{"data":[{"active":true,"blog_title":"Outbound Access\u00a0Protection for Spark (Generally Available)","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/workspace-outbound-access-protection-for-spark-is-now-generally-available","feature_description":"This feature enables Fabric enterprise customers to use ML models in Outbound Access Protection (OAP) enabled workspace and prevent sensitive data from getting exfiltrated.","feature_name":"Outbound Access Protection for ML Model","last_modified":"2026-04-02","product_id":"0522b590-dcd6-ee11-9079-000d3a310f67","product_name":"Data Science","release_date":"2026-03-31","release_item_id":"e3b77c88-c4bb-f011-bbd3-6045bd00f9db","release_status":"Shipped","release_type":"General availability"},{"active":true,"blog_title":"Outbound Access\u00a0Protection for Spark (Generally Available)","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/workspace-outbound-access-protection-for-spark-is-now-generally-available","feature_description":"This feature allow fabric enterprise customers to use ML experiments in Outbound Access Protection enabled workspace and prevent sensitive data from getting exfiltrated.","feature_name":"Outbound Access Protection for ML Experiment","last_modified":"2026-04-02","product_id":"0522b590-dcd6-ee11-9079-000d3a310f67","product_name":"Data Science","release_date":"2026-03-31","release_item_id":"b5548630-c4bb-f011-bbd3-6045bd00f9db","release_status":"Shipped","release_type":"General availability"},{"active":true,"blog_title":"Extending Outbound Access Protection to Fabric Warehouse and SQL Analytics Endpoint","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/extending-outbound-access-protection-to-fabric-warehouse-and-sql-analytics-endpoint","feature_description":"Outbound access protection is one of the top security asks from the Fabric Enterprise customers. Today WS OAP support only limited set of items which limits the adoption of Fabric for Customers. 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This release will introduce broad support for key data sources, including Lakehouse, Warehouse, Semantic Models, Eventhouse, SQL Databases, and Mirrored Databases. Users will also be able to configure data agent using agent-level instructions, data source-specific instructions, and example queries to tailor behavior to your scenarios. Publishing and sharing within Microsoft Fabric will also be generally available, making it easier to operationalize and collaborate on data agents.In addition, this release includes diagnostic downloads, Git integration, and deployment pipelines as part of Microsoft Fabric's Application Lifecycle Management (ALM) capabilities, enabling robust governance and lifecycle management.","feature_name":"General Availability of Fabric Data Agents","last_modified":"2026-02-24","product_id":"0522b590-dcd6-ee11-9079-000d3a310f67","product_name":"Data Science","release_date":"2026-03-31","release_item_id":"0b5b5933-5ac0-f011-bbd3-6045bd05dd14","release_status":"Planned","release_type":"General availability"},{"active":true,"blog_title":null,"blog_url":null,"feature_description":"SQL Views and Functions support in Data Agent enables the system to work with richer, production-grade SQL entities by incorporating views and reusable logic directly into query generation. 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Purview for AI intends to address these challenges by providing a set of capabilities that enable organizations to monitor, audit, and prevent data loss and risk while their users interact with LLMs from their managed devices.Microsoft Purview enables Copilot and Data Agent to leverage data security, governance, and compliance features in their apps. It provides a smooth integration path to embed data protection capabilities into their workflows, ensuring robust enterprise security, such as:*  Audit: Send prompt and response telemetry along with all associated user and system context to Purview for auditing purposes.*  eDiscovery: Send prompt and response contents, including all associated user and system context, to Purview to support electronic discovery processes.*  Data Lifecycle Management (DLM): Send prompt and response contents with all associated user and system context to Purview to manage the data lifecycle effectively.*  Communications Compliance (CC): Send prompt and response contents, along with all associated user and system context, to Purview to detect and address unethical or improper uses of AI.*  Classification: Send prompt and response contents with all associated user and system context to Purview for classification and store the classification results in compliant storage.","feature_name":"Data Agent Audit logs with Purview","last_modified":"2026-02-05","product_id":"0522b590-dcd6-ee11-9079-000d3a310f67","product_name":"Data Science","release_date":"2026-03-31","release_item_id":"2e298f0f-3801-f111-8406-000d3a36696c","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Serve real-time predictions seamlessly with ML model endpoints","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/serve-real-time-predictions-seamlessly-with-ml-model-endpoints","feature_description":"After activating ML Model endpoints in Fabric, users will need to monitor the quality of their models and the health of their endpoints in the artifact UI with a corresponding set of dynamic graphs. 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By leveraging Graph schemas and NL2GQL, the Data Agent can answer complex questions that require relationship traversal, multi-hop reasoning, and entity-centric insights more naturally and accurately. Fabric Graph integrates seamlessly alongside existing Fabric sources (such as Lakehouse and Eventhouse), allowing the agent orchestrator to select graphs when relational context is critical. This unlocks more intuitive analytical experiences, including plain-English answers with the ability to pivot into visual or interactive graph exploration. Overall, Fabric Graph expands the Data Agent's reasoning depth and makes relationship-driven insights first-class in conversational analytics.","feature_name":"[PuPr] DataAgent - Graph As a DataSource","last_modified":"2026-01-13","product_id":"0522b590-dcd6-ee11-9079-000d3a310f67","product_name":"Data Science","release_date":"2026-03-31","release_item_id":"397e4a71-ccf0-f011-8407-002248096d54","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":null,"blog_url":null,"feature_description":"This feature enables users to deploy models with internal dependencies, such as AutoML or FLAML, directly through ML model endpoints.","feature_name":"Support AutoML with FLAML in ML model endpoints","last_modified":"2025-12-24","product_id":"0522b590-dcd6-ee11-9079-000d3a310f67","product_name":"Data Science","release_date":"2025-12-31","release_item_id":"f282e269-15be-f011-bbd3-000d3a30273e","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":null,"blog_url":null,"feature_description":"As an MCP server, data agent can expose its tools as standardized tools to any AI assistant including VSCode and Claude. 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These endpoints can be called from other Fabric engines or from external apps, allowing users to deploy their models for wide, reliable consumption.","feature_name":"Real-Time Endpoints for Machine Learning Models [Public Preview]","last_modified":"2025-11-10","product_id":"0522b590-dcd6-ee11-9079-000d3a310f67","product_name":"Data Science","release_date":"2025-07-15","release_item_id":"a3b61e19-5da0-ef11-8a6a-00224804e9b4","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"Service Principal Support in Semantic Link: Enabling Scalable, Secure Automation","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/service-principal-support-in-semantic-link-enabling-scalable-secure-automation","feature_description":"This feature adds Azure service principal (SPN) support as an authentication type for Semantic Link.An Azure service principal is a non-human security identity used by applications or automation tools to access Azure resources with precise permissions.Using service principals in Semantic Link allows Power BI admins to manage and optimize Fabric items securely and efficiently, without relying on user identities.","feature_name":"Service Principal authentication supported in Semantic Link","last_modified":"2025-11-10","product_id":"0522b590-dcd6-ee11-9079-000d3a310f67","product_name":"Data Science","release_date":"2025-06-30","release_item_id":"e399e3d4-b624-f011-8c4d-00224804b6c3","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"Creator Improvements in the Data Agent","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/creator-improvements-in-the-data-agent","feature_description":"The new multi-tasking experience in the Data Agent is designed to make it easier for creators to switch between different configurations and chat with their data more fluidly. With this update, users can navigate across multiple configuration setups--such as different data sources, instructions, or example queries--without losing context or progress in their conversations.This experience directly addresses key user feedback around the friction of managing and testing configurations, especially when iterating on prompt quality. By streamlining how configurations are accessed and switched, creators can move faster, compare results across variations, and troubleshoot more effectively--all within a unified interface.","feature_name":"Multi-Tasking Configuration Experience for Data Agent Creators","last_modified":"2025-10-27","product_id":"0522b590-dcd6-ee11-9079-000d3a310f67","product_name":"Data Science","release_date":"2025-10-30","release_item_id":"878ac7c1-a461-f011-bec1-000d3a35e553","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"Accelerate Data Transformation with AI Functions in Data Wrangler (Preview)","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/accelerate-data-transformation-with-ai-functions-in-data-wrangler","feature_description":"AI functions in Data Wrangler will allow users to enrich and transform data directly within the Data Wrangler interface. With just a few clicks, users can perform tasks such as text summarization, translation, classification, sentiment analysis, grammar correction, and more, streamlining data preparation through seamless, built-in AI capabilities.","feature_name":"AI functions in Data Wrangler [Public Preview]","last_modified":"2025-10-27","product_id":"0522b590-dcd6-ee11-9079-000d3a310f67","product_name":"Data Science","release_date":"2025-09-30","release_item_id":"c638bc4d-6f5e-f011-bec2-0022480939f0","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"Accelerate Data Transformation with AI Functions in Data Wrangler (Preview)","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/accelerate-data-transformation-with-ai-functions-in-data-wrangler","feature_description":"A new suite of AI-powered operations in Data Wrangler will allow users to describe code transformations with natural language and generate the corresponding Python; translate custom Python code into PySpark code; and view high-confidence suggestions for operations based on their working data.","feature_name":"Low-Code AI-Powered Operations in Data Wrangler [GA]","last_modified":"2025-10-27","product_id":"0522b590-dcd6-ee11-9079-000d3a310f67","product_name":"Data Science","release_date":"2025-09-15","release_item_id":"053c13db-53b3-f011-bbd3-000d3a3740cc","release_status":"Shipped","release_type":"General availability"},{"active":true,"blog_title":"Fabric Data Agent now supports CI/CD, ALM Flow, and Git Integration","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/fabric-data-agent-now-supports-ci-cd-alm-flow-and-git-integration","feature_description":"Enable CI/CD for Fabric data agents.A well implemented CI/CD pipeline brings significant benefits: Automated tests integrated into the CI process validate every change to the Data agent's configuration before deployment, catching errors early and ensuring that only reliable updates reach production. A structured deployment process moves changes from a development workspace, through a test workspace that mirrors production, and finally into production, while automatically applying workspace specific configurations. 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This enables the agent to use Fabric as a data hub, tapping into the insights available within Fabric to answer user queries accurately and efficiently. By connecting to Fabric data agent, the agent can retrieve data insights directly from Fabric, allowing consumers to interact with and analyze their Fabric data seamlessly through the AI applications in Azure AI Foundry.","feature_name":"Fabric data agent integration with Azure AI Foundry","last_modified":"2025-03-31","product_id":"0522b590-dcd6-ee11-9079-000d3a310f67","product_name":"Data Science","release_date":"2025-03-31","release_item_id":"b1c46b53-f390-ef11-ac21-6045bd062aa2","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"Semantic link in Microsoft Fabric: Bridging BI and Data Science","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/semantic-link-use-fabric-notebooks-and-power-bi-datasets-for-machine-learning-data-validation-and-more","feature_description":"This feature allows users to query their Power BI Semantic Models in Fabric using natural language, receiving both a concise answer and the corresponding DAX query. Users can ask questions like 'What were the total sales over the last 12 months?' and get not only the result but also the underlying DAX query for transparency and reuse. In future, user should also be able to provide few-shot examples--sample questions- to guide the AI Skill that semantic model is the best tool to answer those questions. This approach makes data insights more accessible to all users while providing advanced users with greater control and transparency over the analysis.","feature_name":"Semantic Models as new data source for AI Skill","last_modified":"2025-02-20","product_id":"0522b590-dcd6-ee11-9079-000d3a310f67","product_name":"Data Science","release_date":"2025-02-20","release_item_id":"b53e0bb7-4c95-ef11-8a6a-002248098a98","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"New improvements coming to the AI Skill","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/new-improvements-coming-to-the-ai-skill","feature_description":"The AI Skill is now conversational, enabling users to engage in natural, back-and-forth dialogue to explore and understand their data with ease. 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Users can ask questions like 'What was the total number of logins last week?' and get not only the result but also the underlying KQL query for transparency and reuse. To enhance accuracy, users can provide few-shot examples--sample questions with expected answers. 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