As enterprises increasingly adopt AI-driven analytics, the demand for efficient data access continues to rise. Oracle Autonomous Data Warehouse (ADW) is a powerful platform for analytical workloads, but AI-enhanced processes—such as Agentic AI, Retrieval-Augmented Generation (RAG), and predictive modelling—place new strains on data management strategies.
The rise of Agentic AI is transforming the analytics landscape, but it comes with an often-overlooked challenge: database strain. Traditionally, operational databases are ring-fenced to prevent unstructured, inefficient queries from affecting critical business functions. However, in a world where AI agents dynamically generate and execute SQL queries to retrieve real-time data, production databases are facing unprecedented pressure.
Editors Note: This blog post originally appeared on the Elffar Analytics blog