The Digital Press

Researchers propose redesigning data systems to prioritize AI agents, anticipating their dominance in future data tasks through a process called agentic speculation.

Why it matters: Current data systems struggle with the volume and inefficiencies of AI agents’ exploratory data queries.

The big picture: Agentic speculation involves scale, heterogeneity, redundancy, and steerability, requiring new architectures for AI-first data handling.

Research focus: The paper suggests innovations in query interfaces, processing techniques, and specialized memory stores tailored for AI agents.

Commenters say: Many acknowledge the inevitability of AI-driven data interaction shifts and debate how to best adapt systems for agent workloads.