Embracing Responsible AI: A Comprehensive Guide and Call to Action
In an age where artificial intelligence (AI) is becoming increasingly integrated into our daily lives, the need for responsible AI practices has never been more critical. From healthcare to finance, AI systems influence decisions affecting millions of people. As developers, organizations, and users, we are responsible for ensuring that these technologies are designed, deployed, and evaluated ethically. This blog will delve into the principles of responsible AI, the importance of assessing generative AI applications, and provide a call to action to engage with the Microsoft Learn Module on responsible AI evaluations. What is Responsible AI? Responsible AI encompasses a set of principles and practices aimed at ensuring that AI technologies are developed and used in ways that are ethical, fair, and accountable. Here are the core principles that define responsible AI: Fairness AI systems must be designed to avoid bias and discrimination. This means ensuring that the data used to train these systems is representative and that the algorithms do not favor one group over another. Fairness is crucial in applications like hiring, lending, and law enforcement, where biased AI can lead to significant societal harm. Transparency Transparency involves making AI systems understandable to users and stakeholders. This includes providing clear explanations of how AI models make decisions and what data they use. Transparency builds trust and allows users to challenge or question AI decisions when necessary. Accountability Developers and organizations must be held accountable for the outcomes of their AI systems. This includes establishing clear lines of responsibility for AI decisions and ensuring that there are mechanisms in place to address any negative consequences that arise from AI use. Privacy AI systems often rely on vast amounts of data, raising concerns about user privacy. Responsible AI practices involve implementing robust data protection measures, ensuring compliance with regulations like GDPR, and being transparent about how user data is collected, stored, and used. The Importance of Evaluating Generative AI Applications Generative AI, which includes technologies that can create text, images, music, and more, presents unique challenges and opportunities. Evaluating these applications is essential for several reasons: Quality Assessment Evaluating the output quality of generative AI applications is crucial to ensure that they meet user expectations and ethical standards. Poor-quality outputs can lead to misinformation, misrepresentation, and a loss of trust in AI technologies. Custom Evaluators Learning to create and use custom evaluators allows developers to tailor assessments to specific applications and contexts. This flexibility is vital in ensuring that the evaluation process aligns with the intended use of the AI system. Synthetic Datasets Generative AI can be used to create synthetic datasets, which can help in training AI models while addressing privacy concerns and data scarcity. Evaluating these synthetic datasets is essential to ensure they are representative and do not introduce bias. Call to Action: Engage with the Microsoft Learn Module To deepen your understanding of responsible AI and enhance your skills in evaluating generative AI applications, I encourage you to explore the Microsoft Learn Module available at this link. What You Will Learn: Concepts and Methodologies: The module covers essential frameworks for evaluating generative AI, including best practices and methodologies that can be applied across various domains. Hands-On Exercises: Engage in practical, code-first exercises that simulate real-world scenarios. These exercises will help you apply the concepts learned tangibly, reinforcing your understanding. Prerequisites: An Azure subscription (you can create one for free). Basic familiarity with Azure and Python programming. Tools like Docker and Visual Studio Code for local development. Why This Matters By participating in this module, you are not just enhancing your skills; you are contributing to a broader movement towards responsible AI. As AI technologies continue to evolve, the demand for professionals who understand and prioritize ethical considerations will only grow. Your engagement in this learning journey can help shape the future of AI, ensuring it serves humanity positively and equitably. Conclusion As we navigate the complexities of AI technology, we must prioritize responsible AI practices. By engaging with educational resources like the Microsoft Learn Module on responsible AI evaluations, we can equip ourselves with the knowledge and skills necessary to create AI systems that are not only innovative but also ethical and responsible. Join the movement towards responsible AI today! Take the first step by exploring the Microsoft Learn Module and become an advocate for ethical AI practices in your community and beyond. Together, we can ensure that AI serves as a force for good in our society. References Evaluate generative AI applications https://learn.microsoft.com/en-us/training/paths/evaluate-generative-ai-apps/?wt.mc_id=studentamb_263805 Azure Subscription for Students https://azure.microsoft.com/en-us/free/students/?wt.mc_id=studentamb_263805 Visual Studio Code https://code.visualstudio.com/?wt.mc_id=studentamb_263805Student 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.Hands-on webinar: Study and Learn agent in M365 Copilot
Join us on Wednesday, May 13th @ 8am Pacific Time for an in-depth professional development webinar on the new Study & Learn agent in Microsoft 365 Copilot, which is about to become available for all students 13+ and educators. This will be a 60-minute hands-on webinar where the Product Management team will walk through the Study & Learn agent, which is purpose-built for learning. The experience goes beyond just answering questions and instead guiding students through concepts with step-by-step support, interactive activities like quizzes and flashcards, and conversations grounded in learning science. The Study & Learn agent will be globally available by the time of this webinar on May 13th, 2026. We will also be providing links for professional development credit at this session. And don’t worry – we’ll be recording these and posting on our Microsoft Education YouTube channel so you’ll always to able to watch later or share with others. What we will cover ✅ Introduce the new Study & Learn agent ✅Understand concept/question, FC and Quiz, Matching and FIB ✅IT admin motions for enabling Copilot Chat ✅Learning Activities for students hands-on ✅CPNBs and Study Guide slides and demo 📅 Date: Tuesday, May 13th ⏰ Time: 8:00 AM Pacific 🔗 Register: https://msit.events.teams.microsoft.com/event/msit.954c5c3b-cbc0-458d-9739-49e3e8b4baf7@72f988bf-86f1-41af-91ab-2d7cd011db47Accelerating the Flow of Learning
By Vince Frankson, Authentica Solutions | Guest Post via the Microsoft Education Blog Education technology leaders are not short on vision; they are short on time. They know exactly what their Microsoft 365 environment is capable of. What they deserve are better tools, smarter automation, and a partner that shows up ready to serve. At Authentica Solutions, we pride ourselves on our culture of servant leadership, not just as a slogan, but as our guiding principle. Our mission is to support and serve district technology teams, ensuring that technology enhances their valuable work rather than making it more complex. Everything we build is organized around one purpose: accelerating the flow of learning. Not technology for its own sake... tools and services focused at getting teachers back to teaching, students back to learning, and district leaders back to the decisions that matter most. With the school year winding down and Back to School planning already on the horizon, there is no better moment to look at where your Microsoft 365 environment stands, and how the right tools and the right team can compress your timeline, free your people, and make this the smoothest Back to School launch yet. District Technology Teams Deserve Better Tools K-12 technology leaders oversee complex systems, sometimes across multiple campuses, and support a wide range of users. While Microsoft 365 offers advanced features, many remain underused due to the time and expertise required for full implementation. Microsoft’s School Data Sync (SDS) is a great example. It is a genuinely powerful free service that automates the flow of student roster and identity data from your Student Information System (SIS) into Microsoft Entra ID, provisioning Teams classrooms, Intune device groups, SharePoint sites, and OneNote Class Notebooks automatically, at scale. The platform is exceptional. Fully configuring it, keeping it healthy across an academic year, and extending its value into the rest of the data ecosystem is specialized work that deserves specialized support. Authentica addresses this gap by providing support so school IT teams can focus on high-impact tasks, helping educators and students achieve more. Authentica seed™: Built to Accelerate the Flow of Learning Authentica seed™ is Authentica’s Education Intelligence Cloud Service, designed to streamline learning by enabling clean, automated, bidirectional data pipelines. This allows teachers to access rosters quickly, students to begin learning immediately, and administrators to monitor instruction in real time, accelerating progress for everyone. Getting Your Data into Microsoft 365 seed™ sits between your SIS and SDS, handling data preparation, mapping, and delivery so your team can focus on higher-value work. Districts that previously invested significant time in validation cycles and configuration troubleshooting are reaching full value in a fraction of the time. Automated SIS-to-SDS delivery with built-in validation, so errors are caught before they ever reach your tenant. Support for OneRoster API and CSV formats across all major SIS platforms, meeting your district where it already is. Automatic provisioning of users, classes, and groups across Microsoft Entra ID, Teams, SharePoint, Exchange, and Intune for Education. Manage Student Age Groups to limit or support access to Microsoft Copilot for students 13+. Every hour returned from focusing on the data pipeline is an hour returned to learning. That is Cloud with Purpose. Getting Your Data Back Where It Belongs Learning does not stop at the edge of Microsoft 365, and neither should your data. seed™ closes the loop by moving grade data, assessment data, and engagement signals back to the platforms that need them, automatically. Back to your SIS: Keeping your system of record current and authoritative without double grade entry. To your Analytics platform: Giving administrators and instructional coaches visibility into what is working, event making Microsoft 365 activity visible, without waiting for manual exports. Building your dream data estate: What if you could combine your academic and instructional data, your operations and staff data, and your financial data into a single place? What if you could have it prepped and ready so you can ask questions with Copilot and / or AI Agents. Which schools have the most students off track right now? Top 5, and why. Where are we spending the most with the weakest results? Top 3. One connected data ecosystem. Compounding the return on every platform investment your district has made and accelerating the flow of learning throughout. UsageIQ™ Microsoft 365 Edition: Giving Leaders the Visibility They Deserve District technology leaders make consequential decisions about licensing, training, and support every year. They deserve data that makes those decisions clear. Authentica UsageIQ™ Microsoft 365 Edition was built to provide exactly that: a straightforward, actionable picture of how your Microsoft 365 environment is being used across the organization. Not a consultant engagement. Not a manual pull from the admin portal. A purpose-built, single view of your tenant that tells you what you need to know, when you need to know it. App-by-app usage across your tenant: Teams, SharePoint, OneDrive, Exchange, OneNote, and more, in one place. School-by-school and role-based breakdowns: See where adoption is strong and where targeted support would unlock real value. License utilization insights: Ensure every seat is earning its value and renewal conversations are grounded in actual usage data. Back to School readiness views: Identify accounts and configurations that need attention before day one, not after. When technology leaders can see clearly, they lead confidently. That visibility is not a luxury. It is what great tools are supposed to deliver. Managed Services for Microsoft 365: A Team That Has Been Here Before At Authentica, we deliver Managed Services with a servant leadership mindset. We're not just a helpdesk, our team has years of experience at Microsoft, having built education solutions like SDS, Teams for Education, and Graph APIs. We support your district as an expert extension to handle critical Microsoft 365 tasks efficiently, allowing your technology staff to focus on their key responsibilities. What Managed Services for Microsoft 365 Covers End-of-Year close support for SDS expiration management, tenant cleanup, graduating account archiving, and identity hygiene, handled on time and on spec so your team can close the year cleanly. Back to School launch for new year SDS configuration, classroom provisioning, device group refresh, and Conditional Access review, so teachers walk into a working environment on day one. Ongoing SDS health monitoring and proactive troubleshooting, so issues are resolved before they become incidents Microsoft Entra ID and identity management support across the full user lifecycle. Teams for Education configuration, governance, and adoption enablement. Intune for Education device policy and deployment support. Copilot readiness assessment and enablement, so your district is positioned for AI-powered learning when you are ready to move. This is what it looks like when your Microsoft 365 environment has a team behind it that is fully committed to your success, not just at launch, but every day. End of School Checklist: Five Things Worth Doing Before Summer Break These are the actions that make Back to School smoother, faster, and less reactive. Your team knows this; this is a reminder of what is worth prioritizing before the calendar turns. Audit Microsoft Entra ID for inactive and graduating accounts. Clean identities now mean a cleaner, more secure tenant heading into the new year, and fewer licensing surprises at renewal. Review your SDS sync health. Check error and warning logs now. Small data issues that are easy to address in the spring become August emergencies. Get ahead of them while there is room to breathe. Check your Microsoft 365 license utilization. If you are on A3 or A5, are those features actively working for your district? UsageIQ™ Microsoft 365 Edition can show you the full picture and give you a clear story heading into renewal season. Archive Teams classes from the current year. Establish your retention and archival approach before the new year roster drop. A clean tenant makes everyone faster, IT, teachers, and students. Plan your Back-to-School timeline now. Microsoft School Data Sync (SDS) provisioning for a large district takes time. Build in buffer, engage your support resources before August, and set your team up to launch the year confidently instead of reactively. We Are Here to Serve Authentica Solutions is offering a complimentary Microsoft 365 Readiness Assessment for districts that want a clear picture of where they stand before the new year begins. This is a straightforward conversation with our team, people who have been inside these systems for years, helping you identify what is working, what is ready to unlock, and where the right tools can return the most value. No pressure. Just expertise, in service of your district and the learning that happens inside it. Let’s talk to schedule your complimentary Microsoft 365 Readiness Assessment. About Authentica Solutions Authentica Solutions is an EdTech company grounded in servant leadership and built around one purpose: accelerating the flow of learning. Our team, including former Microsoft engineers who built core Microsoft 365 education products, serves K-12 districts through seed™, UsageIQ™, reachAI™, and Managed Services for Microsoft 365. Cloud with Purpose. Visit www.authenticasolutions.comHands-on Session: From idea to interactive lesson with Microsoft Learning Zone
Join us on Tuesday, May 12th at 8:00 AM Pacific for a hands-on professional development session introducing Learning Zone - a new app that helps you create interactive, classroom-ready lessons in minutes. In this 45-minute webinar, the Product Management team will guide you through core capabilities and the latest updates. You can follow along using your own Microsoft 365 Education account. Also, you will be able to get Professional Development credit with this session and we will offer a Credly badge at the end. What we will cover: ✅ Getting started with Learning Zone: Access Learning Zone and get set up ✅ Experience as a student: Join a session and see how it works from the student perspective ✅ Building your first interactive lesson: Create your first interactive lesson (in minutes!) ✅ Assigning to your class: Send lessons via link, short code, Teams Assignments, or your LMS ✅ Exploring the ready-to-learn library: Bring immediate value to your students through a variety of lessons by trusted of partners. Important note: Lesson generation is currently available only on Copilot+ PCs with any Microsoft 365 Education license (supported in English and Spanish). No Copilot+ PC? No problem. You’ll still get to try out the student experience, learn how to use the lesson library, assign interactive lessons, review insights, and integrate Learning Zone into your existing workflows. 📅 Date: Tuesday, May 12th ⏰ Time: 8:00 AM Pacific Register: https://aka.ms/LZwebinarMay26 We look forward to having you attend the event!More standards are coming to the Teach Module and Teams for Education!
Hi everyone! As educators, you have told us that aligning lessons, assessments, and classroom materials to the standards you actually use is one of the most important parts of making AI-powered teaching tools useful in practice. When standards are available and easy to apply, it becomes much faster to create materials that fit your local curriculum and instructional goals. That is why we are continuing to expand the standards experience across Microsoft Education. We are excited to share a new wave of international standards coming to the standards experience in Teach and in Teams for Education. These standards will support experiences in the Teach module across Lesson Plans, Quizzes, and Rubrics, and they are also coming to Assignments in Teams for Education. If you don't see your country listed, you can request more standards at this link. In this post, we will share what is coming this week, what is planned over the next two months, what we are targeting for summer, and how we plan to keep you updated going forward. Why this matters Standards alignment helps you spend less time translating curriculum requirements into classroom materials and more time supporting student learning. Whether you are building a lesson plan, generating a quiz, creating a rubric, or preparing future assignments workflows in Teams for Education, access to the right standards makes those experiences more relevant and easier to use. Our goal is to keep expanding coverage so more educators can work with the standards they already know and trust, in more countries, subjects, and grade bands. New standards added in 2025 In 2025, we expanded standards coverage with a new set of international additions, including: Austria Canada - Ontario Early Language Learners Health & PE Technology Education Art Canada - Quebec Francophone Canada - Ontario World Languages French as a Second Language Native Languages American Sign Language as a Second Language Classical Studies and International Languages Egypt England Arts Education Health & PE World Languages Technology Education Career Technical Education Finland Kuwait UK GCE AS and A Level Qualifications across a broad range of subject areas These additions helped expand standards coverage beyond core national frameworks and into more subject-specific and qualification-based experiences. Recently added We have already started rolling out new international standards this spring. Recent additions include: Czech Republic UK additions, including recent support for Scotland, Wales, and UK GCE AS and A Level qualifications New Brunswick - Technology Standards Kuwait - Language Arts, Math, Social Studies Estonia - Language Arts, Math, Science Estonia - Social Studies Latvia New Brunswick - Language Arts, Math, Science, Social Studies These recent additions laid the groundwork for the next wave of standards now arriving across Teach and Teams for Education. Coming this week This week, we are adding the following standards to the standards experience in Teach and Teams for Education: Finland Lithuania Norway Romania These additions continue our recent rollout of international standards and expand access for educators who want to align AI-assisted lesson creation and assessment workflows to local curriculum expectations. Coming in the next two months Over the next two months, we expect to continue expanding standards coverage with the following additions: Slovakia Sweden Egypt Canada - Quebec Francophone Standards India NCERT - Hindi Language Arts India NCERT - Sanskrit Language Arts Bahrain Lebanon Oman Qatar Greece We also have additional standards in progress that are on the roadmap, with timing still being finalized: Austria Kuwait - Science India NCERT - Urdu Language Arts Australia ACARA National Technology Education Health & PE Art Languages Canada - New Brunswick additional subject expansion Health & PE Art Languages Norway vocational standards As these become available, they will light up the same standards-backed experiences across Teach and Teams for Education. Planned for summer Looking ahead, we are planning an even broader set of standards expansions over the summer. This work is designed to add more international coverage across core subjects and additional curriculum frameworks. The following are planned for summer: Belgium - Flemish Catholic Network Standards (VVKSO) Language Arts, Math, Science, Social Studies Canada, British Columbia ADST (Entrepreneurship & Marketing) Career Education (Career-Life Education, Career-Life Connections) Core Competencies Canada: Ontario Business Studies Canadian & World Studies Co-op Ed Guidance & Career Ed Ontario Catholic expectations (ICE) CEFR (Common European Framework of Reference for Languages) Language Arts Portugal Language Arts, Math, Science, Social Studies Germany: NRW State Kernlehrplan Language Arts, Math, Science, Social Studies Hong Kong Language Arts, Math, Science, Social Studies Turkey Language Arts, Math, Science, Social Studies Vietnam Language Arts, Math, Science, Social Studies Costa Rica Language Arts, Math, Science, Social Studies Peru Language Arts, Math, Science, Social Studies Guatemala Language Arts, Math, Science, Social Studies Morocco Language Arts, Math, Science, Social Studies Croatia Language Arts, Math, Science, Social Studies Kenya Language Arts, Math, Science, Social Studies Bolivia Language Arts, Math, Science, Social Studies Chile Language Arts, Math, Science, Social Studies Pakistan Language Arts, Math, Science, Social Studies Panama Language Arts, Math, Science, Social Studies This planned summer wave reflects our continued focus on expanding standards coverage in ways that are useful for real classroom workflows across regions. Where you will see these standards As standards coverage expands, educators will see the impact across several experiences: Teach module - Lesson Plans Teach module - Quizzes Teach module and Teams for Education – Rubrics Teams for Education – Assignments Instructions (Coming soon) This means more opportunities to use standards as part of lesson creation, assessment design, and classroom workflows without having to start from scratch. What this means for educators As more standards become available, you will be able to: Align lesson materials to more local and regional curriculum requirements Build quizzes and rubrics that better reflect what students are expected to know and do Use standards-backed workflows in Teach across more countries and subject areas Prepare for future standards-aligned experiences in Assignments in Teams for Education For educators working across multiple countries, languages, or curriculum systems, this expanded coverage can help reduce manual work and make AI-generated outputs more relevant to your teaching context. We plan to keep sharing updates We also plan to share regular blog updates roughly every quarter so you can see what standards are newly available, what is rolling out next, and where we are continuing to expand coverage. Our goal is to make these updates easier to track so educators, school leaders, and partners can stay current on what is available in the standards experience across Microsoft Education. Helpful links Getting started with Teach Modify content - Align to Standards Microsoft Teams for Education International standards currently available through EdGate Request additional standards Share feedback with us by joining our EDU Insider Program Have questions or want to let us know which standards you would like to see next? Drop a comment below or submit a request through our Standards Feedback form. We would love to hear what curriculum frameworks matter most in your classrooms. Until next time, Samantha Fisher · Microsoft EducationWhat's New in Microsoft EDU - March 2026
Join us on Wednesday, March 25th, 2026 for our latest "What's New in Microsoft EDU" webinar! We will be covering all of the latest product updates from Microsoft Education. These 30-minute webinars are put on by the Microsoft Education Product Management group and happen once per month, this month both 8:00am Pacific Time and 4:00pm Pacific time to cover as many global time zones as possible around the world. And don’t worry – we’ll be recording these and posting on our Microsoft Education YouTube channel in the new “What’s New in Microsoft EDU” playlist, so you’ll always to able to watch later or share with others! Here is our March 2026 webinar agenda: 1) M365 Copilot and AI updates for Educators and Students - Modify Existing Content - Minecraft EDU Lesson Plans - New Learning Activities: Fill in the Blanks, Matching and Self-Quizzing - Study & Learn agent for studnets 2) Learning Zone General Availability and the Copilot+ PC 3) Microsoft 365 LTI and Teach Module for Learning Management Systems 4) AMA - Ask Microsoft EDU Anything (Q&A) We look forward to having you attend the event! How to sign up 📅 OPTION 1: March 25th, Wednesday @ 8:00am Pacific Time Register here 📅 OPTION 2: March 25th, Wednesday @ 4:00pm Pacific Time Register here This is what the webinar portal will look like when you register: We look forward to seeing you there! Mike Tholfsen Group Product Manager Microsoft EducationIntegrating 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!From Zero to 16 Games in 2 Hours
From Zero to 16 Games in 2 Hours: Teaching Prompt Engineering to Students with GitHub Copilot CLI Introduction What happens when you give a room full of 14-year-olds access to AI-powered development tools and challenge them to build games? You might expect chaos, confusion, or at best, a few half-working prototypes. Instead, we witnessed something remarkable: 16 fully functional HTML5 games created in under two hours, all from students with varying programming experience. This wasn't magic, it was the power of GitHub Copilot CLI combined with effective prompt engineering. By teaching students to communicate clearly with AI, we transformed a traditional coding workshop into a rapid prototyping session that exceeded everyone's expectations. The secret weapon? A technique called "one-shot prompting" that enables anyone to generate complete, working applications from a single, well-crafted prompt. In this article, we'll explore how we structured this workshop using CopilotCLI-OneShotPromptGameDev, a methodology designed to teach prompt engineering fundamentals while producing tangible, exciting results. Whether you're an educator planning STEM workshops, a developer exploring AI-assisted coding, or simply curious about how young people can leverage AI tools effectively, this guide provides a practical blueprint you can replicate. What is GitHub Copilot CLI? GitHub Copilot CLI extends the familiar Copilot experience beyond your code editor into the command line. While Copilot in VS Code suggests code completions as you type, Copilot CLI allows you to have conversational interactions with AI directly in your terminal. You describe what you want to accomplish in natural language, and the AI responds with shell commands, explanations, or in our case, complete code files. This terminal-based approach offers several advantages for learning and rapid prototyping. Students don't need to configure complex IDE settings or navigate unfamiliar interfaces. They simply type their request, review the AI's output, and iterate. The command line provides a transparent view of exactly what's happening, no hidden abstractions or magical "autocomplete" that obscures the learning process. For our workshop, Copilot CLI served as a bridge between students' creative ideas and working code. They could describe a game concept in plain English, watch the AI generate HTML, CSS, and JavaScript, then immediately test the result in a browser. This rapid feedback loop kept engagement high and made the connection between language and code tangible. Installing GitHub Copilot CLI Setting up Copilot CLI requires a few straightforward steps. Before the workshop, we ensured all machines were pre-configured, but students also learned the installation process as part of understanding how developer tools work. First, you'll need Node.js installed on your system. Copilot CLI runs as a Node package, so this is a prerequisite: # Check if Node.js is installed node --version # If not installed, download from https://nodejs.org/ # Or use a package manager: # Windows (winget) winget install OpenJS.NodeJS.LTS # macOS (Homebrew) brew install node # Linux (apt) sudo apt install nodejs npm These commands verify your Node.js installation or guide you through installing it using your operating system's preferred package manager. Next, install the GitHub CLI, which provides the foundation for Copilot CLI: # Windows winget install GitHub.cli # macOS brew install gh # Linux sudo apt install gh This installs the GitHub command-line interface, which handles authentication and provides the framework for Copilot integration. With GitHub CLI installed, authenticate with your GitHub account: gh auth login This command initiates an interactive authentication flow that connects your terminal to your GitHub account, enabling access to Copilot features. Finally, install the Copilot CLI extension: gh extension install github/gh-copilot This adds Copilot capabilities to your GitHub CLI installation, enabling the conversational AI features we'll use for game development. Verify the installation by running: gh copilot --help If you see the help output with available commands, you're ready to start prompting. The entire setup takes about 5-10 minutes on a fresh machine, making it practical for classroom environments. Understanding One-Shot Prompting Traditional programming education follows an incremental approach: learn syntax, understand concepts, build small programs, gradually tackle larger projects. This method is thorough but slow. One-shot prompting inverts this model—you start with the complete vision and let AI handle the implementation details. A one-shot prompt provides the AI with all the context it needs to generate a complete, working solution in a single response. Instead of iteratively refining code through multiple exchanges, you craft one comprehensive prompt that specifies requirements, constraints, styling preferences, and technical specifications. The AI then produces complete, functional code. This approach teaches a crucial skill: clear communication of technical requirements. Students must think through their entire game concept before typing. What does the game look like? How does the player interact with it? What happens when they win or lose? By forcing this upfront thinking, one-shot prompting develops the same analytical skills that professional developers use when writing specifications or planning architectures. The technique also demonstrates a powerful principle: with sufficient context, AI can handle implementation complexity while humans focus on creativity and design. Students learned they could create sophisticated games without memorizing JavaScript syntax—they just needed to describe their vision clearly enough for the AI to understand. Crafting Effective Prompts for Game Development The difference between a vague prompt and an effective one-shot prompt is the difference between frustration and success. We taught students a structured approach to prompt construction that consistently produced working games. Start with the game type and core mechanic. Don't just say "make a game"—specify what kind: Create a complete HTML5 game where the player controls a spaceship that must dodge falling asteroids. This opening establishes the fundamental gameplay loop: control a spaceship, avoid obstacles. The AI now has a clear mental model to work from. Add visual and interaction details. Games are visual experiences, so specify how things should look and respond: Create a complete HTML5 game where the player controls a spaceship that must dodge falling asteroids. The spaceship should be a blue triangle at the bottom of the screen, controlled by left and right arrow keys. Asteroids are brown circles that fall from the top at random positions and increasing speeds. These additions provide concrete visual targets and define the input mechanism. The AI can now generate specific CSS colors and event handlers. Define win/lose conditions and scoring: Create a complete HTML5 game where the player controls a spaceship that must dodge falling asteroids. The spaceship should be a blue triangle at the bottom of the screen, controlled by left and right arrow keys. Asteroids are brown circles that fall from the top at random positions and increasing speeds. Display a score that increases every second the player survives. The game ends when an asteroid hits the spaceship, showing a "Game Over" screen with the final score and a "Play Again" button. This complete prompt now specifies the entire game loop: gameplay, scoring, losing, and restarting. The AI has everything needed to generate a fully playable game. The formula students learned: Game Type + Visual Description + Controls + Rules + Win/Lose + Score = Complete Game Prompt. Running the Workshop: Structure and Approach Our two-hour workshop followed a carefully designed structure that balanced instruction with hands-on creation. We partnered with University College London and students access to GitHub Education to access resources specifically designed for classroom settings, including student accounts with Copilot access and amazing tools like VSCode and Azure for Students and for Schools VSCode Education. The first 20 minutes covered fundamentals: what is AI, how does Copilot work, and why does prompt quality matter? We demonstrated this with a live example, showing how "make a game" produces confused output while a detailed prompt generates playable code. This contrast immediately captured students' attention, they could see the direct relationship between their words and the AI's output. The next 15 minutes focused on the prompt formula. We broke down several example prompts, highlighting each component: game type, visuals, controls, rules, scoring. Students practiced identifying these elements in prompts before writing their own. This analysis phase prepared them to construct effective prompts independently. The remaining 85 minutes were dedicated to creation. Students worked individually or in pairs, brainstorming game concepts, writing prompts, generating code, testing in browsers, and iterating. Instructors circulated to help debug prompts (not code an important distinction) and encourage experimentation. We deliberately avoided teaching JavaScript syntax. When students encountered bugs, we guided them to refine their prompts rather than manually fix code. This maintained focus on the core skill: communicating with AI effectively. Surprisingly, this approach resulted in fewer bugs overall because students learned to be more precise in their initial descriptions. Student Projects: The Games They Created The diversity of games produced in 85 minutes of building time amazed everyone present. Students didn't just follow a template, they invented entirely new concepts and successfully communicated them to Copilot CLI. One student created a "Fruit Ninja" clone where players clicked falling fruit to slice it before it hit the ground. Another built a typing speed game that challenged players to correctly type increasingly difficult words against a countdown timer. A pair of collaborators produced a two-player tank battle where each player controlled their tank with different keyboard keys. Several students explored educational games: a math challenge where players solve equations to destroy incoming meteors, a geography quiz with animated maps, and a vocabulary builder where correct definitions unlock new levels. These projects demonstrated that one-shot prompting isn't limited to entertainment, students naturally gravitated toward useful applications. The most complex project was a procedurally generated maze game with fog-of-war mechanics. The student spent extra time on their prompt, specifying exactly how visibility should work around the player character. Their detailed approach paid off with a surprisingly sophisticated result that would typically require hours of manual coding. By the session's end, we had 16 complete, playable HTML5 games. Every student who participated produced something they could share with friends and family a tangible achievement that transformed an abstract "coding workshop" into a genuine creative accomplishment. Key Benefits of Copilot CLI for Rapid Prototyping Our workshop revealed several advantages that make Copilot CLI particularly valuable for rapid prototyping scenarios, whether in educational settings or professional development. Speed of iteration fundamentally changes what's possible. Traditional game development requires hours to produce even simple prototypes. With Copilot CLI, students went from concept to playable game in minutes. This compressed timeline enables experimentation, if your first idea doesn't work, try another. This psychological freedom to fail fast and try again proved more valuable than any technical instruction. Accessibility removes barriers to entry. Students with no prior coding experience produced results comparable to those who had taken programming classes. The playing field leveled because success depended on creativity and communication rather than memorized syntax. This democratization of development opens doors for students who might otherwise feel excluded from technical fields. Focus on design over implementation teaches transferable skills. Whether students eventually become programmers, designers, product managers, or pursue entirely different careers, the ability to clearly specify requirements and think through complete systems applies universally. They learned to think like system designers, not just coders. The feedback loop keeps engagement high. Seeing your words transform into working software within seconds creates an addictive cycle of creation and testing. Students who typically struggle with attention during lectures remained focused throughout the building session. The immediate gratification of seeing their games work motivated continuous refinement. Debugging through prompts teaches root cause analysis. When games didn't work as expected, students had to analyze what they'd asked for versus what they received. This comparison exercise developed critical thinking about specifications a skill that serves developers throughout their careers. Tips for Educators: Running Your Own Workshop If you're planning to replicate this workshop, several lessons from our experience will help ensure success. Pre-configure machines whenever possible. While installation is straightforward, classroom time is precious. Having Copilot CLI ready on all devices lets you dive into content immediately. If pre-configuration isn't possible, allocate the first 15-20 minutes specifically for setup and troubleshoot as a group. Prepare example prompts across difficulty levels. Some students will grasp one-shot prompting immediately; others will need more scaffolding. Having templates ranging from simple ("Create Pong") to complex (the spaceship example above) lets you meet students where they are. Emphasize that "prompt debugging" is the goal. When students ask for help fixing broken code, redirect them to examine their prompt. What did they ask for? What did they get? Where's the gap? This redirection reinforces the workshop's core learning objective and builds self-sufficiency. Celebrate and share widely. Build in time at the end for students to demonstrate their games. This showcase moment validates their work and often inspires classmates to try new approaches in future sessions. Consider creating a shared folder or simple website where all games can be accessed after the workshop. Access GitHub Education resources at education.github.com before your workshop. The GitHub Education program provides free access to developer tools for students and educators, including Copilot. The resources there include curriculum materials, teaching guides, and community support that can enhance your workshop. Beyond Games: Where This Leads The techniques students learned extend far beyond game development. One-shot prompting with Copilot CLI works for any development task: creating web pages, building utilities, generating data processing scripts, or prototyping application interfaces. The fundamental skill, communicating requirements clearly to AI applies wherever AI-assisted development tools are used. Several students have continued exploring after the workshop. Some discovered they enjoy the creative aspects of game design and are learning traditional programming to gain more control. Others found that prompt engineering itself interests them, they're exploring how different phrasings affect AI outputs across various domains. For professional developers, the workshop's lessons apply directly to working with Copilot, ChatGPT, and other AI coding assistants. The ability to craft precise, complete prompts determines whether these tools save time or create confusion. Investing in prompt engineering skills yields returns across every AI-assisted workflow. Key Takeaways Clear prompts produce working code: The one-shot prompting formula (Game Type + Visuals + Controls + Rules + Win/Lose + Score) reliably generates playable games from single prompts Copilot CLI democratizes development: Students with no coding experience created functional applications by focusing on communication rather than syntax Rapid iteration enables experimentation: Minutes-per-prototype timelines encourage creative risk-taking and learning from failures Prompt debugging builds analytical skills: Comparing intended versus actual results teaches specification writing and root cause analysis Sixteen games in two hours is achievable: With proper structure and preparation, young students can produce impressive results using AI-assisted development Conclusion and Next Steps Our workshop demonstrated that AI-assisted development tools like GitHub Copilot CLI aren't just productivity boosters for experienced programmers, they're powerful educational instruments that make software creation accessible to beginners. By focusing on prompt engineering rather than traditional syntax instruction, we enabled 14-year-old students to produce complete, functional games in a fraction of the time traditional methods would require. The sixteen games created during those two hours represent more than just workshop outputs. They represent a shift in how we might teach technical creativity: start with vision, communicate clearly, iterate quickly. Whether students pursue programming careers or not, they've gained experience in thinking systematically about requirements and translating ideas into specifications that produce real results. To explore this approach yourself, visit the CopilotCLI-OneShotPromptGameDev repository for prompt templates, workshop materials, and example games. For educational resources and student access to GitHub tools including Copilot, explore GitHub Education. And most importantly, start experimenting. Write a prompt, generate some code, and see what you can create in the next few minutes. Resources CopilotCLI-OneShotPromptGameDev Repository - Workshop materials, prompt templates, and example games GitHub Education - Free developer tools and resources for students and educators GitHub Copilot CLI Documentation - Official installation and usage guide GitHub CLI - Foundation tool required for Copilot CLI GitHub Copilot - Overview of Copilot features and pricingChoosing the Right Intelligence Layer for Your Application
Introduction One of the most common questions developers ask when planning AI-powered applications is: "Should I use the GitHub Copilot SDK or the Microsoft Agent Framework?" It's a natural question, both technologies let you add an intelligence layer to your apps, both come from Microsoft's ecosystem, and both deal with AI agents. But they solve fundamentally different problems, and understanding where each excels will save you weeks of architectural missteps. The short answer is this: the Copilot SDK puts Copilot inside your app, while the Agent Framework lets you build your app out of agents. They're complementary, not competing. In fact, the most interesting applications use both, the Agent Framework as the system architecture and the Copilot SDK as a powerful execution engine within it. This article breaks down each technology's purpose, architecture, and ideal use cases. We'll walk through concrete scenarios, examine a real-world project that combines both, and give you a decision framework for your own applications. Whether you're building developer tools, enterprise workflows, or data analysis pipelines, you'll leave with a clear understanding of which tool belongs where in your stack. The Core Distinction: Embedding Intelligence vs Building With Intelligence Before comparing features, it helps to understand the fundamental design philosophy behind each technology. They approach the concept of "adding AI to your application" from opposite directions. The GitHub Copilot SDK exposes the same agentic runtime that powers Copilot CLI as a programmable library. When you use it, you're embedding a production-tested agent, complete with planning, tool invocation, file editing, and command execution, directly into your application. You don't build the orchestration logic yourself. Instead, you delegate tasks to Copilot's agent loop and receive results. Think of it as hiring a highly capable contractor: you describe the job, and the contractor figures out the steps. The Microsoft Agent Framework is a framework for building, orchestrating, and hosting your own agents. You explicitly model agents, workflows, state, memory, hand-offs, and human-in-the-loop interactions. You control the orchestration, policies, deployment, and observability. Think of it as designing the company that employs those contractors: you define the roles, processes, escalation paths, and quality controls. This distinction has profound implications for what you build and how you build it. GitHub Copilot SDK: When Your App Wants Copilot-Style Intelligence The GitHub Copilot SDK is the right choice when you want to embed agentic behavior into an existing application without building your own planning or orchestration layer. It's optimized for developer workflows and task automation scenarios where you need an AI agent to do things, edit files, run commands, generate code, interact with tools, reliably and quickly. What You Get Out of the Box The SDK communicates with the Copilot CLI server via JSON-RPC, managing the CLI process lifecycle automatically. This means your application inherits capabilities that have been battle-tested across millions of Copilot CLI users: Planning and execution: The agent analyzes tasks, breaks them into steps, and executes them autonomously Built-in tool support: File system operations, Git operations, web requests, and shell command execution work out of the box MCP (Model Context Protocol) integration: Connect to any MCP server to extend the agent's capabilities with custom data sources and tools Multi-language support: Available as SDKs for Python, TypeScript/Node.js, Go, and .NET Custom tool definitions: Define your own tools and constrain which tools the agent can access BYOK (Bring Your Own Key): Use your own API keys from OpenAI, Azure AI Foundry, or Anthropic instead of GitHub authentication Architecture The SDK's architecture is deliberately simple. Your application communicates with the Copilot CLI running in server mode: Your Application ↓ SDK Client ↓ JSON-RPC Copilot CLI (server mode) The SDK manages the CLI process lifecycle automatically. You can also connect to an external CLI server if you need more control over the deployment. This simplicity is intentional, it keeps the integration surface small so you can focus on your application logic rather than agent infrastructure. Ideal Use Cases for the Copilot SDK The Copilot SDK shines in scenarios where you need a competent agent to execute tasks on behalf of users. These include: AI-powered developer tools: IDEs, CLIs, internal developer portals, and code review tools that need to understand, generate, or modify code "Do the task for me" agents: Applications where users describe what they want—edit these files, run this analysis, generate a pull request and the agent handles execution Rapid prototyping with agentic behavior: When you need to ship an intelligent feature quickly without building a custom planning or orchestration system Internal tools that interact with codebases: Build tools that explore repositories, generate documentation, run migrations, or automate repetitive development tasks A practical example: imagine building an internal CLI that lets engineers say "set up a new microservice with our standard boilerplate, CI pipeline, and monitoring configuration." The Copilot SDK agent would plan the file creation, scaffold the code, configure the pipeline YAML, and even run initial tests, all without you writing orchestration logic. Microsoft Agent Framework: When Your App Is the Intelligence System The Microsoft Agent Framework is the right choice when you need to build a system of agents that collaborate, maintain state, follow business processes, and operate with enterprise-grade governance. It's designed for long-running, multi-agent workflows where you need fine-grained control over every aspect of orchestration. What You Get Out of the Box The Agent Framework provides a comprehensive foundation for building sophisticated agent systems in both Python and .NET: Graph-based workflows: Connect agents and deterministic functions using data flows with streaming, checkpointing, human-in-the-loop, and time-travel capabilities Multi-agent orchestration: Define how agents collaborate, hand off tasks, escalate decisions, and share state Durability and checkpoints: Workflows can pause, resume, and recover from failures, essential for business-critical processes Human-in-the-loop: Built-in support for approval gates, review steps, and human override points Observability: OpenTelemetry integration for distributed tracing, monitoring, and debugging across agent boundaries Multiple agent providers: Use Azure OpenAI, OpenAI, and other LLM providers as the intelligence behind your agents DevUI: An interactive developer UI for testing, debugging, and visualizing workflow execution Architecture The Agent Framework gives you explicit control over the agent topology. You define agents, connect them in workflows, and manage the flow of data between them: ┌─────────────┐ ┌──────────────┐ ┌──────────────┐ │ Agent A │────▶│ Agent B │────▶│ Agent C │ │ (Planner) │ │ (Executor) │ │ (Reviewer) │ └─────────────┘ └──────────────┘ └──────────────┘ Define Execute Validate strategy tasks output Each agent has its own instructions, tools, memory, and state. The framework manages communication between agents, handles failures, and provides visibility into what's happening at every step. This explicitness is what makes it suitable for enterprise applications where auditability and control are non-negotiable. Ideal Use Cases for the Agent Framework The Agent Framework excels in scenarios where you need a system of coordinated agents operating under business rules. These include: Multi-agent business workflows: Customer support pipelines, research workflows, operational processes, and data transformation pipelines where different agents handle different responsibilities Systems requiring durability: Workflows that run for hours or days, need checkpoints, can survive restarts, and maintain state across sessions Governance-heavy applications: Processes requiring approval gates, audit trails, role-based access, and compliance documentation Agent collaboration patterns: Applications where agents need to negotiate, escalate, debate, or refine outputs iteratively before producing a final result Enterprise data pipelines: Complex data processing workflows where AI agents analyze, transform, and validate data through multiple stages A practical example: an enterprise customer support system where a triage agent classifies incoming tickets, a research agent gathers relevant documentation and past solutions, a response agent drafts replies, and a quality agent reviews responses before they reach the customer, with a human escalation path when confidence is low. Side-by-Side Comparison To make the distinction concrete, here's how the two technologies compare across key dimensions that matter when choosing an intelligence layer for your application. Dimension GitHub Copilot SDK Microsoft Agent Framework Primary purpose Embed Copilot's agent runtime into your app Build and orchestrate your own agent systems Orchestration Handled by Copilot's agent loop, you delegate You define explicitly, agents, workflows, state, hand-offs Agent count Typically single agent per session Multi-agent systems with agent-to-agent communication State management Session-scoped, managed by the SDK Durable state with checkpointing, time-travel, persistence Human-in-the-loop Basic, user confirms actions Rich approval gates, review steps, escalation paths Observability Session logs and tool call traces Full OpenTelemetry, distributed tracing, DevUI Best for Developer tools, task automation, code-centric workflows Enterprise workflows, multi-agent systems, business processes Languages Python, TypeScript, Go, .NET Python, .NET Learning curve Low, install, configure, delegate tasks Moderate, design agents, workflows, state, and policies Maturity Technical Preview Preview with active development, 7k+ stars, 100+ contributors Real-World Example: Both Working Together The most compelling applications don't choose between these technologies, they combine them. A perfect demonstration of this complementary relationship is the Agentic House project by my colleague Anthony Shaw, which uses an Agent Framework workflow to orchestrate three agents, one of which is powered by the GitHub Copilot SDK. The Problem Agentic House lets users ask natural language questions about their Home Assistant smart home data. Questions like "what time of day is my phone normally fully charged?" or "is there a correlation between when the back door is open and the temperature in my office?" require exploring available data, writing analysis code, and producing visual results—a multi-step process that no single agent can handle well alone. The Architecture The project implements a three-agent pipeline using the Agent Framework for orchestration: ┌─────────────┐ ┌──────────────┐ ┌──────────────┐ │ Planner │────▶│ Coder │────▶│ Reviewer │ │ (GPT-4.1) │ │ (Copilot) │ │ (GPT-4.1) │ └─────────────┘ └──────────────┘ └──────────────┘ Plan Notebook Approve/ analysis generation Reject Planner Agent: Takes a natural language question and creates a structured analysis plan, which Home Assistant entities to query, what visualizations to create, what hypotheses to test. This agent uses GPT-4.1 through Azure AI Foundry or GitHub Models. Coder Agent: Uses the GitHub Copilot SDK to generate a complete Jupyter notebook that fetches data from the Home Assistant REST API via MCP, performs the analysis, and creates visualizations. The Copilot agent is constrained to only use specific tools, demonstrating how the SDK supports tool restriction. Reviewer Agent: Acts as a security gatekeeper, reviewing the generated notebook to ensure it only reads and displays data. It rejects notebooks that attempt to modify Home Assistant state, import dangerous modules, make external network requests, or contain obfuscated code. Why This Architecture Works This design demonstrates several principles about when to use which technology: Agent Framework provides the workflow: The sequential pipeline with planning, execution, and review is a classic Agent Framework pattern. Each agent has a clear role, and the framework manages the flow between them. Copilot SDK provides the coding execution: The Coder agent leverages Copilot's battle-tested ability to generate code, work with files, and use MCP tools. Building a custom code generation agent from scratch would take significantly longer and produce less reliable results. Tool constraints demonstrate responsible AI: The Copilot SDK agent is constrained to only specific tools, showing how you can embed powerful agentic behavior while maintaining security boundaries. Standalone agents handle planning and review: The Planner and Reviewer use simpler LLM-based agents, they don't need Copilot's code execution capabilities, just good reasoning. While the Home Assistant data is a fun demonstration, the pattern is designed for something much more significant: applying AI agents for complex research against private data sources. The same architecture could analyze internal databases, proprietary datasets, or sensitive business metrics. Decision Framework: Which Should You Use? When deciding between the Copilot SDK and the Agent Framework, or both, consider these questions about your application. Start with the Copilot SDK if: You need a single agent to execute tasks autonomously (code generation, file editing, command execution) Your application is developer-facing or code-centric You want to ship agentic features quickly without building orchestration infrastructure The tasks are session-scoped, they start and complete within a single interaction You want to leverage Copilot's existing tool ecosystem and MCP integration Start with the Agent Framework if: You need multiple agents collaborating with different roles and responsibilities Your workflows are long-running, require checkpoints, or need to survive restarts You need human-in-the-loop approvals, escalation paths, or governance controls Observability and auditability are requirements (regulated industries, enterprise compliance) You're building a platform where the agents themselves are the product Use both together if: You need a multi-agent workflow where at least one agent requires strong code execution capabilities You want Agent Framework's orchestration with Copilot's battle-tested agent runtime as one of the execution engines Your system involves planning, coding, and review stages that benefit from different agent architectures You're building research or analysis tools that combine AI reasoning with code generation Getting Started Both technologies are straightforward to install and start experimenting with. Here's how to get each running in minutes. GitHub Copilot SDK Quick Start Install the SDK for your preferred language: # Python pip install github-copilot-sdk # TypeScript / Node.js npm install @github/copilot-sdk # .NET dotnet add package GitHub.Copilot.SDK # Go go get github.com/github/copilot-sdk/go The SDK requires the Copilot CLI to be installed and authenticated. Follow the Copilot CLI installation guide to set that up. A GitHub Copilot subscription is required for standard usage, though BYOK mode allows you to use your own API keys without GitHub authentication. Microsoft Agent Framework Quick Start Install the framework: # Python pip install agent-framework --pre # .NET dotnet add package Microsoft.Agents.AI The Agent Framework supports multiple LLM providers including Azure OpenAI and OpenAI directly. Check the quick start tutorial for a complete walkthrough of building your first agent. Try the Combined Approach To see both technologies working together, clone the Agentic House project: git clone https://github.com/tonybaloney/agentic-house.git cd agentic-house uv sync You'll need a Home Assistant instance, the Copilot CLI authenticated, and either a GitHub token or Azure AI Foundry endpoint. The project's README walks through the full setup, and the architecture provides an excellent template for building your own multi-agent systems with embedded Copilot capabilities. Key Takeaways Copilot SDK = "Put Copilot inside my app": Embed a production-tested agentic runtime with planning, tool execution, file edits, and MCP support directly into your application Agent Framework = "Build my app out of agents": Design, orchestrate, and host multi-agent systems with explicit workflows, durable state, and enterprise governance They're complementary, not competing: The Copilot SDK can act as a powerful execution engine inside Agent Framework workflows, as demonstrated by the Agentic House project Choose based on your orchestration needs: If you need one agent executing tasks, start with the Copilot SDK. If you need coordinated agents with business logic, start with the Agent Framework The real power is in combination: The most sophisticated applications use Agent Framework for workflow orchestration and the Copilot SDK for high-leverage task execution within those workflows Conclusion and Next Steps The question isn't really "Copilot SDK or Agent Framework?" It's "where does each fit in my architecture?" Understanding this distinction unlocks a powerful design pattern: use the Agent Framework to model your business processes as agent workflows, and use the Copilot SDK wherever you need a highly capable agent that can plan, code, and execute autonomously. Start by identifying your application's needs. If you're building a developer tool that needs to understand and modify code, the Copilot SDK gets you there fast. If you're building an enterprise system where multiple AI agents need to collaborate under governance constraints, the Agent Framework provides the architecture. And if you need both, as most ambitious applications do, now you know how they fit together. The AI development ecosystem is moving rapidly. Both technologies are in active development with growing communities and expanding capabilities. The architectural patterns you learn today, embedding intelligent agents, orchestrating multi-agent workflows, combining execution engines with orchestration frameworks, will remain valuable regardless of how the specific tools evolve. Resources GitHub Copilot SDK Repository – SDKs for Python, TypeScript, Go, and .NET with documentation and examples Microsoft Agent Framework Repository – Framework source, samples, and workflow examples for Python and .NET Agentic House – Real-world example combining Agent Framework with Copilot SDK for smart home data analysis Agent Framework Documentation – Official Microsoft Learn documentation with tutorials and user guides Copilot CLI Installation Guide – Setup instructions for the CLI that powers the Copilot SDK Copilot SDK Getting Started Guide – Step-by-step tutorial for SDK integration Copilot SDK Cookbook – Practical recipes for common tasks across all supported languages
