Al agents fail in production because they're overwhelmed with data but starved for context. LLM models aren't the problem. The bottleneck is the data stack: fragmented silos, inconsistent definitions, and logic hidden in tribal knowledge. Agents need structured, reliable, and interpretable context-not just data access.

In this session, we'll show how Apache Iceberg becomes the backbone of Al-ready pipelines. You'll learn how to elevate your Iceberg implementation from a storage format to a live context layer that powers structured retrieval-augmented generation (RAG), schema-aware agents, and autonomous reasoning grounded in truth.

What we'll cover:
1. Iceberg Foundations for Al - from ACID to Time Travel
2. From Rows to Relationships - The role of the semantic layer
3. Structured RAG in Practice - Fully open source

The session includes a live demo of a fully open- source Structured RAG stack built on Apache Iceberg, featuring semantic query translation, hybrid retrieval, and governed agent reasoning. Expect architecture diagrams, real code, and practical guidance.