Request to merge multiple Microsoft Learn certification IDs
Hello Microsoft Learn Support Team, I was advised by Pearson VUE and Microsoft Support to contact Microsoft Learn to request merging of my certification profiles. I have paid for the PL-900 exam, but the exam confirmation and status are not visible due to multiple Microsoft Learn IDs. The following Microsoft Learn IDs need to be merged: - ms1100781607 - ms1100998551 - ms1100999029 Kindly help merge these IDs and associate my PL-900 exam correctly. Thank you.
Power Pages & SPA: Deploying a Custom React SPA to Microsoft Power Pages
Build modern client-side experiences with React and deploy them securely on Power Pages using Power Platform CLI. Learn how to build a React Single Page Application using Vite and deploy it to Microsoft Power Pages with Power Platform CLI while using Power Pages authentication, Web API, and Dataverse security.Building Reliable AI Coding Workflows Using Modular AI Agent Optimization
Artificial Intelligence is rapidly transforming the modern software development industry. AI-powered coding assistants such as GitHub Copilot, Claude Code, and other Large Language Model (LLM)-based systems are helping developers automate repetitive coding tasks, improve productivity, and accelerate software development processes. These tools can generate code, assist with debugging, provide recommendations, and support developers during implementation. However, despite their growing capabilities, many AI coding assistants still face challenges related to reliability, maintainability, project-specific conventions, and structured software engineering workflows. Most coding assistants perform well for generic programming tasks but often struggle when working with domain-specific development requirements, API integrations, project architectures, validation workflows, and coding standards. In real-world software engineering environments, developers require systems that not only generate code but also follow project conventions, maintain readability, support modular development, and improve long-term maintainability. The project “AI Agents Optimization” focuses on improving the reliability and effectiveness of AI coding agents by designing structured workflows, modular configurations, validation mechanisms, and optimized task execution strategies. The objective of the project is to investigate how AI agents can become dependable collaborators in practical software engineering tasks instead of functioning only as autocomplete systems. The project explores different approaches for organizing AI agent workflows using structured instruction handling, modular task division, context management, validation systems, and integration of external tools and documentation sources. Different agent configurations are analyzed and evaluated to understand how workflow optimization affects software development quality and performance. Why Existing AI Coding Workflows Often Fail Most AI coding assistants perform well for isolated coding tasks but struggle in real-world engineering environments where projects involve multiple files, coding standards, APIs, validation requirements, and contextual dependencies. For example, a generic prompt such as: “Build authentication middleware” may generate functional code, but the output often lacks: Project-specific architecture Error handling consistency Validation logic Security best practices Dependency awareness This project approaches the problem differently by introducing a structured workflow pipeline where AI agents operate in defined stages rather than generating outputs in a single step. The workflow separates planning, generation, validation, and refinement into independent modules. This improves maintainability, reduces inconsistent outputs, and supports iterative refinement similar to real software engineering workflows. Project Objectives The primary objective of this project is to optimize AI coding agents for real-world software engineering workflows. The project aims to improve how AI systems handle development tasks such as code generation, debugging, testing, validation, feature implementation, and workflow management. Another major objective is to design modular AI workflows where different stages of software development are managed systematically. The workflow focuses on task planning, instruction processing, validation, refinement, and output evaluation. This structured approach improves transparency, maintainability, and consistency in AI-generated outputs. The project also aims to evaluate how AI coding agents perform under different configurations and development scenarios. By testing multiple workflows and structured instruction methods, the project analyzes how optimization techniques improve development reliability and coding quality. Technologies and Tools Used The project utilizes multiple modern technologies and development tools for experimentation and workflow optimization. Technology / Tool Purpose Python Automation and scripting GitHub Copilot AI-assisted coding Claude / LLM APIs AI workflow experimentation Visual Studio Code Development environment Git & GitHub Version control and repository management Structured Prompting Workflow optimization MCP Concepts Tool and context integration These tools collectively support the implementation and testing of optimized AI coding workflows. Implementation Workflow The system was implemented using a modular AI workflow pipeline where each stage performs a dedicated engineering task. Step 1 — Task Parsing The user submits a development task or coding requirement. The Instruction Processing Module extracts: Objective Constraints Project context Expected output format Example structured prompt: Task: Create JWT authentication middleware Language: Node.js Constraints: - Use Express.js - Add token validation - Follow modular architecture - Include error handling Step 2 — Planning & Reasoning The Planning Module divides the task into subtasks such as: Route handling Token verification Error management Security validation This improves reasoning consistency before generation begins. Step 3 — Code Generation The Code Generation Module produces outputs using structured prompts and contextual references instead of generic instructions. Step 4 — Validation Generated outputs are validated using: Syntax checks Logical consistency checks Formatting standards Dependency validation Step 5 — Refinement If validation fails, the workflow loops back into refinement where issues are corrected before final delivery. System Workflow The workflow of the AI Agents Optimization system is based on modular task execution and structured development processes. The workflow begins with task planning and requirement analysis. The AI agent receives structured instructions along with coding constraints, project context, and validation requirements. The system processes the provided instructions and generates outputs according to defined workflows and development standards. Different configurations are tested to evaluate how instruction structures and modular task handling influence the quality of generated code The workflow also includes validation and refinement stages where generated outputs are analyzed for correctness, maintainability, and consistency. The project focuses not only on code generation but also on improving readability, workflow transparency, debugging support, and adherence to project conventions. Key Features of the Project Structured AI workflow design Modular task execution AI-assisted software development Workflow optimization strategies Validation and refinement mechanisms Integration of development tools and documentation Improved maintainability and readability Support for practical software engineering workflows Challenges Faced During Development One of the major challenges encountered during the project was maintaining consistency and reliability in AI-generated outputs. Different AI models often produce different responses depending on prompts, context, and task structure. Designing workflows that improve output stability and maintain coding standards required careful experimentation and optimization. Another challenge involved integrating structured workflows while ensuring flexibility in task execution. AI systems often require clear instructions and contextual information to produce accurate outputs. Balancing automation with maintainability and project-specific requirements was an important aspect of the project. Managing validation and refinement processes was also challenging because generated outputs needed to be evaluated not only for correctness but also for readability, maintainability, and software engineering best practices. Observations and Outcomes During experimentation, structured workflows produced more reliable and maintainable outputs compared to single-prompt generation approaches. Some important observations included: Reduced repetitive corrections during code refinement Improved consistency in generated outputs Better adherence to coding structure and formatting More stable workflow behavior for multi-step tasks Improved readability and maintainability of generated code The validation and refinement stages were particularly effective in reducing incomplete outputs and improving response quality. Although the project focuses primarily on workflow architecture and qualitative analysis rather than benchmark testing, the results demonstrate that modular AI pipelines can significantly improve practical software engineering workflows. Future Enhancements The project can be further enhanced by implementing advanced multi-agent collaboration systems where multiple AI agents work together on complex software development tasks. Future versions may also include real-time documentation integration, automated testing frameworks, cloud-based workflow management, and improved reasoning models. Additional enhancements may include IDE extensions, intelligent debugging systems, automated code review mechanisms, and adaptive workflow optimization based on project requirements. Conclusion The AI Agents Optimization project demonstrates how structured workflows and modular configurations can improve the effectiveness of AI-powered coding assistants in modern software engineering environments. By focusing on workflow optimization, validation mechanisms, modular task execution, and structured instruction handling, the project highlights the future potential of AI agents as reliable development collaborators capable of supporting real-world software engineering processes. The project represents an important step toward building dependable AI-assisted development systems that improve productivity, maintainability, and software quality while supporting modern engineering practices. How to Try This Workflow Define a structured development task Provide project constraints and context Break the task into subtasks Generate output using structured prompts Validate output quality Refine based on validation feedbackCopilot, Microsoft 365 & Power Platform product updates call
💡Microsoft 365 & Power Platform product updates call concentrates on the different use cases and features within the Microsoft 365 and in Power Platform. Call includes topics like Microsoft 365 Copilot, Copilot Studio, Microsoft Teams, Power Platform, Microsoft Graph, Microsoft Viva, Microsoft Search, Microsoft Lists, SharePoint, Power Automate, Power Apps and more. 👏 Weekly Tuesday call is for all community members to see Microsoft PMs, engineering and Cloud Advocates showcasing the art of possible with Microsoft 365 and Power Platform. 📅 On the 2nd of June we'll have following agenda: News and updates from Microsoft Together mode group photo Joe Komban – Get inspired with SharePoint Skills - the art of possible Sarah Sinclair – What's New in Microsoft Teams Shifts App: Smart Scheduling and Usability Enhancements April Dunnam – Introduction to Copilot Cowork 📞 & 📺 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!Copilot, Microsoft 365 & Power Platform Community call
💡 Copilot, Microsoft 365 & Power Platform weekly community call focuses on different use cases and features within the Copilot, Microsoft 365 and Power Platform - across Microsoft 365 Copilot, Copilot Studio, SharePoint, Power Apps and more. 👏 Looking to catch up on the latest news and updates, including cool community demos, this call is for you! 📅 On 4th of June we'll have following agenda: Copilot prompt of the week CommunityDays.org update Microsoft 365 Maturity model Latest on PnP Framework and Core SDK extension Latest on PnP PowerShell Latest on script samples Latest Copilot pro dev samples Latest on Power Platform samples Picture time with the Together Mode! Mark Kashman (Sympraxis Consulting) – Use SharePoint pages and a single-source list for tracking marketing activities Kenny Oduala (First Bank of Nigeria) – Building a Scalable Loan Management SPA in SharePoint with SPFx, Custom Navigation, and Workflow Automation Federico Porceddu (Avanade) – Introduction to Copilot Engagement Program 📅 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 👋 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!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_263805Copilot, Microsoft 365 & Power Platform Community call
💡 Microsoft 365 & Power Platform Development bi-weekly community call focuses on different use cases and features within the Microsoft 365 and Power Platform - across Microsoft 365 Copilot, Copilot Studio, SharePoint, Power Apps and more. Demos in this call are presented by the community members. 👏 Looking to catch up on the latest news and updates, including cool community demos, this call is for you! 📅 On 28th of May we'll have following agenda: Latest on SharePoint Framework (SPFx) Latest on Copilot prompt of the week PnPjs CLI for Microsoft 365 Dev Proxy Reusable Controls for SPFx SPFx Toolkit VS Code extension PnP Search Solution Demos this time Mike Fortgens (Ichicraft) – Copilot embedded in SharePoint pages Praveen Kumar R (Quadrasystems.net India) – IntelliLegal: AI-Driven Legal Document Review Demo Nello d’Andrea (die Mobiliar) – Generating page-grounded images in SharePoint with Microsoft Foundry's MAI-Image-2 📅 Download recurrent invite from https://aka.ms/community/m365-powerplat-dev-call-invite 📞 & 📺 Join the Microsoft Teams meeting live at https://aka.ms/community/m365-powerplat-dev-call-join 💡 Building something cool for Microsoft 365 or Power Platform (Copilot, SharePoint, Power Apps, etc)? We are always looking for presenters - Volunteer for a community call demo at https://aka.ms/community/request/demo 👋 See you in the call! 📖 Resources: Previous community call recordings and demos from the Microsoft Community Learning YouTube channel at https://aka.ms/community/youtube Microsoft 365 & Power Platform samples from Microsoft and community - https://aka.ms/community/samples Microsoft 365 & Power Platform community details - https://aka.ms/community/home 🧡 Sharing is caring!Copilot, Microsoft 365 & Power Platform product updates call
💡Microsoft 365 & Power Platform product updates call concentrates on the different use cases and features within the Microsoft 365 and in Power Platform. Call includes topics like Microsoft 365 Copilot, Copilot Studio, Microsoft Teams, Power Platform, Microsoft Graph, Microsoft Viva, Microsoft Search, Microsoft Lists, SharePoint, Power Automate, Power Apps and more. 👏 Weekly Tuesday call is for all community members to see Microsoft PMs, engineering and Cloud Advocates showcasing the art of possible with Microsoft 365 and Power Platform. 📅 On the 26th of May we'll have following agenda: News and updates from Microsoft Together mode group photo Vesa Juvonen & Alex Terentiev – Overriding list and library panes with SPFx Arnav Gupta – Scaling Microsoft Teams Pilots to Your Broader Frontline Organization Paolo Pialorsi – Creating your own secure MCP server 📞 & 📺 Join the Microsoft Teams meeting live at https://aka.ms/community/ms-speakers-call-join 🗓️ Download recurrent invite for this weekly call from https://aka.ms/community/ms-speakers-call-invite 👋 See you in the call! 💡 Building something cool for Microsoft 365 or Power Platform (Copilot, SharePoint, Power Apps, etc)? We are always looking for presenters - Volunteer for a community call demo at https://aka.ms/community/request/demo 📖 Resources: Previous community call recordings and demos from the Microsoft Community Learning YouTube channel at https://aka.ms/community/youtube Microsoft 365 & Power Platform samples from Microsoft and community - https://aka.ms/community/samples Microsoft 365 & Power Platform community details - https://aka.ms/community/home 🧡 Sharing is caring!Three Tiers, One Platform: Building Agents Together with the Build-Along Series
The Agentic Opportunity Every partner has agent ambition, but a single workshop format cannot serve every builder. Business makers want a fast win without writing code. Citizen developers want to orchestrate real processes across enterprise systems. Pro developers want a code-first surface with evaluations, tracing, and managed identity. Run the wrong session for the wrong audience, and engagement collapses - too abstract for one group, too constrained for another. The Agent Build-Along Series solves that by meeting builders exactly where they are, across three tiers of the Microsoft platform stack. Behind the series sits a self-serve GitHub repository - a curated library of build-along sessions with easy-to-follow, step-by-step instructions, scoped to specific industry scenarios and business functions. Partners pick the scenario that fits the room, clone the assets, and run the session. No bespoke content build, no facilitator guesswork. A Self-Serve Repository for Build-Along Sessions Diagram illustrating three tiers of agent building The repo at Agent Build-Along Github is a self-serve catalog of ready-to-run workshops. Every session is curated for a specific industry and business function, with easy-to-follow instructions a facilitator can pick up and deliver. Pick the combination that matches the room and run the session - the content, scenario, and assets are already there. Sessions are organised across three axes so partners can find the right fit fast: Industry - Financial Services, Healthcare, Retail, Manufacturing, Public Sector, Energy, Professional Services Business function - Sales, Marketing, Finance, HR, Operations, IT, Customer Service Workload - Agent Builder, Copilot Studio and Azure AI Foundry Each session in the repo includes a real-world scenario, step-by-step build instructions, sample data, prompts and configuration, and a facilitator narrative. Clone, brief the room, build the agent - that is the loop. Three Tiers - Meet Builders Where They Are The pyramid is intentional. Builders start where their skills are today and move up as confidence grows. The tier defines platform, audience, duration, and approach - content stays consistent. Tier Platform Audience Duration Approach 1 - Foundation Microsoft 365 Copilot Agent Builder Business makers, end users, departmental teams 60 min No-code 2 - Extend Copilot Studio Citizen developers, power users, ops teams 90 min Low-code 3 - Pro-Code Azure AI Foundry Pro developers, architects, AI engineers 180+ min Code-first Tier 1 - M365 Copilot Agent Builder Author your first agent - no code required. Microsoft 365 Copilot Agent Builder virtual workshop overview, What your customers will build A custom agent scoped to a real workplace scenario Grounded with knowledge sources (SharePoint sites, files, web URLs) Configured with custom instructions and conversation starters Tested in the preview pane and shared with a team Session flow Define → Ground → Refine → Test & Share Prerequisites Microsoft 365 Copilot licence (or Copilot Chat free) Modern browser (Edge / Chrome) A workplace scenario in mind, or use our template Outcome Attendees leave with a working agent they can use immediately, plus a repeatable pattern for future use cases. This is the right entry point for departmental teams who want to operationalise Copilot without engineering involvement. Tier 2 - Copilot Studio Orchestrate multi-step agents across enterprise systems. Microsoft Copilot Studio virtual workshop overview What your customers will build A copilot with generative answers and authored topics Custom actions that call APIs and Power Platform connectors Knowledge sources spanning SharePoint, Dataverse, public web, and enterprise data Channel deployment to Microsoft Teams and a public web embed Session flow Design → Connect → Author → Publish Prerequisites M365 Copilot license with access to Copilot Studio Familiarity with Power Platform helpful, not required A multi-step workflow or process in mind Outcome Attendees leave with a published copilot running in Teams or on the web, plus a pattern for connecting copilots to enterprise systems. This is where citizen developers and ops teams turn manual processes into orchestrated, agent-driven workflows. Tier 3 - Azure AI Foundry Ship a code-first agent with evaluations and observability. What your customers will build A code-first agent with custom tools and function calling Grounding via Azure AI Search and the customer's own data Model selection from the Foundry catalog, with optional fine-tuning Evaluations, tracing, and managed-identity deployment Session flow Provision → Build → Evaluate → Deploy Prerequisites Azure subscription with Azure AI Foundry access VS Code, plus Python or .NET familiarity Sample data or a use case ready to ground on Outcome Attendees leave with a deployed agent - code, evaluations, and tracing in place - plus reusable SDK templates. This is the right tier for architects and AI engineers shipping agents into production with the governance bar enterprise customers expect. How the Repository Works The repo removes the heaviest lift in running these workshops: content creation. Every session is curated against the same three axes - industry, business function, tier - and shipped with step-by-step instructions a facilitator can follow with minimum prep. Industry/Business Function × Tier → Ready-to-run Build-Along Each session in the repo ships with: A real-world scenario grounded in the chosen industry and business function Step-by-step build instructions tuned to the selected tier Sample data the agent can ground against Prompts, conversation starters, and configuration snippets Facilitator narrative so anyone can run the session Need an enterprise agent that can handle integrate with CRM data? There is a curated Copilot Studio session with a deal-desk scenario and sample CRM data. Need a pro-code health care agent? Choose the Foundry session with a patient-flow scenario and a grounded dataset. Same repo, different curated paths - at the right depth for the room. What Attendees Walk Away With Agent-Builder - A working agent in Microsoft 365 Copilot, ready to share with colleagues. Copilot Studio - A published copilot running in Teams or on the web, connected to enterprise systems. Foundry - A deployed Foundry agent with evaluations, tracing, and SDK templates for the next build. In all cases attendees leave with assets - not slides - and the confidence to build the next one without facilitation. Next Steps - Host a Build-Along Pick your tier - Start where your skills are today. Move up the pyramid as confidence grows. Drive Registration – Socialise your Build-Along session to maximise attendance. Show up and build. Bring a real scenario. Customers leave with a working agent to share. Where to Next Browse the self-serve repository of Build-Along sessions - curated by industry and business function, with step-by-step instructions ready to run: Agent Build Along.
