Microsoft Power Platform community call - March 2026
đĄ 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. đ Looking to catch up on the latest news and updates, including cool community demos, this call is for you! đ On 18th of March we'll have following agenda: Power Platform Updates & Events Latest on Power Platform samples Shiv Sharma (Ameriprise Financial Services) - Design Full App Pages in Minutes with AIâPowered Generative Pages in Power Apps Darshan Magdum (Nihilent) - Adding Filters and Microsoft Teams Width to Adaptive Cards, and Tooltips & Anchor Tags via YAML in Copilot Studio Giridhar Mungamuri (EY) - Getting Started with Power Apps Code Apps: Build Your First Real App with the New ProâCode Experience đ Download recurrent invite from https://aka.ms/powerplatformcommunitycall đ & đș Join the Microsoft Teams meeting live at https://aka.ms/PowerPlatformMonthlyCall đĄ 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 đ See you in the call! đ 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/home
CPOR Association for Incentives and Recognition
CPOR Association for Incentives and Recognition As I am sure you are all aware we all need to claim CPOR when we are not the billing partner. I have seen some changes to this process in recent months and wanted to get some clarification please Build Intent - Advisor (Pre-sales) We have recently been told that this is only for incentives So now Msft will pay you for workloads that you are not recognised for ?? Influenced Revenue Recognition - Non Incentivised This will get you association and recognition for the workloads Usage Recognition - Non Incentivised This will get you association and recognition for the workloads You appear to only be able to claim this if you use the Msft template. All other signed documentation appear to be rejected My questions are When did Msft make these changes and why What is the difference between Influenced and Usage Recognition Why MUST we use the template, and not be able to use a signed SoW with the details included We are now potentially making 3 claims per customer to ensure that they are fully associated to our Solution Designation scores
Tech Talks presents: Build Power Pages sites with AI usingâ agentic coding tools
Join us on Thursday, March 12th at 8am PTâŻas Neeraj Nandwana, Principal PM, Power Platform presents 'Build Power Pages sites with AI usingâ agentic coding tools (preview)'. We'll get a public preview of the Power Pages plugin for GitHub Copilot CLI and Claude Code. With this plugin, you simply describe the site you want in natural language. The plugin takes care of everything else, from project scaffolding and Dataverse setup to API integrations, permissions, and deployment. This plugin is purpose-built for Power Pages. It understands table permissions, web roles, site settings, setting up authentication and Web API patterns. The code it generates is platform-aware, so you spend less time on manual configuration and more time building your site. We hope to see you there! Call to Action:⯠Click on the link to save the calendar invite: https://aka.ms/TechTalksInvite⯠View past recordings (sign in required):âŻhttps://aka.ms/TechTalksRecordingPartner Training | Build and extend AI-powered copilots with Microsoft Copilot Studio
Microsoft Copilot Studio lets you create powerful AI-powered copilots for a range of requestsâfrom providing simple answers to common questions to resolving issues requiring complex conversations. Learn how you can build and extend these copilots, boot their capabilities with AI and extend them to perform actions, and provide answers for your copilot users. This three-day virtual training experience includes two-to-four hours a day of structured instructor-led training theory, hands-on labs to help develop specialized skills, and live Q&A with technical experts. Enroll today at: https://aka.ms/CopilotStudioPartnerTraining Note: Either register or sign into the Depth Training page, and then click on the Copilot Studio card to enroll, selecting the session appropriate to your time zone.
Building High-Performance Agentic Systems
Most enterprise chatbots fail in the same quiet way. They answer questions. They impress in demos. And then they stall in production. Knowledge goes stale. Answers cannot be audited. The system cannot act beyond generating text. When workflows require coordination, execution, or accountability, the chatbot stops being useful. Agentic systems exist because that model is insufficient. Instead of treating the LLM as the product, agentic architecture embeds it inside a bounded control loop: plan â act (tools) â observe â refine The model becomes one component in a runtime system with explicit state management, safety policies, identity enforcement, and operational telemetry. This shift is not speculative. A late-2025 MIT Sloan Management Review / BCG study reports that 35% of organizations have already adopted AI agents, with another 44% planning deployment. Microsoft is advancing open protocols for what it calls the âagentic web,â including Agent-to-Agent (A2A) interoperability and Model Context Protocol (MCP), with integration paths emerging across Copilot Studio and Azure AI Foundry. The real question is no longer whether agents are coming. It is whether enterprise architecture is ready for them. This article translates âagenticâ into engineering reality: the runtime layers, latency and cost levers, orchestration patterns, and governance controls required for production deployment. The Core Capabilities of Agentic AI What makes an AI âagenticâ is not a single featureâitâs the interaction of different capabilities. Together, they form the minimum set needed to move from âansweringâ to âoperatingâ. Autonomy â Goal-Driven Task Completion Traditional bots are reactive: they wait for a prompt and produce output. Autonomy introduces a goal state and a control loop. The agent is given an objective (or a trigger) and it can decide the next step without being micromanaged. The critical engineering distinction is that autonomy must be bounded: in production, you implement it with explicit budgets and stop conditionsâmaximum tool calls, maximum retries, timeouts, and confidence thresholds. The typical execution shape is a loop: plan â act â observe â refine. A project-management agent, for example, doesnât just answer âwhatâs the status?â It monitors signals (work items, commits, build health), detects a risk pattern (slippage, dependency blockage), and then either surfaces an alert or prepares a remediation action (re-plan milestones, notify owners). In high-stakes environments, autonomy is usually human-in-the-loop by design: the agent can draft changes, propose next actions, and only execute after approval. Over time, teams expand the autonomy envelope for low-risk actions while keeping approvals for irreversible or financially sensitive operations. Tool Integration â Taking Action and Staying Current A standalone LLM cannot fetch live enterprise state and cannot change it. Tool integration is how an agent becomes operational: it can query systems of record, call APIs, trigger workflows, and produce outputs that reflect the current world rather than the modelâs pretraining snapshot. There are two classes of tools that matter in enterprise agents: Retrieval tools (grounding / RAG)When the agent needs facts, it retrieves them. This is the backbone of reducing hallucination: instead of guessing, the agent pulls authoritative content (SharePoint, Confluence, policy repositories, CRM records, Fabric datasets) and uses it as evidence. In practice, retrieval works best when it is engineered as a pipeline: query rewrite (optional) â hybrid search (keyword + vector) â filtering (metadata/ACL) â reranking â compact context injection. The point is not âstuff the prompt with documents,â but âinject only the minimum evidence required to answer accurately.â Action tools (function calling / connectors) These are the hands of the agent: update a CRM record, create a ticket, send an email, schedule a meeting, generate a report, run a pipeline. Tool integration shifts value from âadviceâ to âexecution,â but also introduces riskâso action tools need guardrails: least-privilege permissions, input validation, idempotency keys, and post-condition checks (confirm the update actually happened). In Microsoft ecosystems, this tool plane often maps to Graph actions + business connectors (via Logic Apps/Power Automate) + custom APIs, with Copilot Studio (low code) or Foundry-style runtimes (pro code) orchestrating the calls. Memory (Context & Learning) â Context Awareness and Adaptation âMemoryâ is not just a long prompt. In agentic systems, memory is an explicit state strategy: Working memory: what the agent has learned during the current run (intermediate tool results, constraints, partial plans). Session memory: what should persist across turns (user preferences, ongoing tasks, summarized history). Long-term memory: enterprise knowledge the agent can retrieve (indexed documents, structured facts, embeddings + metadata). Short-term memory enables multi-step workflows without repeating questions. An HR onboarding agent can carry a new hireâs details from intake through provisioning without re-asking, because the workflow state is persisted and referenced. Long-term âlearningâ is typically implemented through feedback loops rather than real-time model weight updates: capturing corrections, storing validated outcomes, and periodically improving prompts, routing logic, retrieval configuration, or (where appropriate) fine-tuning. The key design rule is that memory must be policy-aware: retention rules, PII handling, and permission trimming apply to stored state as much as they apply to retrieved documents. Orchestration â Coordinating Multi-Agent Teams Complex enterprise work is rarely single-skill. Orchestration is how agentic systems scale capability without turning one agent into an unmaintainable monolith. The pattern is âmanager + specialistsâ: an orchestrator decomposes the goal into subtasks, routes each to the best tool or sub-agent, and then composes a final response. This can be done sequentially or in parallel. Employee onboarding is a classic: HR intake, IT account creation, equipment provisioning, and training scheduling can run in parallel where dependencies allow. The engineering challenge is making orchestration reliable: defining strict input/output contracts between agents (often structured JSON), handling failures (timeouts, partial completion), and ensuring only one component has authority to send the final user-facing message to avoid conflicting outputs. In Microsoft terms, orchestration can be implemented as agentic flows in Copilot Studio, connected-agent patterns in Foundry, or explicit orchestrators in code using structured tool schemas and shared state. Strategic Impact â How Agentic AI Changes Knowledge Work Agentic AI is no longer an experimental overlay to enterprise systems. It is becoming an embedded operational layer inside core workflows. Unlike earlier chatbot deployments that answered isolated questions, modern enterprise agents execute end-to-end processes, interact with structured systems, maintain context, and operate within governed boundaries. The shift is not about conversational intelligence alone; it is about workflow execution at scale. The transformation becomes clearer when examining real implementations across industries. In legal services, agentic systems have moved beyond document summarization into operational case automation. Assembly Softwareâs NeosAI, built on Azure AI infrastructure, integrates directly into legal case management systems and automates document analysis, structured data extraction, and first-draft generation of legal correspondence. What makes this deployment impactful is not merely the generative drafting capability, but the integration architecture. NeosAI is not an isolated chatbot; it operates within the same document management systems, billing systems, and communication platforms lawyers already use. Firms report time savings of up to 25 hours per case, with document drafting cycles reduced from days to minutes for first-pass outputs. Importantly, the system runs within secure Azure environments with zero data retention policies, addressing one of the most sensitive concerns in legal AI adoption: client confidentiality. JPMorganâs COiN platform represents another dimension of legal and financial automation. Instead of conversational assistance, COiN performs structured contract intelligence at production scale. It analyzes more than 12,000 commercial loan agreements annually, extracting over 150 clause attributes per document. Work that previously required approximately 360,000 human hours now executes in seconds. The architecture emphasizes structured NLP pipelines, taxonomy-based clause classification, and private cloud deployment for regulatory compliance. Rather than replacing legal professionals, the system flags unusual clauses for human review, maintaining oversight while dramatically accelerating analysis. Over time, COiN has also served as a knowledge retention mechanism, preserving institutional contract intelligence that would otherwise be lost with employee turnover. In financial services, the impact is similarly structural. Morgan Stanleyâs internal AI Assistant allows wealth advisors to query over 100,000 proprietary research documents using natural language. Adoption has reached nearly universal usage across advisor teams, not because it replaces expertise, but because it compresses research time and surfaces insights instantly. Building on this foundation, the firm introduced an AI meeting debrief agent that transcribes client conversations using speech-to-text models and generates CRM notes and follow-up drafts through GPT-based reasoning. Advisors review outputs before finalization, preserving human judgment. The result is faster client engagement and measurable productivity improvements. What differentiates Morgan Stanleyâs approach is not only deployment scale, but disciplined evaluation before release. The firm established rigorous benchmarking frameworks to test model outputs against expert standards for accuracy, compliance, and clarity. Only after meeting defined thresholds were systems expanded firmwide. This patternâevaluation before scaleâis becoming a defining trait of successful enterprise agent deployment. Human Resources provides a different perspective on agentic AI. Johnson Controls deployed an AI HR assistant inside Slack to manage policy questions, payroll inquiries, and onboarding support across a global workforce exceeding 100,000 employees. By embedding the agent in a channel employees already use, adoption barriers were reduced significantly. The result was a 30â40% reduction in live HR call volume, allowing HR teams to redirect focus toward strategic workforce initiatives. Similarly, Ciena integrated an AI assistant directly into Microsoft Teams, unifying HR and IT support through a single conversational interface. Employees no longer navigate separate portals; the agent orchestrates requests across backend systems such as Workday and ServiceNow. The technical lesson here is clear: integration breadth drives usability, and usability drives adoption. Engineering and IT operations reveal perhaps the most technically sophisticated application of agentic AI: multi-agent orchestration. In a proof-of-concept developed through collaboration between Microsoft and ServiceNow, an AI-driven incident response system coordinates multiple agents during high-priority outages. Microsoft 365 Copilot transcribes live war-room discussions and extracts action items, while ServiceNowâs Now Assist executes operational updates within IT service management systems. A Semantic Kernelâbased manager agent maintains shared context and synchronizes activity across platforms. This eliminates the longstanding gap between real-time discussion and structured documentation, automatically generating incident reports while freeing engineers to focus on remediation rather than clerical tasks. The system demonstrates that orchestration is not conceptualâit is operational. Across these examples, the pattern is consistent. Agentic AI changes knowledge work by absorbing structured cognitive labor: document parsing, compliance classification, research synthesis, workflow routing, transcription, and task coordination. Humans remain essential for judgment, ethics, and accountability, but the operational layer increasingly runs through AI-mediated execution. The result is not incremental productivity improvement; it is structural acceleration of knowledge processes. Design and Governance Challenges â Managing the Risks As agentic AI shifts from answering questions to executing workflows, governance must mature accordingly. These systems retrieve enterprise data, invoke APIs, update records, and coordinate across platforms. That makes them operational actors inside your architectureânot just assistants. The primary shift is this: autonomy increases responsibility. Agents must be observable. Every retrieval, reasoning step, and tool invocation should be traceable. Without structured telemetry and audit trails, enterprises lose visibility into why an agent acted the way it did. Agents must also operate within scoped authority. Least-privilege access, role-based identity, and bounded credentials are essential. An HR agent should not access finance systems. A finance agent should not modify compliance data without policy constraints. Autonomy only works when it is deliberately constrained. Execution boundaries are equally critical. High-risk actionsâfinancial approvals, legal submissions, production changesâshould include embedded thresholds or human approval gates. Autonomy should be progressive, not absolute. Cost and performance must be governed just like cloud infrastructure. Agentic systems can trigger recursive calls and model loops. Without usage monitoring, rate limits, and model-tier routing, compute consumption can escalate unpredictably. Finally, agentic systems require continuous evaluation. Real-world testing, live monitoring, and drift detection ensure the system remains aligned with business rules and compliance requirements. These are not âset and forgetâ deployments. In short, agentic AI becomes sustainable only when autonomy is paired with observability, scoped authority, embedded guardrails, cost control, and structured oversight. Conclusion â Towards the Agentic Enterprise The organizations achieving meaningful returns from agentic AI share a common pattern. They do not treat AI agents as experimental tools. They design them as production systems with defined roles, scoped authority, measurable KPIs, embedded observability, and formal governance layers. When autonomy is paired with integration, memory, orchestration, and governance discipline, agentic AI becomes more than automationâit becomes an operational architecture. Enterprises that master this architecture are not merely reducing costs; they are redefining how knowledge work is executed. In this emerging model, human professionals focus on strategic judgment and innovation, while AI agents manage structured cognitive execution at scale. The competitive advantage will not belong to those who deploy the most AI, but to those who deploy it with architectural rigor and governance maturity. Before we rush to deploy more agents, a few questions are worth asking: If an AI agent executes a workflow in your enterprise today, can you trace every reasoning step and tool invocation behind that decision? Does your architecture treat AI as a conversational layer - or as an operational actor with scoped identity, cost controls, and policy enforcement? Where should autonomy stop in your organization - and who defines that boundary? Agentic AI is not just a capability shift. It is an architectural decision. Curious to hear how others are designing their control planes and orchestration layers. References MIT Sloan â âAgentic AI, Explainedâ by Beth Stackpole: A foundational overview of agentic AI, its distinction from traditional generative AI, and its implications for enterprise workflows, governance, and strategy. Microsoft TechCommunity â âIntroducing Multi-Agent Orchestration in Foundry Agent Serviceâ: Details Microsoftâs multi-agent orchestration capabilities, including Connected Agents, Multi-Agent Workflows, and integration with A2A and MCP protocols. Microsoft Learn â âExtend the Capabilities of Your Agent â Copilot Studioâ: Explains how to build and extend custom agents in Microsoft Copilot Studio using tools, connectors, and enterprise data sources. Assembly Softwareâs NeosAI case â Microsoft Customer Stories JPMorgan COiN platform â GreenData Case Study HR support AI (Johnson Controls, Ciena, Databricks) â Moveworks case studies ServiceNow & Semantic Kernel multi-agent P1 Incident â Microsoft Semantic Kernel BlogTech Talks presents: Governance to manage Low Code Agents
Join us on Thursday, March 5th at 8am PTâŻas Ken Auguillard, Principal Program Manager, Power Platform presents 'Getting started with or adopting your current governance to manage low code agents'. We hope to see you there! Call to Action:⯠Click on the link to save the calendar invite: https://aka.ms/TechTalksInvite⯠View past recordings (sign in required):âŻhttps://aka.ms/TechTalksRecordingPartner Case Study | Cognizant
Cognizant, a 2025 Microsoft Partner of the Year award winner and global services company, is redefining enterprise transformation across industries. By democratizing AI and embedding intelligent automation, Cognizant helps organizations modernize systems, reduce costs, and accelerate growth. Their unique approachâcombining deep Microsoft expertise, strategic change management, and a culture of innovationâdelivers measurable results, including millions in savings, productivity boosts, and record-setting employee engagement. Organizations across industries face mounting technical debt, fragmented legacy systems, and the urgent need to consolidate onto modern, scalable platforms. Three Cognizant clientsâone in healthcare, one in financial services, and one in retailâfaced similar pressures. Regulatory compliance and governance added further complexity, especially in healthcare and finance. Cognizantâs clients needed a forward-looking technology roadmap to streamline operations, unlock data-driven insights, and support automation for better employee and customer experiences. Cognizantâs approach: Empowerment through Microsoft intelligent automation Using Microsoft Power Platform, Copilot agents, and robust governance frameworks, Cognizant delivers solutions that are secure, scalable, and tailored to each clientâs needs. Ongoing training, upskilling, and community-building ensure sustained adoption and a strong digital culture. An essential part of that digital culture is Cognizantâs belief that innovation with AI should be accessible to employees at every levelânot just technical teams. âAt Cognizant, intelligent automation is more than a technology capabilityâitâs a strategic enabler for business transformation,â said Chakradhar Gooty Agraharam, Global Intelligent Automation Leader at Cognizant. âBy combining Microsoft Power Platform, Microsoft Dynamics 365, and Copilot innovations with our deep industry expertise, we help clients accelerate automation at scale, unlock new efficiencies, and deliver measurable outcomes. Our partnership with Microsoft empowers us to lead with innovation, speed, and precisionâdriving hyper automation strategies that redefine whatâs possible for enterprises worldwide.â For their healthcare, financial services, and retail engagements, Cognizant selected solutions that met each client's immediate needs while supporting long-term scalability. They implemented strong governance frameworksâusing the Microsoft Power Platform Center of Excellence Starter Kit alongside their own methodologiesâto maintain regulatory compliance and secure operations. By empowering clients to adopt Microsoft intelligent automation tools, including Copilot agents, Microsoft Power Automate, and Power Platform, Cognizant helped their clients streamline processes, unlock insights, and enhance both employee and customer experiences. Continue reading here Explore all case studies or submit your own Subscribe to case studies tag to follow all new case study posts. Don't forget to follow this blog to receive email notifications of new stories!Microsoft 365 & Power Platform product updates call
đĄMicrosoft 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. đ 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. đ On the 3rd of March we'll have following agenda: News and updates from Microsoft Together mode group photo SĂ©bastien Levert â Creating your first Copilot Chat declarative agent with Microsoft 365 Agents Toolkit April Dunnam â Using Microsoft 365 Copilot in Power Apps đ & đș Join the Microsoft Teams meeting live at https://aka.ms/community/ms-speakers-call-join đïž Download recurrent invite for this weekly call from https://aka.ms/community/ms-speakers-call-invite đ See you in the call! đĄ 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 đ 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 𧥠Sharing is caring!Per certification designed badges
Hi First Microsoft opted out from awesome Credly (awesome, as learners collected âallâ personal certifications in one place, no matter the vendor - easy to share the Credly profile link for various reasons) And now you have quit creating âper certification branded badgeâs, and only provide standard âAssociateâ & âExpertâ badges with a âLearn diploma) showing the name of the certification âin textâ (the new Fabric exam as example) For us globally in roles like âAlliance Managersâ, âPartner Managersâ, driving and summarizing partners excellence in the area of Microsoft + pushing with marketing us and Microsoft- this is bad! Example on how we earlier are using the per certification badges â Is it just by mistake you have taken this path? Or is it just me and my learners that have missed where they can download per exam branded badges for newer certifications now? Regards Gabriel
