Agentic Analytics on the Apache Lakehouse

This article discusses the challenges and solutions for implementing agentic analytics on the Apache Lakehouse. It highlights the need for AI agents to have business context, governed access, and interactive speed to avoid hallucinations. The semantic layer, built through virtual datasets in Dremio, translates technical schemas into business logic, enabling accurate AI-driven queries. Additionally, it covers Autonomous Reflections, which optimize query performance by monitoring patterns and automatically tuning the system. The article is part of a series on open-source lakehouse technologies like Apache Parquet, Iceberg, and Arrow.