partner network, and the Microsoft marketplace if you'd rather buy than build. Watch the recording The on-demand recording is available now. Block 20 minutes — that's literally all it takes — and ideally watch with your Azure portal open in another tab so you can follow along. If you're the kind of person who learns by doing, pause at the agent-building section and try it yourself in parallel. Foundry is free to explore; the agent we build in the webinar costs cents to run. → Watch the on-demand webinar A few things we'd love feedback on If you watch it, we'd genuinely love to know: Did the timer help or distract? (We thought it would feel gimmicky. It turned out to be the most-mentioned thing in early feedback.) What use case from your business would you want to see in the next one? We're picking the next demo problem from comments. Was the lumber thing weirdly compelling or were you just here for the Azure parts? Drop a comment, tag us, or grab a partner and try building your own version this week. The timer's reset. Your 20 minutes start whenever you press play. Want to go deeper than the webinar? Two companion reads: From Idea to Impact: How Growing Businesses Scale with Azure (five real customer stories with the full architectures) and AI Made Simple: 3 Practical Moves for Growing Businesses (the structured playbook for figuring out what to build first).What’s New in Microsoft 365 Copilot | May 2026
Welcome to the May 2026 edition of What's New in Microsoft 365 Copilot! Every month, we highlight new features and enhancements to keep Microsoft 365 admins up to date with Copilot features that help your users be more productive and efficient in the apps they use every day.
From insight to action: how Adobe and Microsoft are helping marketers move faster with AI
Today’s marketing leaders are under pressure to do more than ever—deliver meaningful personalization, accelerate execution, and prove measurable business impact. At the same time, teams are navigating increasing complexity: fragmented data, disconnected tools, and insights that arrive too late to act on. AI can change this—but only when it’s embedded directly into how people already work. That’s why Microsoft and Adobe are deepening our partnership: bringing customer experience intelligence, AI-powered workflows, and enterprise-grade AI directly into Microsoft 365 Copilot—so teams can move from insight to alignment to execution in one continuous workflow. The result is faster decisions, more coordinated execution, and clearer business outcomes—without breaking flow or context. Bringing customer experience intelligence into the flow of work Marketing teams don’t struggle because they lack data. They struggle because insights live in one place, collaboration in another, and execution somewhere else entirely. That disconnect slows teams down and creates unnecessary friction between analysis and action. Together, Adobe and Microsoft are changing that dynamic by connecting Adobe’s customer experience capabilities with Microsoft 365 Copilot and Copilot Cowork—so insight, collaboration, and next-best action can happen where work already happens: in Copilot Chat and in everyday apps like Teams, Word, and PowerPoint. Marketers can ask questions, explore insights, align with teammates, and take action without jumping between tools—turning intelligence into impact at the moment it matters. Adobe Marketing Agent for Microsoft 365 Copilot: now generally available A major milestone in this journey is the general availability of the Adobe Marketing Agent for Microsoft 365 Copilot, now available via Microsoft Commercial Marketplace. The Adobe Marketing Agent brings Adobe customer experience intelligence directly into Copilot, enabling marketing teams to: Accelerate time from insight to decision Move seamlessly from analysis to execution Keep humans firmly in control, with AI supporting—not replacing—decision‑making Importantly, the agent is enterprise-ready by design. IT administrators can deploy and manage the experience through the Microsoft 365 admin center, ensuring security, governance, and compliance at scale. Expanding executive experiences with Copilot Cowork Looking ahead, Adobe skills designed for customer experience orchestration will be accessible in Copilot Cowork—in a future release. This upcoming experience will enable customer experience leaders to engage with customer experience insights in a more direct, conversational way, bringing strategic visibility into the same Copilot environments where decisions are made and actions are coordinated. Built on Azure to scale securely and responsibly The technology foundation of this innovation is Azure. Adobe Experience Platform, Adobe Experience Platform Agent Orchestrator, and Adobe AI Agents are built on Azure and leverage Azure AI models, providing the scalability, security, and reliability enterprises require. By running on Azure, these agentic experiences benefit from Microsoft’s global infrastructure, enterprise‑grade security, and responsible AI commitments—supporting customer trust as organizations scale AI across their business. Designed for interoperability across agent ecosystems Modern enterprises don’t operate in a single ecosystem—and their agents shouldn’t either. Adobe agents are built to interoperate with agents created using Microsoft Azure AI Foundry or Copilot Studio, enabling customers to orchestrate richer, cross‑functional workflows across marketing, sales, service, and operations. This architecture is designed to enable organizations to compose agentic solutions that reflect how work actually happens—across systems, teams, and business processes. Moving from experimentation to execution This partnership reflects a broader shift in how organizations adopt AI—moving from experimentation to embedded, enterprise‑ready execution. By bringing the full power of Adobe Experience Platform together with Microsoft’s AI platform, cloud infrastructure, and Copilot experiences, we’re helping teams move faster with clarity, confidence, and control. This is how AI becomes not just powerful—but practical. Learn more Adobe + Microsoft partnership page Adobe Marketing Agent for Microsoft Copilot pagePitch Maker Agent: Turn Copilot Chat Signals into Microsoft 365 Copilot Deals
Executive Summary Customers are already using free Copilot Chat at scale, but adoption is often ungoverned and disconnected from the Microsoft 365 workloads where measurable productivity and risk controls live. The Pitch Maker Agent (BETA) helps partners convert Partner Center Copilot growth opportunity signals into customer-ready narratives—reducing pitch preparation from days to minutes and improving consistency across stakeholders (replace with your measured baseline). What it enables (partner outcomes) Turn raw usage signals into an executive business case with clear opportunity, risk, and next steps. Standardize value conversations across IT and business buyers while keeping customer context specific. Accelerate conversion from exploration to governed deployment by anchoring on Microsoft 365 workloads. Why it’s different Evidence-led: uses Partner Center Copilot growth opportunities (ASPX) signals rather than generic prompts. Buyer-ready: outputs a structured narrative (not a feature list) designed for executive alignment and action. Inputs required Partner Center Copilot growth opportunities export (all columns) for the target customer. The Opportunity: From AI Exploration to Enterprise Direction The move from free Copilot Chat to Microsoft 365 Copilot is a timing advantage: customers have intent and familiarity, but need a governed path that ties AI to real work in Teams, Outlook, Excel, and beyond. Advisory gap: translate usage metrics into business insight executives can fund. Governance gap: balance opportunity with security, compliance, and lifecycle controls. Workflow gap: connect AI usage to measurable outcomes inside Microsoft 365 workloads. How the Agent Works (BETA) The Agent follows a simple, repeatable flow to generate an executive-ready pitch narrative from Partner Center Copilot growth opportunity signals. See the agent in action below. In three steps Upload the Partner Center Copilot growth opportunities export (all columns). Run the Agent to translate usage signals into a customer-specific executive narrative. Use the generated business case, recommendations, and next steps in the customer conversation. What the Output Enables Translate Partner Center signals into a fundable business case, faster. Improve executive alignment by presenting opportunity, risk, and plan in one narrative. Increase repeatability across accounts with a consistent structure and messaging. The figure below illustrates how the Agent turns usage signals into a concise, executive-ready pitch narrative and action plan. Figure 1. From Copilot Chat signals to an executive pitch narrative and next-step plan. For customers, the conversation shifts from features to outcomes—clear productivity impact, role-based change, and risk-aware governance. Deployment and Execution The Agent is delivered as a solution package and deployed through Copilot Studio with a straightforward publish-and-run flow. Prepare Partner Center ASPX export (all columns) and validate sensitivity labels. Import the solution package into Copilot Studio. Verify dependencies, publish the Agent, and enable access in Microsoft 365 Copilot and Teams. Run the guided pitch flow by uploading customer data and capturing the narrative output. The run guide provides step-by-step visuals for data preparation, import, publication, and how to use the output in customer conversations. Why This Matters for Partner Practices The Pitch Maker Agent (BETA) supports a repeatable value motion: identify opportunity, align stakeholders, and move customers from experimentation to governed Microsoft 365 Copilot adoption. Higher conversion: clearer executive rationale anchored in evidence and outcomes. Lower effort: less time drafting, more time on discovery and delivery. Better governance: built-in prompts to address risk, readiness, and controls early. Call to Action This week: 15-minute start Locate the solution package and run guide in the Agent folder. Deploy the Agent in Copilot Studio and publish to Microsoft 365 Copilot/Teams. Export Partner Center Copilot growth opportunities data and validate sensitivity labels. Upload the dataset and generate a customer-specific executive pitch narrative. Resources Helpful links to learn more and access supporting materials: Partner Center Copilot growth opportunities data GitHub repository Overview: Run guide
Building Agentic Systems on Azure: Microsoft Foundry Agents SDK vs Microsoft Agent Framework
In my recent experience as a Senior Consultant at Microsoft, I’ve been actively involved in designing and delivering AI-driven solutions, with a strong focus on building intelligent agents using modern frameworks. Along the way, I've built agents using both Microsoft Foundry Agents SDK (hereafter "Agents SDK") and Microsoft Agent Framework (MAF) Both approaches are powerful and capable. However, once you move beyond simple proofs of concept, the developer experience and architectural patterns start to differ significantly. This article provides a practical comparison based on real implementation experience and aims to help developers choose the right approach. Approach 1: Agents SDK Agents SDK provides a straightforward way to create agents with integrated tools and models. Example: Creating an Agent from azure.ai.projects import AIProjectClient from azure.ai.agents.models import AzureAISearchTool, AzureAISearchQueryType from azure.identity import DefaultAzureCredential client = AIProjectClient(credential=DefaultAzureCredential(), endpoint=os.getenv("AZURE_AI_PROJECT_ENDPOINT")) # Configure tools ai_search = AzureAISearchTool( index_connection_id=conn_id, index_name="my-index", query_type=AzureAISearchQueryType.SEMANTIC, ) # Create agent (persisted in Foundry portal) agent = client.agents.create_agent( model=os.getenv("AZURE_AI_AGENT_DEPLOYMENT_NAME"), name="MyAgent", instructions="You are a helpful assistant.", tool_resources=ai_search.resources, tools=ai_search.definitions, ) # Run conversation thread = client.agents.threads.create() client.agents.messages.create(thread_id=thread.id, role="user", content="Hello") run = client.agents.runs.create(thread_id=thread.id, agent_id=agent.id) What this approach provides Native integration with Azure AI services (OpenAI, AI Search, MCP) Managed execution environment Simple and quick agent setup Conceptually, this approach can be summarized as: Model + Tools + Execution Strengths ✅ Rapid development and onboarding ✅ Strong integration within the Azure ecosystem ✅ Well-suited for single-agent or tool-driven use cases ✅ Minimal infrastructure overhead Challenges observed in practice As the complexity of scenarios increases, certain limitations become more visible: Multi-agent workflows require custom orchestration logic Agent handoffs must be implemented manually Context sharing across agents requires additional design effort While this approach offers flexibility, it shifts orchestration complexity to the developer. Approach 2: Microsoft Agent Framework (MAF) Microsoft Agent Framework introduces a higher-level abstraction, focused on agent orchestration and system design. Creating an Agent from agent_framework import Agent, WorkflowBuilder, Message from agent_framework.foundry import FoundryChatClient from azure.identity import DefaultAzureCredential client = FoundryChatClient( project_endpoint=os.getenv("FOUNDRY_PROJECT_ENDPOINT"), model=os.getenv("FOUNDRY_MODEL_DEPLOYMENT_NAME"), credential=DefaultAzureCredential(), ) # Create agents (in-process only, not persisted in portal) researcher = Agent(client, name="ResearcherAgent", instructions="Research topics thoroughly.") writer = Agent(client, name="WriterAgent", instructions="Write concise summaries.") # Build and run multi-agent workflow workflow = WorkflowBuilder(start_executor=researcher).add_edge(researcher, writer).build() async for event in workflow.run(Message("user", "Summarize migration best practices"), stream=True): print(event.content) What this approach provides Built-in orchestration capabilities Native support for multi-agent workflows Structured agent lifecycle management Context and memory handling Conceptually, this can be viewed as: Agents + Orchestration + System Design Observations from implementation When implementing similar use cases using MAF: Agent responsibilities became clearly defined Routing and delegation patterns were significantly simplified Overall system architecture became easier to maintain and scale This approach encourages thinking in terms of agent ecosystems rather than isolated agents. Architecture Comparison Agents SDK Microsoft Agent Framework (MAF) Choosing the Right Approach Use Agents SDK when: You need rapid development for a single-agent use case The workflow is relatively straightforward You prefer flexibility and lower-level control Use Microsoft Agent Framework when: You are designing multi-agent systems Your solution requires routing, delegation, or handoffs Long-term scalability and maintainability are essential Pros and Cons Summary Agents SDK Pros Easy to get started Strong Azure integration Flexible design Cons Manual orchestration required Limited native multi-agent support Complexity increases as scenarios grow Microsoft Agent Framework (MAF) Pros Built-in orchestration Native multi-agent support Scalable and structured architecture Cons Learning curve for new developers More opinionated framework design Reduced low-level control compared to SDK-based approach References and Repositories 🔗 Microsoft Agent Framework (MAF) Microsoft Agent Framework – GitHub Repository Microsoft Agent Framework Samples – Tutorials & Examples Workflow Samples (Multi-agent patterns) FoundryChatClient sample (Python) Agent Framework demos - GitHub Source 📘 Documentation Microsoft Agent Framework Overview (Microsoft Learn) Agent Framework + Microsoft Foundry provider docs 🔗 Azure AI Projects / Agents SDK Azure AI Projects SDK – Python (GitHub Source) Azure AI Projects Agents (.NET SDK repo) 📘 Documentation Azure AI Projects SDK (Python) – Microsoft Learn Azure AI Agents SDK – Microsoft Learn Conclusion Azure AI Projects and Microsoft Agent Framework both play important roles in the modern agent development landscape. Agents SDK enables quick and flexible agent development Microsoft Agent Framework enables structured, scalable agent systems In practice, the choice depends on whether you are building a single agent feature or a multi-agent system. Final Thought Agents SDK helps you get started quickly. Microsoft Agent Framework helps you scale with confidence In a follow-up blog, I’ll dive into how the M365 Agents SDK compares with Microsoft Agent Framework, especially in the context of enterprise productivity and Copilot experiences.Copilot, Microsoft 365 & Power Platform Community call
💡 Copilot, 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 11th of June 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) – Personalized SharePoint pages with configurable widgets Vipul Jain (Bosch Global Software Technologies) – Creating Smart Export to PDF in SharePoint Online using SPFx João Mendes (Kuehne & Nagel) & Hugo Bernier – Creating a custom events web part with React and SharePoint Framework (SPFx) 📅 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!
