为智能代理设计新一代数据系统，提升其处理复杂任务的效率。
Supporting Our AI Overlords: Redesigning Data Systems to be Agent-First
- 基于智能体探索与试错特性，重构数据系统架构
- 支持高并发、多样化的代理任务，降低冗余开销
- 适合构建自动化数据分析系统的研发人员参考
大型语言模型（LLM）智能体作为用户代理，执行数据操作与分析任务，未来可能成为数据系统的主要负载。这些智能体在完成任务时采用高吞吐量的探索与解决方案生成过程，我们称之为‘智能体推测’。该过程规模大、类型杂、重复多且可引导，对现有数据系统构成挑战。本文指出数据系统需向‘代理优先’演进，并基于智能体推测的四大特征——规模性、异构性、冗余性和可引导性——提出一系列新研究方向，涵盖新型查询接口、查询处理技术及智能体记忆存储机制。
原文摘要 · Abstract (English)
Large 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 for a new agent-first data systems architecture, ranging from new query interfaces, to new query processing techniques, to new agentic memory stores.
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