Agentic Mentor: A Specification-Driven, Multi-Agent Learning Tool
Project Overview Rather than relying on the model to know when the student understands, we built a pipeline where progress itself is gated: a student cannot move forward until they can demonstrate they understood what was just built. And rather than depending on commercial, token-billed APIs to make this teaching possible, the system is designed to run on locally hosted or free-tier models, keeping it accessible to students and institutions alike. As our contribution to Microsoft, we've handed over the full public repository behind Agentic Mentor. Given how central token-based services have become to software development, we see real potential for this approach in initiatives like GitHub Education, where affordable, agentic tools for students matter. In this post, we'll walk through what motivated the project, how Agentic Mentor works under the hood, and what we learned putting it to the test on a real piece of university coursework. The Project Journey This project was completed over three months. The first few weeks were spent on background research and requirements elicitation. We reviewed the literature on AI-assisted learning, specification-driven development and prompt ambiguity, elicited functional and non-functional requirements from our client supervisor at Microsoft, Lee Stott, and our academic supervisors, and broke the work into components we could build and test independently. This told us what was realistically achievable in the time we had, and which features were core to the tool rather than desirable extras. Implementation was coordinated through GitHub, with the team meeting daily to keep parallel work in sync and weekly meetings with both our supervisors and our client to report progress and check we were still building the right thing. We worked this way because the shape of the tool was still settling once implementation began, and that frequency of contact meant a wrong assumption surfaced in days rather than weeks. The most consequential decision of the project came out of those conversations. Agentic Mentor had been proposed as an assessment tool, but the AI does not write the assessment brief, so it cannot be relied on to read it as the academic intended, and grading on top of that reading would carry the model's misunderstanding into a student's mark. We pivoted to a learning tool, and the design of the pipeline followed from there. The final month went on completing both interfaces and evaluating the system, first against SpecBench as a correctness check, then against a real master's-level coursework. Two problems surfaced. Our GPU infrastructure went offline, so we moved to free-tier cloud models and scheduled runs around quota resets. And one of the questions the system generated turned out to be subtly wrong, and we had read it and accepted it without question, which is the automation bias we had spent the project writing about. Technical Details Agentic Mentor is built as a multi-agent, specification-driven pipeline. It has four agents; Research, Ingestion, Mentoring, and Viva. Each agent was implemented using the Microsoft Agent Framework. We chose to give each phase its own dedicated agent so that every stage could be equipped with the specific tools its task required, rather than relying on one general-purpose agent to handle the whole assignment. The agents run linearly, with each one writing its output to disk and the orchestrator passing the resulting file paths to the next stage. This design lets a session pause and resume. An overview of Agentic Mentor's architecture is seen in the figure below. Research Agent. This stage grounds the pipeline in external context. It uses GitHub and arXiv MCP servers to gather relevant literature and existing implementations. MCP gave the agent a uniform interface to both sources, meaning further ones could be added later simply by connecting another server, without reworking the agent itself. Ingestion Agent. Built around GitHub SpecKit, an open-source toolkit for spec-driven development, this agent converts the assignment brief and research context into structured specification files. Rather than letting the model resolve ambiguities in the brief on its own, SpecKit's clarification step surfaces unclear points directly to the student, who must answer before the pipeline continues. This choice keeps the student engaged in design decisions rather than letting the model make them silently. Mentoring Agent. This is where the student and the model actually build the project together, phase by phase, with the agent writing code and explaining its reasoning as it goes. Progress is gated: the student cannot move to the next phase until every task is complete, and a short multiple-choice checkpoint has been passed. The checkpoint tests understanding of what was just built rather than simply advancing on request. Viva Agent. Once implementation is complete, this agent interviews the student. Answers are checked against key points prepared in advance, with feedback given after each response and a full transcript saved for later review. How Microsoft's tools shaped the project. The Microsoft Agent Framework was the backbone that made the multi-agent design practical: it let us build four functionally distinct agents that could each carry their own tools and responsibilities while still communicating cleanly through a shared orchestration layer. Together with GitHub SpecKit's structured approach to specification-driven development, these tools gave Agentic Mentor a technical foundation that would have been considerably harder to assemble from scratch, and were central to making the pipeline's phase-gated, specification-first design actually work in practice rather than remaining a concept on paper. Demo This demo video shows an end-to-end demonstration of the VS Code extension of Agentic Mentor. Results and outcomes We evaluated Agentic Mentor in two phases: a benchmark check for basic correctness, followed by a real master's-level coursework that better matched the system's intended use. Phase 1: Code Correctness We used SpecBench to evaluate Agentic Mentor’s code correctness. SpecBench is a benchmark of 30 systems-level programming tasks with pre-existing test suites. We ran six of them using a locally served Qwen3.6-27B model. Our results show that tasks with common structural logic scored well, such as json_parser achieving a 97.7% pass rate. However, more complex tasks performed worse. On crypto_primitives, hallucinations prevented a testable solution being produced at all. This result reflects the limits of a small local model such as Qwen3.6-27B. However, the success of some tasks show that smaller local models have promise. Phase 2: Student Understanding The second phase evaluated the system against a real academic assignment on Test-Driven Development, which the team had previously completed without Agentic Mentor. Claude Sonnet 5 was used to reduce hallucinations and improve reasoning. We combined our own assessment as students with an interview with Jens Krinke, the module leader who set and assessed the coursework. From our perspective, we found that Agentic Mentor made a large, loosely specified project easier to approach, and the questions at each stage required active recall which helped us understand the assignment better. It improved on our original attempt by constructing a synthetic repository with known test–production pairs as concrete acceptance criteria, linking test and production files by co-occurrence in commit history rather than naming conventions, and producing unit test coverage within a properly separated project structure. The assessment of its understanding was less favourable. Jens rated its comprehension as comparable to a typical student's, but no better: it treated commit history as capable of confirming the presence of TDD, when it can only reveal the degree of its absence, and it missed an implicit hint regarding commit size. Lessons Learned Building Agentic Mentor taught our team a great deal about coding agents, local models, and the practical challenges of applying AI in education. These lessons shaped both the tool itself and how we think about deploying AI in learning contexts. Knowing where the tool fits. One of our clearest takeaways was that Agentic Mentor works best on well-specified, undergraduate-level assignments rather than open-ended, research-style coursework. Our evaluation on SpecBench's json_parser task, where the tool achieved a 97.7% pass rate, showed just how effective it can be on concrete, well-bounded problems. Our Test-Driven Development case study showed the same tool struggling once the task demanded interpreting ambiguous or partially hidden objectives. Even so, our own experience using the tool showed genuine improvements in our learning outcomes and even surfaced implementation ideas we hadn't considered ourselves, which reinforced our belief in the promise of agentic learning tools when applied to the right kind of problem. Navigating the local model trade-off. Our extensive experimentation with locally hosted models taught us that there is a trade-off between accessibility and quality. Local models performed well on smaller, simpler tasks, but as task complexity grew, code correctness declined and hallucinations became more frequent. This was a valuable lesson in engineering trade-offs: a fully local, cost-free deployment is achievable in principle, but a genuinely effective one currently depends on access to larger, cloud-hosted models. Rethinking what the tool should be for. Perhaps our biggest shift in thinking came from an idea we abandoned. We originally envisioned Agentic Mentor as an assessment tool, capable of evaluating a student's understanding for official grading. Working through the implementation made clear why that vision doesn't hold up: an AI system that didn't generate the assessment brief itself cannot be guaranteed to interpret an educator's intent correctly. This is a limitation of natural language interpretation that is well documented in the literature we reviewed. Recognizing this early enough to change course was itself a valuable exercise in engineering judgment, and it led us to reposition Agentic Mentor as a learning tool focused on helping students engage with agentic AI, rather than an assessment tool that asks AI to make judgments it isn't equipped to make reliably. Implications for Educators Our experience building and evaluating Agentic Mentor highlighted three critical takeaways for educators looking to integrate AI into their classrooms. Watch out for automation bias. Our academic evaluation revealed that the AI agents occasionally held technical misconceptions, which raised up the risk of automation bias; one of the biggest risks in AI-assisted education. When an AI speaks with absolute confidence, it’s incredibly easy to believe it. If students trust the model's outputs without sufficient questioning, they bypass the critical thinking the coursework was meant to provoke. Educational AI needs built-in friction to force the AI to highlight its own uncertainties and encourage students to question the output. Be careful using AI as an Assessment tool. The discovery of the AI’s technical misconceptions also suggests caution using AI for grading or formal assessment. Because the model can misunderstand core concepts the exact same way a student might, it cannot reliably evaluate student comprehension. This also led us to pivot Agentic Mentor strictly to a learning tool. AI is an excellent tool for supporting student learning through phased tasks, but it is not currently reliable enough to act as an autonomous judge of a student's underlying understanding. Agentic Development is reshaping software engineering. As agentic development reshapes software engineering, educators must shift their focus from teaching students how to write code manually, to teaching them how to prompt coding agents. This includes using practices such as specification-driven development, which reduces AI errors in code by preventing ambiguities in natural language prompts. Future Development The limitations we encountered while building Agentic Mentor point to two clear directions for further development. Both would make the system a more reliable learning tool for students. Validation agent Agentic Mentor currently interprets a project with the same gaps and misconceptions a typical student might bring to it. Because the system presents its output with confidence, those gaps can be passed on unchallenged. We propose extending the pipeline with a validation agent. This component would be dedicated to interrogating the questions and conclusions the system produces. Its purpose would be to introduce a deliberate layer of hesitation, surfacing assumptions explicitly rather than allowing them to reach the student as established fact. The intended outcome is a reduction in automation bias, encouraging students to engage critically with the system's output rather than accepting it uncritically. Integration with academic platforms As long as the system's understanding of an assignment is constrained in the same ways a student's is, it cannot be relied upon to identify a task's critical elements. A future version could accept explicit input from educators, specifying those critical elements in advance along with guidance on how students should be directed through them. This would require Agentic Mentor to move beyond a standalone local tool and integrate with the institution's existing education platform. Such integration would give educators direct control over the system's behaviour, making it both more trustworthy and more productive as a learning aid. Conclusion Agentic Mentor was built to help students learn from their coding coursework rather than simply complete it, embedding the learning process into the structure of the system instead of relying on vibe coding. We found that a model cannot be trusted to be consistently correct. Running on smaller local models trades capability for accessibility. And measuring student understanding is difficult without a large user study. Agentic Mentor nonetheless represents a step towards integrating AI-assisted development into education while preserving student understanding. With adequate guardrails against automation bias, it can serve as an accessible tool that adapts the learning process to the changes AI has brought to software development. Call to Action The challenge of "one-click" AI code generation in education is here to stay, but with Agentic Mentor, we aim to keep students actively engaged in problem-solving rather than bypassing it. We invite you to explore our work and help us build the future of AI in education. Explore the Code: Visit the Agentic Mentor GitHub Repository Run It Your Way: Execute the orchestrator via the CLI (see the main README), or use our custom GUI by running the VS Code Extension (located in the agentic-mentor-extension folder). Tools: Our pipeline is built on the Microsoft Agent Framework for multi-agent routing and GitHub SpecKit to enforce strict Specification-Driven Development. Team Our team involved in developing this project included 6 members. All of us are Masters students at UCL, studying either Software Systems Engineering or Artificial Intelligence and Data Engineering. Mark Connor – Team Leader – Software Engineer GitHub URL: https://github.com/markjconnor LinkedIn URL: http://www.linkedin.com/in/mark-connor2003 Alexander Filippov – Software Engineer GitHub URL: https://github.com/ucabavf LinkedIn URL: https://www.linkedin.com/in/alexander-f-003a5721b Weeraya Hew – Software Engineer GitHub URL: https://github.com/tingwry LinkedIn URL: https://www.linkedin.com/in/weeraya-hew-924a19261 Tanishka Jaikrishnia – Software Engineer GitHub URL: https://github.com/tanishkajaikrishnia LinkedIn URL: https://www.linkedin.com/in/tanishka-jaikrishnia-96b652274/ Pranav Kannan – Software Engineer GitHub URL: https://github.com/pranavk295 LinkedIn URL: https://www.linkedin.com/in/pranav-kannan-0b2a11221 Gabriel Mardakhaev – Software Engineer GitHub URL: https://github.com/gabmardakhaev LinkedIn URL: https://www.linkedin.com/in/gabriel-mardakhaev Special Thanks to Contributors We want to express our deepest gratitude to the following contributors, whose ongoing support and dedication led to the success of this project. Lee Stott, Principal Cloud Advocate Manager at Microsoft He Ye, Academic Supervisor Jens Krinke, Senior Lecturer and Academic Supervisor
Copilot, Microsoft 365 & Power Platform product updates call
💡Copilot, Microsoft 365 & Power Platform product updates call concentrates on the different use cases and features within the Microsoft 365 and in Power Platform. Call includes topics like Microsoft 365 Copilot, Copilot Studio, Microsoft Teams, Power Platform, Microsoft Graph, Microsoft Viva, Microsoft Search, Microsoft Lists, SharePoint, Power Automate, Power Apps and more. 👏 Weekly Tuesday call is for all community members to see Microsoft PMs, engineering and Cloud Advocates showcasing the art of possible with Microsoft 365 and Power Platform. 📅 On the 15th of September we'll have following agenda: News and updates from Microsoft Together mode group photo Rémi Dyon – Building an Agent with GitHub Harness in Copilot Studio Steve Pucelik + Marc Windle – Latest on SharePoint Embedded Vesa Juvonen – Surfacing your business apps in Copilot canvas – IT concierge scenario 📞 & 📺 Join the Microsoft Teams meeting live at https://aka.ms/community/ms-speakers-call-join 🗓️ Download recurrent invite for this weekly call from https://aka.ms/community/ms-speakers-call-invite 👋 See you in the call! 💡 Building something cool for Microsoft 365 or Power Platform (Copilot, SharePoint, Power Apps, etc)? We are always looking for presenters - Volunteer for a community call demo at https://aka.ms/community/request/demo 📖 Resources: Previous community call recordings and demos from the Microsoft Community Learning YouTube channel at https://aka.ms/community/youtube Microsoft 365 & Power Platform samples from Microsoft and community - https://aka.ms/community/samples Microsoft 365 & Power Platform community details - https://aka.ms/community/home 🧡 Sharing is caring!Microsoft Power Platform community call - September 2026
💡 Power Platform monthly community call focuses on different extensibility options for builders, makers and developers within the Power Platform. Typically demos are from our awesome community members who showcase the art of possible within the Power Platform capabilities. 👏 Looking to catch up on the latest news and updates, including cool community demos, this call is for you! 📅 On 16th of September we'll have following agenda: Power Platform Updates & Events Latest on Power Platform samples John Liu - How to easily to convert markdown documents to PDF with Power Automate Sanjiv Venkatram - Using Azure AI video analyzer + Functions + Storage & Power Platform to analyze echocardiograms Billur Şamdancıoğlu - Copilot Cowork for Dynamics 365 Finance and Operations Elliot Margot - Stop Clicking Through Run History: Build Your Own MCP to Debug Power Automate Flows 📅 Download recurrent invite from https://aka.ms/powerplatformcommunitycall 📞 & 📺 Join the Microsoft Teams meeting live at https://aka.ms/PowerPlatformMonthlyCall 💡 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 365 & Power Platform community YouTube channel at https://aka.ms/community/videos Microsoft 365 & Power Platform samples from Microsoft and community - https://aka.ms/community/samples Microsoft 365 & Power Platform community details - https://aka.ms/community/homeCopilot, Microsoft 365 & Power Platform Community call
💡 Copilot, Microsoft 365 & Power Platform 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 17th of September 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 Josh Bray – Managing Power Platform Environments from SharePoint with an SPFx Web Part Yves Habersaat – Agent-Driven Power Apps: Canvas Authoring MCP Server with GitHub Copilot CLI Mohammed Amer – Build Interactive Component Components UX for Copilot canvas for Microsoft 365 Roadmap Features 📅 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!AI where and when you need it with Dragon Copilot's ability to focus
At HIMSS 2026, we showed that the most useful place to apply clinical intelligence is often not in a separate app or side window, but at the cursor, in the field where the clinician is already working. Our last post on this topic described the problem Dragon Copilot's focus capability is designed to solve: clinicians becoming data couriers, moving between windows, copying information from one system into another, and stitching together a day's work across multiple applications. That post looked ahead to what was coming. Now, it's here. What Dragon Copilot's focus capability does Dragon Copilot's focus capability brings AI directly into the text field where clinicians work, whether that's an EHR, a browser, Microsoft Word, SharePoint, or another desktop application. There is no separate application to launch and no workflow to rebuild. Simply place your cursor where you're working, select content if needed, and ask for help using natural language by voice or text. The focus capability works directly within the application you're already using, helping you stay in the flow of work. With focus, Dragon Copilot can: Read content from applications such as an EHR, browser, Microsoft Word, SharePoint, and other desktop applications. Answer questions about selected content and provide responses in context, using information from trusted sources and resources approved by the organization. Surface relevant information and insights without requiring manual searches, copying, or pasting. Generate, edit, and refine text directly where you're working. The experience is conversational and voice-first. Because the focus capability operates directly at the cursor, clinicians can take action without switching applications or rebuilding context. Questions are answered in context using information from the EHR and trusted sources, including resources approved by the organization. Focus in action The following examples illustrate how Dragon Copilot's focus capability helps clinicians read, answer, write, edit and surface information directly within their workflow. Journal and guideline Q&A Highlight a passage from a clinical guideline or journal article and ask: "Summarize the three key takeaways in patient-friendly language for the after-visit summary." The focus capability analyzes the selected content and generates a draft directly where you're working. Answer questions in context Clinicians often have the information they need in front of them but spend valuable time locating or interpreting it. Highlight relevant content and ask: "What does this recommendation mean for a patient with diabetes?" The focus capability reviews the selected information and delivers an answer within the workflow, without requiring clinicians to leave the application. The information is sourced from the EHR, a credible source such as MSD Manual, DailyMed, Medline Plus or from an organization’s internal source. Edit in place Select existing text and ask: "Convert this into a bulleted list." or "Summarize this HPI in two sentences." The focus capability reshapes the content directly where it already exists, helping clinicians refine documentation without disrupting their workflow. Surface relevant information Place your cursor inside a note and ask: "What's the patient's BMI?" The focus capability can retrieve relevant information available in context and return the answer inline, eliminating the need to navigate between screens, search for values, or perform calculations manually. Generate new content Need a referral letter, patient instruction, or summary? Simply ask. The focus capability can generate content directly within the destination field, helping clinicians create documentation where it belongs without copying and pasting between applications. Why this capability matters Bringing AI directly to the cursor is more than a convenience. It's what makes AI practical, visible, and actionable within everyday clinical workflows. Dragon Copilot's focus capability sits in a useful middle ground between ambient AI, which operates largely in the background, and traditional dictation, which requires clinicians to explicitly construct documentation themselves. Outputs are reviewable and editable before being finalized. Actions remain visible within the workflow, providing transparency and control. Clinicians can work without repeatedly navigating between applications or rebuilding context. Because the focus capability operates directly at the cursor, it creates an extensible foundation for future Dragon Copilot experiences and integrations from Microsoft and partners. When Dragon Copilot's focus capability brings AI directly to where clinicians are already working, copying, pasting, and window-switching fall away. Clinicians can access information, generate content, and take action without leaving their workflow, helping them spend less time managing technology and more time focused on patient care. Learn more about Dragon Copilot.
Phone verification is not working
Hello. I was logged out of my accounts when I opened Outlook on Windows. I managed to log into the first one, but I forgot my password of the other one. I am struggling to log into that account. That account is not connected to an authenticator app, but is connected to my phone number. I tried resetting my password using my phone and entered last 4 digits of my phone number, but the method is NOT WORKING for some reason. I also tried verifying my account with my phone number, but I didn't get any code from Microsoft. All the accounts that I buy online with use this email account. Please help.
Windows 10 unable to troubleshoot
Hello, I have an Asus laptop running Windows 10 and occasionally, it would lag (even offline while I am going through images like cropping a photo or clicking through document files- it would take 3 seconds for the action to occur)Apart from that, the start up would take tremendously long time. But since last week, I couldn't get past the black and/ blue screens after powering my laptop on ... I've already tried some, not all the troubleshooting available in the Advanced Options because even the loading alone doesn't always allow me to the next step as it shuts down instead. So I am tired of repeating the same cycle over and over again. I'll just briefly list the ones I WAS able to try and the messages I got from them for reference: Automatic Repair----- Automatic Repair couldnt repair your PC... c windows system32 logfiles srt srttrail.txt Uninstall Updates--- Not sure if it did anything because it didnt display another page Reset this PC: Keep Files.... tried both Cloud download and Local download and it both said "unable to download" Command prompt : I tried the 'recoveryenabled no' and 'safeboot minimal' Now I downloaded Media Creation Tool from a working computer in a USB but I cant even get to the blue screen. Is this how I can get it back? Or are there other options? I also wanted to make sure if I do this, will it get rid of personal files? If not, how do I make sure that I retain my files because I dont have a back up of all of them.Copilot, Microsoft 365 & Power Platform Community call
💡 Copilot, Microsoft 365 & Power Platform 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 10th of September we'll have following agenda: Copilot prompt of the week CommunityDays.org update Microsoft 365 Maturity model PnP Framework and Core SDK extension PnP PowerShell Script samples Copilot pro dev samples Power Platform samples Marlon König – Document Analysis in Practice: AI Builder vs. Content Understanding in a Real-World Customer Project Sankalp Saoji – From Insights to Action: Building a Power BI AI Agent with Microsoft Copilot Studio Peter Paul Kirschner – Integrating UI elements into Copilot with 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 Copilot, Microsoft 365 or Power Platform (Copilot Studio, SharePoint, Power Apps, etc)? We are always looking for presenters - Volunteer for a community call demo at https://aka.ms/community/request/demo 👋 See you in the call! 📖 Resources: Previous community call recordings and demos from the Microsoft Community Learning YouTube channel at https://aka.ms/community/youtube Microsoft 365 & Power Platform samples from Microsoft and community - https://aka.ms/community/samples Microsoft 365 & Power Platform community details - https://aka.ms/community/home 🧡 Sharing is caring!Copilot, Microsoft 365 & Power Platform product updates call
💡Copilot, Microsoft 365 & Power Platform product updates call concentrates on the different use cases and features within the Microsoft 365 and in Power Platform. Call includes topics like Microsoft 365 Copilot, Copilot Studio, Microsoft Teams, Power Platform, Microsoft Graph, Microsoft Viva, Microsoft Search, Microsoft Lists, SharePoint, Power Automate, Power Apps and more. 👏 Weekly Tuesday call is for all community members to see Microsoft PMs, engineering and Cloud Advocates showcasing the art of possible with Microsoft 365 and Power Platform. 📅 On the 8th of September we'll have following agenda: News and updates from Microsoft Together mode group photo Rémi Dyon – Building Skills in the new Copilot studio experience Fabia Williams – Ask Copilot how your agents are being used - Introduction to Insights Agent Vesa Juvonen – Creating Copilot Components with SPFx: How Does It Actually Work? 📞 & 📺 Join the Microsoft Teams meeting live at https://aka.ms/community/ms-speakers-call-join 🗓️ Download recurrent invite for this weekly call from https://aka.ms/community/ms-speakers-call-invite 👋 See you in the call! 💡 Building something cool for Microsoft 365 or Power Platform (Copilot, SharePoint, Power Apps, etc)? We are always looking for presenters - Volunteer for a community call demo at https://aka.ms/community/request/demo 📖 Resources: Previous community call recordings and demos from the Microsoft Community Learning YouTube channel at https://aka.ms/community/youtube Microsoft 365 & Power Platform samples from Microsoft and community - https://aka.ms/community/samples Microsoft 365 & Power Platform community details - https://aka.ms/community/home 🧡 Sharing is caring!Building Production-Ready AI Agents with Microsoft Foundry
Excited to host the first event of the Dallas AI Agents User Group! 🚀 Building Production-Ready AI Agents with Microsoft Foundry Join us for a practical session exploring how to build, test, and evaluate production-ready AI agents using Microsoft Foundry. 🎤 Speaker: Jothsna Praveena — AI Architect 🎙️ Moderator: Pramod BN — Cloud Solutions Architect 📅 September 5, 2026 | 6:00–7:00 PM 📍 Microsoft Innovation Hub, Irving, TX Address: Building LC1, 7000 State Hwy 161, Irving, TX 75039 Register Here: https://luma.com/4kd3pukt Looking forward to an evening of learning, meaningful conversations, and community building around AI agents! #AgenticAI #AIAgents #MicrosoftFoundry #ArtificialIntelligence #DallasAI #MicrosoftAI #AICommunity
