How to Solve the Context Gap: Engineering Reliable AI Agents

The biggest hurdle to production AI isn't the model — it's the context window. What an agent sees, when, and in what format determines whether it reasons correctly or hallucinates. Most teams stitch together RAG, tools, and state management ad hoc, then struggle to evaluate whether their context decisions actually work.This session is a deep dive into context engineering on Databricks: the systematic practice of designing what agents see at every step. You'll learn how to configure RAG systems that go beyond basic retrieval, use Model Context Protocol (MCP) for secure tool and API integration, and manage long-term memory across complex agent workflows.You'll leave with a playbook for building measurement pipelines that empirically evaluate your context decisions — and the patterns to ship agents that stay reliable when users, data, and tasks evolve.