As AI inference costs plummet toward near-zero, Berkeley researchers argue data systems must evolve along three axes. First, 'Data Systems For Agents': databases must be redesigned to handle thousands of concurrent agentic queries, exploiting multi-query optimization, approximate query processing, and proactive steering rather than passive execution. Second, 'Data Systems Of Agents': a new substrate is needed for agent swarms to manage shared state, structured memory (organized across multiple attributes for precise retrieval), concurrent edits, coordination, and failure recovery—going well beyond markdown files and knowledge graphs. Third, 'Data Systems By Agents': near-free intelligence enables agents to synthesize entire custom data systems (OLAP engines, key-value stores) from scratch in minutes, but verifying correctness against incomplete specifications remains the core challenge, with verification agents and formal proof generation as promising directions. The post concludes that agents and data systems will co-evolve, with boundaries blurring as agents design the very systems they run on.
