Microsoft Power Platform community call - April 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 15th of April we'll have following agenda: Power Platform Updates & Events Latest on Power Platform samples Rafsan Huseynov (SHI) - Azure AI Search with Autonomous Copilot Studio Agents Delia Zuniga (Huron) - AI for Change Managers: Automating Change Impact Insights with Copilot Studio Elliot Margot (Witivio) - MCP + Copilot Studio: Let Any Agent Explore Power Platform Solutions via Chat 📅 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/homeMicrosoft 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 16th of April 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 Federico Porceddu (Avanade) – Smart Context Sample using Copilot Chat API Adam Wójcik (Hitachi Energy) – SPFx Toolkit Showcase - Use SPFx Toolkit features in any SharePoint Framework version Ejaz Hussain (Advania UK) – How to build a web part that explores Microsoft 365 Copilot APIs through Microsoft Graph 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 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!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 14th of April we'll have following agenda: News and updates from Microsoft Together mode group photo Sébastien Levert – Testing and debugging your Copilot Chat declarative agent Marc Windle & Steve Pucelik – Vibe coding meets SharePoint Embedded Ayça Baş – Connect Declarative Agent to OAuth-Protected 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!
Driving AI‑Powered Healthcare: A Data & AI Webinar and Workshop Series
Across these sessions, you’ll learn how healthcare organizations are using Microsoft Fabric, advanced analytics, and AI to unify fragmented data, modernize analytics, and enable intelligent, scalable solutions, from enterprise reporting to AI‑powered use cases. Whether you’re just getting started or looking to accelerate adoption, these sessions offer practical guidance, real‑world examples, and hands‑on learning to help you build a strong data foundation for AI in healthcare. Date Topic Details Location Registration Link May 6 Webinar: Microsoft Fabric Foundations - A Simple Path to Modern Analytics and AI Discover how Microsoft Fabric consolidates fragmented analytics into a single integrated data platform, making it easier to deliver trusted insights and adopt AI without added complexity. Virtual Register May 13 Webinar: Reduce BI Sprawl, Cut Cost and Build an AI-Ready Analytics Foundation Learn how Power BI enables enterprise BI consolidation, consistent metrics, and secure, scalable analytics that support both operational reporting and emerging AI use cases. Virtual Register May 19-20 In Person Workshop: Driving AI‑Powered Healthcare: Advanced Analytics, AI, and Real‑World Impact Attend this two‑day, in‑person event to learn how healthcare organizations use Microsoft Fabric to unify data, accelerate AI adoption, and deliver measurable clinical and operational value. Day 1 focuses on strategy, architecture, and real‑world healthcare use cases, while Day 2 offers hands‑on workshops to apply those concepts through guided labs and agent‑powered solutions. Chicago Register May 27 Webinar: Unified Data Foundation for AI & Analytics - Leveraging OneLake and Microsoft Fabric This session shows how organizations can simplify fragmented data architectures by using Microsoft Fabric and OneLake as a single, governed foundation for analytics and AI. Virtual Register May 27-28 In Person Workshop: Driving AI‑Powered Healthcare: Advanced Analytics, AI, and Real‑World Impact Attend this two‑day, in‑person event to learn how healthcare organizations use Microsoft Fabric to unify data, accelerate AI adoption, and deliver measurable clinical and operational value. Day 1 focuses on strategy, architecture, and real‑world healthcare use cases, while Day 2 offers hands‑on workshops to apply those concepts through guided labs and agent‑powered solutions. Silicon Valley Register June 2 Webinar: Delivering Personalized Patient Experiences at Scale with Microsoft Fabric and Adobe Learn how healthcare organizations can improve patient engagement by unifying trusted data in Microsoft Fabric and activating it through Adobe’s personalization platform. Virtual Register June 3-4 In Person Workshop: Driving AI‑Powered Healthcare: Advanced Analytics, AI, and Real‑World Impact Attend this two‑day, in‑person event to learn how healthcare organizations use Microsoft Fabric to unify data, accelerate AI adoption, and deliver measurable clinical and operational value. Day 1 focuses on strategy, architecture, and real‑world healthcare use cases, while Day 2 offers hands‑on workshops to apply those concepts through guided labs and agent‑powered solutions. New York Register June 10 Webinar: From Data to Decisions: How AI Data Agents in Microsoft Fabric Redefine Analytics Join us to learn how Fabric Data Agents enable users to interact with enterprise data through AI‑powered, governed agents that understand both data and business context. Virtual Register June 17 Webinar: Building the Intelligent Factory: A Unified Data and AI Approach to Life Science Manufacturing Discover how life science & MedTech manufacturers use Microsoft Fabric to integrate operational, quality, and enterprise data and apply AI‑powered analytics for smarter, faster manufacturing decisions. Virtual Register June 23-24 In Person Workshop: Driving AI‑Powered Healthcare: Advanced Analytics, AI, and Real‑World Impact Attend this two‑day, in‑person event to learn how healthcare organizations use Microsoft Fabric to unify data, accelerate AI adoption, and deliver measurable clinical and operational value. Day 1 focuses on strategy, architecture, and real‑world healthcare use cases, while Day 2 offers hands‑on workshops to apply those concepts through guided labs and agent‑powered solutions. Dallas RegisterDemystifying GitHub Copilot Security Controls: easing concerns for organizational adoption
At a recent developer conference, I delivered a session on Legacy Code Rescue using GitHub Copilot App Modernization. Throughout the day, conversations with developers revealed a clear divide: some have fully embraced Agentic AI in their daily coding, while others remain cautious. Often, this hesitation isn't due to reluctance but stems from organizational concerns around security and regulatory compliance. Having witnessed similar patterns during past technology shifts, I understand how these barriers can slow adoption. In this blog, I'll demystify the most common security concerns about GitHub Copilot and explain how its built-in features address them, empowering organizations to confidently modernize their development workflows. GitHub Copilot Model Training A common question I received at the conference was whether GitHub uses your code as training data for GitHub Copilot. I always direct customers to the GitHub Copilot Trust Center for clarity, but the answer is straightforward: “No. GitHub uses neither Copilot Business nor Enterprise data to train the GitHub model.” Notice this restriction also applies to third-party models as well (e.g. Anthropic, Google). GitHub Copilot Intellectual Property indemnification policy A frequent concern I hear is, since GitHub Copilot’s underlying models are trained on sources that include public code, it might simply “copy and paste” code from those sources. Let’s clarify how this actually works: Does GitHub Copilot “copy/paste”? “The AI models that create Copilot’s suggestions may be trained on public code, but do not contain any code. When they generate a suggestion, they are not “copying and pasting” from any codebase.” To provide an additional layer of protection, GitHub Copilot includes a “duplicate detection filter”. This feature helps prevent suggestions that closely match public code from being surfaced. (Note: This duplicate detection currently does not apply to the Copilot coding agent.) More importantly, customers are protected by an Intellectual Property indemnification policy. This means that if you receive an unmodified suggestion from GitHub Copilot and face a copyright claim as a result, Microsoft will defend you in court. GitHub Copilot Data Retention Another frequent question I hear concerns GitHub Copilot’s data retention policies. For organizations on GitHub Copilot Business and Enterprise plans, retention practices depend on how and where the service is accessed from: Access through IDE for Chat and Code Completions: Prompts and Suggestions: Not retained. User Engagement Data: Kept for two years. Feedback Data: Stored for as long as needed for its intended purpose. Other GitHub Copilot access and use: Prompts and Suggestions: Retained for 28 days. User Engagement Data: Kept for two years. Feedback Data: Stored for as long as needed for its intended purpose. For Copilot Coding Agent, session logs are retained for the life of the account in order to provide the service. Excluding content from GitHub Copilot To prevent GitHub Copilot from indexing sensitive files, you can configure content exclusions at the repository or organization level. In VS Code, use the .copilotignore file to exclude files client-side. Note that files listed in .gitignore are not indexed by default but may still be referenced if open or explicitly referenced (unless they’re excluded through .copilotignore or content exclusions). The life cycle of a GitHub Copilot code suggestion Here are the key protections at each stage of the life cycle of a GitHub Copilot code suggestion: In the IDE: Content exclusions prevent files, folders, or patterns from being included. GitHub proxy (pre-model safety): Prompts go through a GitHub proxy hosted in Microsoft Azure for pre-inference checks: screening for toxic or inappropriate language, relevance, and hacking attempts/jailbreak-style prompts before reaching the model. Model response: With the public code filter enabled, some suggestions are suppressed. The vulnerability protection feature blocks insecure coding patterns like hardcoded credentials or SQL injections in real time. Disable access to GitHub Copilot Free Due to the varying policies associated with GitHub Copilot Free, it is crucial for organizations to ensure it is disabled both in the IDE and on GitHub.com. Since not all IDEs currently offer a built-in option to disable Copilot Free, the most reliable method to prevent both accidental and intentional access is to implement firewall rule changes, as outlined in the official documentation. Agent Mode Allow List Accidental file system deletion by Agentic AI assistants can happen. With GitHub Copilot agent mode, the "Terminal auto approve” setting in VS Code can be used to prevent this. This setting can be managed centrally using a VS Code policy. MCP registry Organizations often want to restrict access to allow only trusted MCP servers. GitHub now offers an MCP registry feature for this purpose. This feature isn’t available in all IDEs and clients yet, but it's being developed. Compliance Certifications The GitHub Copilot Trust Center page lists GitHub Copilot's broad compliance credentials, surpassing many competitors in financial, security, privacy, cloud, and industry coverage. SOC 1 Type 2: Assurance over internal controls for financial reporting. SOC 2 Type 2: In-depth report covering Security, Availability, Processing Integrity, Confidentiality, and Privacy over time. SOC 3: General-use version of SOC 2 with broad executive-level assurance. ISO/IEC 27001:2013: Certification for a formal Information Security Management System (ISMS), based on risk management controls. CSA STAR Level 2: Includes a third-party attestation combining ISO 27001 or SOC 2 with additional cloud control matrix (CCM) requirements. TISAX: Trusted Information Security Assessment Exchange, covering automotive-sector security standards. In summary, while the adoption of AI tools like GitHub Copilot in software development can raise important questions around security, privacy, and compliance, it’s clear that existing safeguards in place help address these concerns. By understanding the safeguards, configurable controls, and robust compliance certifications offered, organizations and developers alike can feel more confident in embracing GitHub Copilot to accelerate innovation while maintaining trust and peace of mind.The "IQ Layer": Microsoft’s Blueprint for the Agentic Enterprise
The "IQ Layer": Microsoft’s Blueprint for the Agentic Enterprise Modern enterprises have experimented with artificial intelligence for years, yet many deployments have struggled to move beyond basic automation and conversational interfaces. The fundamental limitation has not been the reasoning power of AI models—it has been their lack of organizational context. In most organizations, AI systems historically lacked visibility into how work actually happens. They could process language and generate responses, but they could not fully understand business realities such as: Who is responsible for a project What internal metrics represent Where corporate policies are stored How teams collaborate across tools and departments Without this contextual awareness, AI often produced answers that sounded intelligent but lacked real business value. To address this challenge, Microsoft introduced a new architectural model known as the IQ Layer. This framework establishes a structured intelligence layer across the enterprise, enabling AI systems to interpret work activity, enterprise data, and organizational knowledge. The architecture is built around three integrated intelligence domains: Work IQ Fabric IQ Foundry IQ Together, these layers allow AI systems to move beyond simple responses and deliver insights that are aligned with real organizational context. The Three Foundations of Enterprise Context For AI to evolve from a helpful assistant into a trusted decision-support partner, it must understand multiple dimensions of enterprise operations. Microsoft addresses this need by organizing contextual intelligence into three distinct layers. IQ Layer Purpose Platform Foundation Work IQ Collaboration and work activity signals Microsoft 365, Microsoft Teams, Microsoft Graph Fabric IQ Structured enterprise data understanding Microsoft Fabric, Power BI, OneLake Foundry IQ Knowledge retrieval and AI reasoning Azure AI Foundry, Azure AI Search, Microsoft Purview Each layer contributes a unique type of intelligence that enables enterprise AI systems to understand the organization from different perspectives. Work IQ — Understanding How Work Gets Done The first layer, Work IQ, focuses on the signals generated by daily collaboration and communication across an organization. Built on top of Microsoft Graph, Work IQ analyses activity patterns across the Microsoft 365 ecosystem, including: Email communication Virtual meetings Shared documents Team chat conversations Calendar interactions Organizational relationships These signals help AI systems map how work actually flows across teams. Rather than requiring users to provide background context manually, AI can infer critical information automatically, such as: Project stakeholders Communication networks Decision makers Subject matter experts For example, if an employee asks: "What is the latest update on the migration project?" Work IQ can analyse multiple collaboration sources including: Project discussions in Microsoft Teams Meeting transcripts Shared project documentation Email discussions As a result, AI responses become grounded in real workplace activity instead of generic information. Fabric IQ — Understanding Enterprise Data While Work IQ focuses on collaboration signals, Fabric IQ provides insight into structured enterprise data. Operating within Microsoft Fabric, this layer transforms raw datasets into meaningful business concepts. Instead of interpreting information as isolated tables and columns, Fabric IQ enables AI systems to reason about business entities such as: Customers Products Orders Revenue metrics Inventory levels By leveraging semantic models from Power BI and unified storage through OneLake, Fabric IQ establishes a shared data language across the organization. This allows AI systems to answer strategic questions such as: "Why did revenue decline last quarter?" Instead of simply retrieving numbers, the AI can analyse multiple business drivers, including: Product performance trends Regional sales variations Customer behaviour segments Supply chain disruptions The outcome is not just data access, but decision-oriented insight. Foundry IQ — Understanding Enterprise Knowledge The third layer, Foundry IQ, addresses another major enterprise challenge: fragmented knowledge repositories. Organizations store valuable information across numerous systems, including: SharePoint repositories Policy documents Contracts Technical documentation Internal knowledge bases Corporate wikis Historically, connecting these knowledge sources to AI required complex retrieval-augmented generation (RAG) architectures. Foundry IQ simplifies this process through services within Azure AI Foundry and Azure AI Search. Capabilities include: Automated document indexing Semantic search capabilities Document grounding for AI responses Access-aware information retrieval Integration with Microsoft Purview ensures that governance policies remain intact. Sensitivity labels, compliance rules, and access permissions continue to apply when AI systems retrieve and process information. This ensures that users only receive information they are authorized to access. From Chatbots to Autonomous Enterprise Agents The full potential of the IQ architecture becomes clear when all three layers operate together. This integrated intelligence model forms the basis of what Microsoft describes as the Agentic Enterprise—an environment where AI systems function as proactive digital collaborators rather than passive assistants. Instead of simple chat interfaces, organizations will deploy AI agents capable of understanding context, reasoning about business situations, and initiating actions. Example Scenario: Supply Chain Disruption Consider a scenario where a shipment delay threatens delivery commitments. Within the IQ architecture: Fabric IQ Detects anomalies in shipment or logistics data and identifies potential risks to delivery schedules. Foundry IQ Retrieves supplier contracts and evaluates service-level agreements to determine whether penalties or mitigation clauses apply. Work IQ Identifies the logistics manager responsible for the account and prepares a contextual briefing tailored to their communication patterns. Tasks that previously required hours of investigation can now be completed by AI systems within minutes. Governance Embedded in the Architecture For enterprise leaders, security and compliance remain critical considerations in AI adoption. Microsoft designed the IQ framework with governance deeply embedded in its architecture. Key governance capabilities include: Permission-Aware Intelligence AI responses respect user permissions enforced through Microsoft Entra ID, ensuring individuals only see information they are authorized to access. Compliance Enforcement Data classification and protection policies defined in Microsoft Purview continue to apply throughout AI workflows. Observability and Monitoring Organizations can monitor AI agents and automation processes through tools such as Microsoft Copilot Studio and other emerging agent management platforms. This provides transparency and operational control over AI-driven systems. The Strategic Shift: AI as Enterprise Infrastructure Perhaps the most significant implication of the IQ architecture is the transformation of AI from a standalone tool into a foundational enterprise capability. In earlier deployments, organizations treated AI as isolated applications or experimental tools. With the IQ Layer approach, AI becomes deeply integrated across core platforms including: Microsoft 365 Microsoft Fabric Azure AI Foundry This integrated intelligence allows AI systems to behave more like experienced digital employees. They can: Understand organizational workflows Analyse complex data relationships Retrieve institutional knowledge Collaborate with human teams Enterprises that successfully implement this intelligence layers will be better positioned to make faster decisions, respond to change more effectively, and unlock new levels of operational intelligence. References: Work IQ MCP overview (preview) - Microsoft Copilot Studio | Microsoft Learn What is Fabric IQ (preview)? - Microsoft Fabric | Microsoft Learn What is Foundry IQ? - Microsoft Foundry | Microsoft Learn From Data Platform to Intelligence Platform: Introducing Microsoft Fabric IQ | Microsoft Fabric Blog | Microsoft FabricAgents League: Meet the Winners
Agents League brought together developers from around the world to build AI agents using Microsoft's developer tools. With 100+ submissions across three tracks, choosing winners was genuinely difficult. Today, we're proud to announce the category champions. 🎨 Creative Apps Winner: CodeSonify View project CodeSonify turns source code into music. As a genuinely thoughtful system, its functions become ascending melodies, loops create rhythmic patterns, conditionals trigger chord changes, and bugs produce dissonant sounds. It supports 7 programming languages and 5 musical styles, with each language mapped to its own key signature and code complexity directly driving the tempo. What makes CodeSonify stand out is the depth of execution. CodeSonify team delivered three integrated experiences: a web app with real-time visualization and one-click MIDI export, an MCP server exposing 5 tools inside GitHub Copilot in VS Code Agent Mode, and a diff sonification engine that lets you hear a code review. A clean refactor sounds harmonious. A messy one sounds chaotic. The team even built the MIDI generator from scratch in pure TypeScript with zero external dependencies. Built entirely with GitHub Copilot assistance, this is one of those projects that makes you think about code differently. 🧠 Reasoning Agents Winner: CertPrep Multi-Agent System View project CertPrep Multi-Agent System team built a production-grade 8-agent system for personalized Microsoft certification exam preparation, supporting 9 exam families including AI-102, AZ-204, AZ-305, and more. Each agent has a distinct responsibility: profiling the learner, generating a week-by-week study schedule, curating learning paths, tracking readiness, running mock assessments, and issuing a GO / CONDITIONAL GO / NOT YET booking recommendation. The engineering behind the scene here is impressive. A 3-tier LLM fallback chain ensures the system runs reliably even without Azure credentials, with the full pipeline completing in under 1 second in mock mode. A 17-rule guardrail pipeline validates every agent boundary. Study time allocation uses the Largest Remainder algorithm to guarantee no domain is silently zeroed out. 342 automated tests back it all up. This is what thoughtful multi-agent architecture looks like in practice. 💼 Enterprise Agents Winner: Whatever AI Assistant (WAIA) View project WAIA is a production-ready multi-agent system for Microsoft 365 Copilot Chat and Microsoft Teams. A workflow agent routes queries to specialized HR, IT, or Fallback agents, transparently to the user, handling both RAG-pattern Q&A and action automation — including IT ticket submission via a SharePoint list. Technically, it's a showcase of what serious enterprise agent development looks like: a custom MCP server secured with OAuth Identity Passthrough, streaming responses via the OpenAI Responses API, Adaptive Cards for human-in-the-loop approval flows, a debug mode accessible directly from Teams or Copilot, and full OpenTelemetry integration visible in the Foundry portal. Franck also shipped end-to-end automated Bicep deployment so the solution can land in any Azure environment. It's polished, thoroughly documented, and built to be replicated. Thank you To every developer who submitted and shipped projects during Agents League: thank you 💜 Your creativity and innovation brought Agents League to life! 👉 Browse all submissions on GitHub
Why Data Platforms Must Become Intelligence Platforms for AI Agents to Work
The promise and the gap Your organization has invested in an AI agent. You ask it: "Prepare a summary of Q3 revenue by region, including year-over-year trends and top product lines." The agent finds revenue numbers in a SQL warehouse, product metadata in Dataverse, regional mappings in SharePoint, historical data in Azure Blob Storage, and organizational context in Microsoft Graph. Five data sources. Five schemas. No shared definitions. The result? The agent hallucinates, returns incomplete data, or asks a dozen clarifying questions that defeat its purpose. This isn't a model limitation — modern AI models are highly capable. The real constraint is that enterprise data is not structured for reasoning. Traditional data platforms were built for humans to query. Intelligence platforms must be built for agents to _reason_ over. That distinction is the subject of this post. What you'll understand Why fragmented enterprise data blocks effective AI agents What distinguishes a storage platform from an intelligence platform How Microsoft Fabric and Azure AI Foundry work together to enable trustworthy, agent-ready data access The enterprise pain: Fragmented data breaks AI agents Enterprise data is spread across relational databases, data lakes, business applications, collaboration platforms, third-party APIs, and Microsoft Graph — each with its own schema and security model. Humans navigate this fragmentation through institutional knowledge and years of muscle memory. A seasoned analyst knows that "revenue" in the data warehouse means net revenue after returns, while "revenue" in the CRM means gross bookings. An AI agent does not. The cost of this fragmentation isn't hypothetical. Each new AI agent deployment can trigger another round of bespoke data preparation — custom integrations and transformation pipelines just to make data usable, let alone agent-ready. This approach doesn't scale. Why agents struggle without a semantic layer To produce a trustworthy answer, an AI agent needs: (1) **data access** to reach relevant sources, (2) **semantic context** to understand what the data _means_ (business definitions, relationships, hierarchies), and (3) **trust signals** like lineage, permissions, and freshness metadata. Traditional platforms provide the first but rarely the second or third — leaving agents to infer meaning from column names and table structures. This is fragile at best and misleading at worst. Figure 1: Without a shared semantic layer, AI agents must interpret raw, disconnected data across multiple systems — often leading to inconsistent or incomplete results. From storage to intelligence: What must change The fix isn't another ETL pipeline or another data integration tool. The fix is a fundamental shift in what we expect from a data platform. A storage platform asks: "Where is the data, and how do I access it?" An intelligence platform asks: "What does the data mean, who can use it, and how can an agent reason over it?" This shift requires four foundational pillars: Pillar 1: Unified data access OneLake, the data lake built into Microsoft Fabric, provides a single logical namespace across an organization. Whether data originates in a Fabric lakehouse, a warehouse, or an external storage account, OneLake makes it accessible through one interface — using shortcuts and mirroring rather than requiring data migration. This respects existing investments while reducing fragmentation. Pillar 2: Shared semantic layer Semantic models in Microsoft Fabric define business measures, table relationships, human-readable field descriptions, and row-level security. When an agent queries a semantic model instead of raw tables, it gets _answers_ — like `Total Revenue = $42.3M for North America in Q3` — not raw result sets requiring interpretation and aggregation. Before vs After: What changes for an agent? Without semantic layer: Queries raw tables Infers business meaning Risk of incorrect aggregation With semantic layer: Queries `[Total Revenue]` Uses business-defined logic Gets consistent, governed results Pillar 3: Context enrichment Microsoft Graph adds organizational signals — people and roles, activity patterns, and permissions — helping agents produce responses that are not just accurate, but _relevant_ and _appropriately scoped_ to the person asking. Pillar 4: Agent-ready APIs Data Agents in Microsoft Fabric (currently in preview) provide a natural-language interface to semantic models and lakehouses. Instead of generating SQL, an AI agent can ask: "What was Q3 revenue by region?" and receive a structured, sourced response. This is the critical difference: the platform provides structured context and business logic, helping reduce the reasoning burden on the agent. Figure 2: An intelligence platform adds semantic context, trust signals, and agent-ready APIs on top of unified data access — enabling AI agents to combine structured data, business definitions, and relationships to produce more consistent responses. Microsoft Fabric as the intelligence layer Microsoft Fabric is often described as a unified analytics platform. That description is accurate but incomplete. In the context of AI agents, Fabric's role is better understood as an **intelligence layer** — a platform that doesn't just store and process data, but _makes data understandable_ to autonomous systems. Let's look at each capability through the lens of agent readiness. OneLake: One namespace, many sources OneLake provides a single logical namespace backed by Azure Data Lake Storage Gen2. For AI agents, this means one authentication context, one discovery mechanism, and one governance surface. Key capabilities: **shortcuts** (reference external data without copying), **mirroring** (replicate from Azure SQL, Cosmos DB, or Snowflake), and a **unified security model**. For more on OneLake architecture, see [OneLake documentation on Microsoft Learn](https://learn.microsoft.com/fabric/onelake/onelake-overview). Semantic models: Business logic that agents can understand Semantic models (built on the Analysis Services engine) transform raw tables into business concepts: Raw Table Column Semantic Model Measure `fact_sales.amount` `[Total Revenue]` — Sum of net sales after returns `fact_sales.amount / dim_product.cost` `[Gross Margin %]` — Revenue minus COGS as a percentage `fact_sales.qty` YoY comparison `[YoY Growth %]` — Year-over-year quantity growth Code Snippet 1 — Querying a Fabric Semantic Model with Semantic Link (Python) import sempy.fabric as fabric # Query business-defined measures — no need to know underlying table schemas dax_query = """ EVALUATE SUMMARIZECOLUMNS( 'Geography'[Region], 'Calendar'[FiscalQuarter], "Total Revenue", [Total Revenue], "YoY Growth %", [YoY Growth %] ) """ result_df = fabric.evaluate_dax( dataset="Contoso Sales Analytics", workspace="Contoso Analytics Workspace", dax_string=dax_query ) print(result_df.head()) # NOTE: Output shown is illustrative and based on the semantic model definition # Output (illustrative): # Region FiscalQuarter Total Revenue YoY Growth % # North America Q3 FY2026 42300000 8.2 # Europe Q3 FY2026 31500000 5.7 Key takeaway: The agent doesn’t need to know that revenue is in `fact_sales.amount` or that fiscal quarters don’t align with calendar quarters. The semantic model handles all of this. Code Snippet 2 — Discovering Available Models and Measures (Python) Before an agent can query, it needs to _discover_ what data is available. Semantic Link provides programmatic access to model metadata — enabling agents to find relevant measures without hardcoded knowledge. import sempy.fabric as fabric # Discover available semantic models in the workspace datasets = fabric.list_datasets(workspace="Contoso Analytics Workspace") print(datasets[["Dataset Name", "Description"]]) # NOTE: Output shown is illustrative and based on the semantic model definition # Output (illustrative): # Dataset Name Description # Contoso Sales Analytics Revenue, margins, and growth metrics # Contoso HR Analytics Headcount, attrition, and hiring pipeline # Contoso Supply Chain Inventory, logistics, and supplier data # Inspect available measures — these are the business-defined metrics an agent can query measures = fabric.list_measures( dataset="Contoso Sales Analytics", workspace="Contoso Analytics Workspace" ) print(measures[["Table Name", "Measure Name", "Description"]]) # Output (illustrative): # Table Name Measure Name Description # Sales Total Revenue Sum of net sales after returns # Sales Gross Margin % Revenue minus COGS as a percentage # Sales YoY Growth % Year-over-year quantity growth Key takeaway: An agent can programmatically discover which semantic models exist and what measures they expose — turning the platform into a self-describing data catalog that agents can navigate autonomously. For more on Semantic Link, see the Semantic Link documentation on Microsoft Learn. Data Agents: Natural-language access for AI (preview) Note: Fabric Data Agents are currently in preview. See [Microsoft preview terms](https://learn.microsoft.com/legal/microsoft-fabric-preview) for details. A Data Agent wraps a semantic model and exposes it as a natural-language-queryable endpoint. An AI Foundry agent can register a Fabric Data Agent as a tool — when it needs data, it calls the Data Agent like any other tool. Important: In production scenarios, use managed identities or Microsoft Entra ID authentication. Always follow the [principle of least privilege](https://learn.microsoft.com/entra/identity-platform/secure-least-privileged-access) when configuring agent access. Microsoft Graph: Organizational context Microsoft Graph adds the final layer: who is asking (role-appropriate detail), what’s relevant (trending datasets), and who should review (data stewards). Fabric’s integration with Graph brings these signals into the data platform so agents produce contextually appropriate responses. Tying it together: Azure AI Foundry + Microsoft Fabric The real power of the intelligence platform concept emerges when you see how Azure AI Foundry and Microsoft Fabric are designed to work together. The integration pattern Azure AI Foundry provides the orchestration layer (conversations, tool selection, safety, response generation). Microsoft Fabric provides the data intelligence layer (data access, semantic context, structured query resolution). The integration follows a tool-calling pattern: 1.User prompt → End user asks a question through an AI Foundry-powered application. 2.Tool call → The agent selects the appropriate Fabric Data Agent and sends a natural-language query. 3.Semantic resolution → The Data Agent translates the query into DAX against the semantic model and executes it via OneLake. 4.Structured response → Results flow back through the stack, with each layer adding context (business definitions, permissions verification, data lineage). 5.User response → The AI Foundry agent presents a grounded, sourced answer to the user. Why these matters No custom ETL for agents — Agents query the intelligence platform directly No prompt-stuffing — The semantic model provides business context at query time No trust gap — Governed semantic models enforce row-level security and lineage No one-off integrations — Multiple agents reuse the same Data Agents Code Snippet 3 — Azure AI Foundry Agent with Fabric Data Agent Tool (Python) The following example shows how an Azure AI Foundry agent registers a Fabric Data Agent as a tool and uses it to answer a business question. The agent handles tool selection, query routing, and response grounding automatically. from azure.ai.projects import AIProjectClient from azure.ai.projects.models import FabricTool from azure.identity import DefaultAzureCredential # Connect to Azure AI Foundry project project_client = AIProjectClient.from_connection_string( credential=DefaultAzureCredential(), conn_str="<your-ai-foundry-connection-string>" ) # Register a Fabric Data Agent as a grounding tool # The connection references a Fabric workspace with semantic models fabric_tool = FabricTool(connection_id="<fabric-connection-id>") # Create an agent that uses the Fabric Data Agent for data queries agent = project_client.agents.create_agent( model="gpt-4o", name="Contoso Revenue Analyst", instructions="""You are a business analytics assistant for Contoso. Use the Fabric Data Agent tool to answer questions about revenue, margins, and growth. Always cite the source semantic model.""", tools=fabric_tool.definitions ) # Start a conversation thread = project_client.agents.create_thread() message = project_client.agents.create_message( thread_id=thread.id, role="user", content="What was Q3 revenue by region, and which region grew fastest?" ) # The agent automatically calls the Fabric Data Agent tool, # queries the semantic model, and returns a grounded response run = project_client.agents.create_and_process_run( thread_id=thread.id, agent_id=agent.id ) # Retrieve the agent's response messages = project_client.agents.list_messages(thread_id=thread.id) print(messages.data[0].content[0].text.value) # NOTE: Output shown is illustrative and based on the semantic model definition # Output (illustrative): # "Based on the Contoso Sales Analytics model, Q3 FY2026 revenue by region: # - North America: $42.3M (+8.2% YoY) # - Europe: $31.5M (+5.7% YoY) # - Asia Pacific: $18.9M (+12.1% YoY) — fastest growing # Source: Contoso Sales Analytics semantic model, OneLake" Key takeaway: The AI Foundry agent never writes SQL or DAX. It calls the Fabric Data Agent as a tool, which resolves the query against the semantic model. The response comes back grounded with source attribution — matching the five-step integration pattern described above. Figure 3: Each layer adds context — semantic models provide business definitions, Graph adds permissions awareness, and Data Agents provide the natural-language interface. Getting started: Practical next steps You don't need to redesign your entire data platform to begin this shift. Start with one high-value domain and expand incrementally. Step 1: Consolidate data access through OneLake Create OneLake shortcuts to your most critical data sources — core business metrics, customer data, financial records. No migration needed. [Create OneLake shortcuts](https://learn.microsoft.com/fabric/onelake/create-onelake-shortcut) Step 2: Build semantic models with business definitions For each major domain (sales, finance, operations), create a semantic model with key measures, table relationships, human-readable descriptions, and row-level security. [Create semantic models in Microsoft Fabric](https://learn.microsoft.com/fabric/data-warehouse/semantic-models) Step 3: Enable Data Agents (preview) Expose your semantic models as natural-language endpoints. Start with a single domain to validate the pattern. Note: Review the [preview terms](https://learn.microsoft.com/legal/microsoft-fabric-preview) and plan for API changes. [Fabric Data Agents overview](https://learn.microsoft.com/fabric/data-science/concept-data-agent) Step 4: Connect Azure AI Foundry agents Register Data Agents as tools in your AI Foundry agent configuration. Azure AI Foundry documentation Conclusion: The bottleneck isn't the model — it's the platform Models can reason, plan, and hold multi-turn conversations. But in the enterprise, the bottleneck for effective AI agents is the data platform underneath. Agents can’t reason over data they can’t find, apply business logic that isn’t encoded, respect permissions that aren’t enforced, or cite sources without lineage. The shift from storage to intelligence requires unified data access, a shared semantic layer, organizational context, and agent-ready APIs. Microsoft Fabric provides these capabilities, and its integration with Azure AI Foundry makes this intelligence layer accessible to AI agents. Disclaimer: Some features described in this post, including Fabric Data Agents, are currently in preview. Preview features may change before general availability, and their availability, functionality, and pricing may differ from the final release. See [Microsoft preview terms](https://learn.microsoft.com/legal/microsoft-fabric-preview) for details.
