Adam Gurary

Building agents is challenging. Most teams iterate on agent quality by vibe-checking. Try a question, fix what looks wrong, ship. It works at first and breaks down fast. Each fix can revive an old bug. Coding agents make this worse by rewriting the agent in seconds while verification stays manual.This session introduces a new best practice. With one MLflow command, your coding agent turns into a rigorous builder that grows a test suite from inside the vibe-check loop. Every piece of feedback on a bad answer becomes an automated test. Every coding-agent fix runs against the accumulated suite, scored and recorded in MLflow. By the time your agent is ready to ship, you have a real test suite that grew alongside the build.We walk through this on stage end to end. We start with a prototype agent that has no tracing and no eval, and we end with a production-ready agent backed by a test suite that grew alongside the build.

This session is repeated. Mosaic AI Vector Search is powering high-accuracy retrieval systems in production across a wide range of use cases — including RAG applications, entity resolution, recommendation systems and search. Fully integrated with the Databricks Data Intelligence Platform, it eliminates pipeline maintenance by automatically syncing data from source to index. Over the past year, customers have asked for greater scale, better quality out-of-the-box and cost-efficient performance. This session delivers on those needs — showcasing best practices for implementing high-quality retrieval systems and revealing major product advancements that improve scalability, efficiency and relevance. What you’ll learn: - How to optimize Vector Search with hybrid retrieval and reranking for better out-of-the-box results - Best practices for managing vector indexes with minimal operational overhead - Real-world examples of how organizations have scaled and improved their search and recommendation systems

Most enterprise data is trapped in unstructured formats — documents, PDFs, scanned images and tables — making it difficult to access, analyze and use. This session shows how to unlock that hidden value by building intelligent document processing workflows on the Databricks Data Intelligence Platform. You’ll learn how to ingest unstructured content using Lakeflow Connect, extract structured data with AI Parse — even from complex tables and scanned documents — and apply analytics or AI to this newly structured data. What you’ll learn: - How to build scalable pipelines that transform unstructured documents into structured tables - Techniques for automating document workflows with Databricks tools - Strategies for maintaining quality and governance with Unity Catalog - Real-world examples of AI applications built with intelligent document processing