Extending Databricks Genie with Custom MCP: Patterns and Lessons from Enterprise Integration

This technical session explores the real-world implementation of integrating Databricks Genie with a custom MCP server, sharing practical insights and hard-won lessons from developing enterprise AI agents that seamlessly access diverse data ecosystems.

As organizations increasingly deploy AI agents for data analysis and business intelligence, the need to connect these agents with custom data sources, proprietary APIs, and specialized business logic becomes critical. This session presents a deep dive into integrating Databricks Genie—the AI/BI assistant that enables natural language querying of data—with a custom MCP server designed to extend Genie's capabilities beyond Unity Catalog into external enterprise systems.

What You'll Learn
- Architecture Patterns: Proven designs for integrating AI agents with enterprise data ecosystems using MCP

-Security Best Practices: Methods for extending Unity Catalog's governance model to external systems

-Debugging Strategies: Tools and techniques for troubleshooting MCP implementations and Genie integrations

-Performance Optimization: Approaches to minimize latency and maximize reliability in custom AI agent architectures

-Scalability Considerations: Design patterns that support growth from prototype to production-scale deployments

Target Audience:

This session is ideal for data engineers, AI/ML engineers, and platform architects who are building or planning AI agent integrations in enterprise environments. Attendees should have familiarity with Databricks platforms and basic understanding of AI agent architectures. Experience with API development and data integration patterns will be beneficial but not required.

The lessons shared are particularly valuable for organizations looking to extend their existing Databricks investments with custom AI agent capabilities while maintaining enterprise-grade security and governance standards.