Redesigning Data Systems for LLM Agent Workloads: Addressing Agentic Speculation Challenges
By
derekhecksher
Pure flour-power. Hearty enough to carry you through lunch.
Summary
This research paper argues that data systems need to be redesigned to better support LLM agents, which are predicted to become the dominant workload for data systems. The authors identify key characteristics of 'agentic speculation' - the high-throughput process agents use for data exploration and solution formulation - including scale, heterogeneity, redundancy, and steerability. They propose new research opportunities for agent-first data system architectures, including new query interfaces, processing techniques, and memory stores.
Key quotes
· 5 pulledLarge Language Model (LLM) agents, acting on their users' behalf to manipulate and analyze data, are likely to become the dominant workload for data systems in the future.
When working with data, agents employ a high-throughput process of exploration and solution formulation for the given task, one we call agentic speculation.
The sheer volume and inefficiencies of agentic speculation can pose challenges for present-day data systems.
We argue that data systems need to adapt to more natively support agentic workloads.
We take advantage of the characteristics of agentic speculation that we identify, i.e., scale, heterogeneity, redundancy, and steerability - to outline a number of new research opportunities.
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