| name | agentic-data-environments |
| description | 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 th. Based on arXiv:2607.07397. |
Agentic Data Environments
arXiv: 2607.07397 | Authors: Elaine Ang, Chenxi Huang, Georgios Liargkovas, Jerry Liu, Jinhui Liu et al. | Utility: 0.88
Overview
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.
Key Contributions
- 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.
Implementation Notes
- Keywords: agentic
- Categories: cs.AI, cs.DB
- Published: 2026-07-08
Activation Criteria
Use this skill when working on tasks involving: agentic.
