Introduction to Fabric Data Agents

In this course module, Mitchell Pearson introduces Microsoft Fabric Data Agents, guiding students through the essential prerequisites and tenant settings required to enable and use this powerful generative AI feature within their organization. The module explains the purpose and benefits of data agents, contrasts them with the standalone co-pilot experience, and provides practical steps for building, tuning, publishing, and sharing data agents. Students will also learn about current limitations, compliance considerations, and how to navigate the Microsoft Fabric environment to manage these settings effectively.

In this module, Mitchell introduces data agents as powerful tools that create curated, AI-driven experiences by connecting to specific data sources and tables, enabling precise and customized querying beyond what standalone Copilot offers. He explains how data agents leverage natural language processing to translate user questions into complex queries across multiple data environments, reducing the need for technical expertise and improving data accuracy and trust. The session also highlights the advantages of data agents in managing data overload, tuning AI behavior, and integrating seamlessly across platforms like Teams and co-pilot studio for targeted business insights.

In this module, Mitchell guides you through creating a data agent within the Microsoft Fabric environment by setting up a sample data warehouse and connecting relevant data sources. You’ll learn how to customize your data agent by selecting specific tables, asking natural language questions, and reviewing the generated SQL queries to validate results. This hands-on approach emphasizes the iterative process of refining your data agent to ensure accurate and meaningful insights from your data.

In this module, Mitchell guides students through the essential process of adding AI instructions to data agents to create a more precise and user-friendly experience. He explains how to customize agent behavior by setting response guidelines, clarifying ambiguous queries, and formatting outputs to align with specific business contexts. Through practical demonstrations, learners will understand how to refine AI responses, manage synonyms, and troubleshoot common issues to ensure consistent and accurate results.

In this module, Mitchell demonstrates how to enhance data agents by adding example queries in KQL and SQL to improve their ability to handle complex user requests. He explains how example queries help the generative AI understand table relationships and parse intricate data structures, ensuring consistent and accurate results. The lesson also covers practical steps to create, import, and test these queries within the data agent environment, highlighting their value in optimizing performance for challenging scenarios.

In this module, Mitchell guides students through the process of publishing and sharing data agents within Microsoft Fabric, emphasizing the importance of managing access permissions to ensure data security. Students learn the distinction between draft and published versions of data agents, how to provide meaningful descriptions for better discoverability, and the various platforms from which these agents can be accessed. By the end, learners will understand how to effectively share their data agents with colleagues while maintaining control over data visibility based on user permissions.