Data plays a critical role in agentic AI, enabling AI agents to operate independently, make informed decisions, and take meaningful actions. And that’s why we are expanding capabilities and deepening integrations between our data and AI platforms.
Starting today, organizations can use Azure AI Foundry to connect customized, conversational agents, created in Fabric. A critical component of Azure AI Agent Service is the ability to securely ground AI agent outputs in enterprise knowledge, so that responses are accurate, relevant, and contextually aware. Data agents in Fabric, formerly known as AI skills, can retrieve knowledge across different data sources – from lakehouse and warehouse data to Power BI semantic models and KQL databases – using a number of specialized query language tools that help AI to generate SQL, KQL and DAX to extract, process, and present data effectively and delivers precise, actionable insights. Fabric data agents can determine when to use specific data, how to combine it, and what insights matter most.
By combining Fabric’s sophisticated data analysis over enterprise data with Azure AI Foundry’s cutting-edge GenAI technology, businesses can create custom conversational AI agents leveraging domain expertise. This seamless integration enables organizations to develop agents that are not only based on unstructured data in Azure AI Search or SharePoint but also integrate with structured and semantic data in Microsoft OneLake, thereby enhancing data-driven decision-making.
Customers like NTT DATA are leveraging data agents in Microsoft Fabric to extract real time insights. NTT DATA built a suite of HR-focused data agents, helping users interact directly with real-time data to uncover patterns in staffing, chargeability, and productivity.
“We see data agents as a conversational capability layer we can use to ‘talk’ to our data, understand it, and derive different insights in support of our daily decision making. By significantly improving real-time actionable insights, Azure AI and Fabric help elevate business outcomes as well as human potential.”
Genis Campa, Head of Data Products Strategy, NTT DATA
We invite you to explore the preview and experience firsthand how this unified approach can transform your data into a powerful asset for more insightful decision-making.
What if generating embeddings in Eventhouse didn’t require an external endpoint, callout policies, throttling management, or per‑request costs? That’s exactly what slm_embeddings_fl() delivers: a new user-defined function (UDF) that generates text embeddings using local Small Language Models (SLMs) from within the Kusto Python sandbox, returning vectors that you can immediately use for semantic search, similarity …
Continue reading “Create Embeddings in Fabric Eventhouse with built-in Small Language Models (SLMs)”
As 2025 ends, we’re taking a moment to reflect on Microsoft Fabric’s second year in the market and the collective progress made alongside our community, customers, and partners. What began as a unified vision for data and AI has grown into a platform adopted by more than 28,000 organizations worldwide, anchored by OneLake and shaped …
Continue reading “Microsoft Fabric 2025 holiday recap: Unified Data and AI Innovation”
