构建安全可控的智能体数据环境，提升自动化效率与可靠性。
Agentic Data Environments
- 设计智能体运行的主动数据环境，整合文件、API等多源数据
- 通过环境机制增强智能体能力并限制错误后果
- 适合关注智能体安全与系统级自动化的研究者
自主智能体在速度、规模和人力效率上带来显著提升，但其失败可能造成突发且不可逆的成本。智能体自动化的核心挑战在于提升自动化收益的同时，控制失败带来的后果。尽管数据库仍是现代计算的核心，智能体的操作范围已扩展至文件、API、应用及系统状态等更广泛的数据环境。本文将介绍早期关于「智能体数据环境」的研究——即智能体运行的执行基础架构——该架构既能增强智能体能力，又能提供安全保证。这一视角将数据系统从被动的状态存储转变为支持安全、可靠执行的主动底座。
原文摘要 · Abstract (English)
Autonomous agents promise substantial gains in speed, scale, and labor efficiency, but their failures can impose abrupt and often irreversible costs. The central challenge for agentic automation is therefore to increase the benefits of automation while bounding the consequences of failure. While databases remain central to modern computing, agents operate over a broader data environment spanning files, APIs, applications, and system state. In this talk, I will outline early work on Agentic Data Environments -- the execution substrate in which agents operate -- that both amplify agent capabilities and enforce safety guarantees. This perspective reframes data systems from passive stores of state into active substrates for safe, reliable execution.
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