Elise Gonzales

Build a GitHub CI workflow that uses specialist agents to review pull requests, coordinate bounded repairs, and validate the resulting changes. In this hands-on workshop, you’ll connect agents across models and coding harnesses, define their roles and permissions, and test the pipeline while Omnigent handles session lifecycles, delegation, and messaging.

The gap between a working agent prototype and a deployed enterprise app is bigger than most teams expect. In this demo-led session, we’ll build a custom agent with the Agent Bricks agent platform and show the pieces required to ship it: multi-model access, governed structured and unstructured data, tools, memory, traces, evals, monitoring, and flexible deployment options. Come learn about the full agent platform on Databricks.

Agent work is moving beyond a single developer prompting a single model on a laptop. Omnigent provides the capabilities needed for team-scale agent work: portable context across harnesses, reusable agents and tools, session history, policy and quality protocols, async approvals, and mobile and web UI. This deep dive shows how those pieces fit together to make agent work persistent, collaborative, and production-ready. We'll show this in both forms: Omnigent OSS, the open meta-harness you can run anywhere, and Omnigent on Databricks, where the same agents, skills, and policies run on top of the governed Databricks Platform.

Learn how to build sophisticated systems that enable natural language interactions with both your structured databases and unstructured document collections. This session explores advanced techniques for creating unified and governed AI systems that can seamlessly interpret questions, retrieve relevant information and generate accurate answers across your entire data ecosystem. Key takeaways include: - Strategies for combining vector search over unstructured documents with retrieval from structured databases - Techniques for optimizing unstructured data processing through effective parsing, metadata enrichment and intelligent chunking - Methods for integrating different retrieval mechanisms while ensuring consistent data governance and security - Practical approaches for evaluating and improving KBQA system quality through automated and human feedback

Want to create AI agents that can do more than just generate text? Join us to explore how combining Databricks' Mosaic AI Agent Framework with the Model Context Protocol (MCP) unlocks powerful tool-calling capabilities. We'll show you how MCP provides a standardized way for AI agents to interact with external tools, data and APIs, solving the headache of fragmented integration approaches. Learn to build agents that can retrieve both structured and unstructured data, execute custom code and tackle real enterprise challenges. Key takeaways: - Implementing MCP-enabled tool-calling in your AI agents - Prototyping in AI Playground and exporting for deployment - Integrating Unity Catalog functions as agent tools - Ensuring governance and security for enterprise deployments Whether you're building customer service bots or data analysis assistants, you'll leave with practical know-how to create powerful, governed AI agents.