AI and agentic workloads on open lakehouses — optimizing Iceberg for LLM agents, sub-second queries, MCP integration, and intelligent data access.
4 articles
AI agents fail in production because they are overwhelmed with data but starved for context. The bottleneck is not the model — it is the data stack. Apache Iceberg turns lakehouse storage into a live, versioned context layer that powers structured RAG, schema-aware agents, and governed reasoning grounded in truth.
AI agents are becoming primary consumers of Iceberg lakehouse data — querying tables iteratively, at high frequency, and without human review. This guide walks through the five components your infrastructure needs to support agentic workloads — MCP connectivity, guardrails, multi-engine routing, self-optimizing storage, and observability — and shows how LakeOps provides each one.
Netflix spent years building an intelligent lakehouse — Polaris for catalog management, Autotune for compaction, janitors for cleanup, and Metacat for observability. LakeOps lets every team build the same — and go beyond — in minutes. Here is what an intelligent lakehouse actually requires, and how LakeOps provides each component.
AI agents issue SQL iteratively, repeat query templates at high frequency, and need sub-second responses from tables designed for batch workloads. This post covers what breaks when agents hit a production Iceberg lake — and the five infrastructure layers that fix it: MCP connectivity, guardrails, multi-engine routing, self-optimizing storage, and closed-loop feedback.
