AI agents need well-structured and reliable data to function effectively within Microsoft Fabric. Preparing your data assets correctly opens the door for AI agents to perform tasks like automated data quality monitoring, intelligent alerting on anomalies, or metadata enrichment. This session focuses on the practical steps required to prepare existing Lakehouses and Data Warehouses for interaction with custom AI agents, diving into tangible configuration and data structuring techniques.

Learn how to identify key information for an agent, how to expose it correctly within Fabric, and how to ensure the underlying data quality meets the requirements for automated processing. We will cover practical considerations based on real-world consulting experience, focusing on enabling agents to reliably understand and utilize your data assets.

Explore practical topics including:
+ Essential Metadata: Identifying, structuring, and exposing key metadata (like clear descriptions, tags, lineage hints) that agents can use for discovery and understanding within Fabric.
+ Data Quality for Agents: Implementing practical data quality checks and monitoring within Fabric pipelines or notebooks to ensure data consistency agents can trust.
+ Agent-Friendly Structures: Examples of organizing data within Lakehouse zones or designing Data Warehouse tables for easier parsing and access by automated processes.
+ Access & Permissions: Configuring appropriate security and access patterns within Fabric specifically for service principals or identities used by agents.
+ Observability: Key logging and monitoring practices to track agent activity and troubleshoot interactions with your data platforms.

Key Takeaways:
+ Identify critical information and metadata needed by AI agents interacting with Fabric data.
+ Learn practical methods for improving data quality and consistency for automated use.
+ Understand how data structure choices impact AI agent accessibility and performance.
+ Gain insights into configuring secure and appropriate access for agents in Fabric.
+ Discover practical techniques for monitoring agent interactions with Lakehouses and Data Warehouses.