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Explore how Apache Iceberg transforms from a storage format into a live context layer that powers AI agents through structured retrieval-augmented generation (RAG) in this 26-minute conference talk. Learn why AI agents fail in production due to fragmented data silos and inconsistent definitions rather than inadequate LLM models, and discover how to build AI-ready pipelines that provide agents with structured, reliable, and interpretable context. Examine Iceberg foundations for AI including ACID properties and time travel capabilities, understand the transition from rows to relationships through semantic layers, and see structured RAG implemented in practice using fully open-source solutions. Watch a live demonstration of a complete open-source Structured RAG stack built on Apache Iceberg that features semantic query translation, hybrid retrieval, and governed agent reasoning, complete with architecture diagrams, real code examples, and practical implementation guidance for elevating your lakehouse data into production-ready AI context.
