Intelligence is Free, Now What? Data Systems for, of, and by Agents

A Berkeley EPIC Data Lab perspective argues that as inference costs fall toward near-zero, data systems face three challenges: redesigning for agent workloads (e.g., exploiting the 80-90% duplicate sub-queries in agentic speculation), building a substrate for agent swarms (structured memory, concurrent shared-state edits, coordination), and letting agents synthesize custom data systems that can be trusted. It surveys work including Bespoke OLAP, GenDB, and custom key-value store synthesis, and sketches a future where agents and data systems co-evolve and blur together.