Most production AI agents fail before reasoning begins: their context is noisy, incomplete, stale, too long, or impossible to verify. This talk shows how to build structured context layers that make agents, RAG systems, search, and recommendation pipelines more reliable in real enterprise environments.

Drawing from billion-scale LLM system experience, we will cover practical patterns for converting messy enterprise data such as documents, logs, tickets, policies, conversations, knowledge bases, catalogs, and multimodal assets into compact, inspectable, continuously evaluated context. The architecture combines schema discovery, self-supervised extraction, teacher-student distillation, context compression, influence scoring, deterministic guardrails, and LLM-as-judge monitoring.

Attendees will leave with a blueprint for building agent ready data infrastructure that reduces cost, improves reliability, supports evolving schemas, and makes AI decisions easier to evaluate and defend.