DBA-Bench: A Production-Fidelity Benchmark for LLM-Based Database Operations Agents
LLM-based database agents show promise, but differing task scopes, testbeds, and metrics hinder comparison. We identify four gaps between evaluation and production operations: live-environment fidelity (multi-turn read-write interaction with a running database); observation-space scale and complexity (causal diagnosis across thousands of time series, business logs, and concurrent activity); solution-space openness (multiple remediations with different operational trade-offs); and scenario comple
Record details
Published: 24 July 2026
Source: arXiv cs.AI
Category: Research
Topics: Healthcare · Agents & autonomy · Environment
Retrieved: 27 July 2026
Related evidence
These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.
Clinical Pathways as Safety Specifications for Physical AI in Hospital Wards
arXiv cs.CY · 23 July 2026
Benchmarking the Safety of Large Language Models for Robotic Health Attendant Control
arXiv cs.CY · 27 July 2026
SR-Agent: An Experience-Driven Agentic Framework for Post-Ranking Strategies Refinement in E-Commerce Recommendation
arXiv · 20 July 2026
Change2Task: From Repository Changes to Executable Coding Agent Tasks and Environments
arXiv cs.LG · 30 July 2026
The Path to Self-Evolving Clinical Systems: Scaling Medical Agents from Assistance to Autonomy
arXiv · 13 July 2026
ResidencyRL: Reinforcement Learning in Simulated Clinical Environments
arXiv · 7 August 2026
How to cite this record
ethics.ai (24 July 2026), “DBA-Bench: A Production-Fidelity Benchmark for LLM-Based Database Operations Agents,” evidence record 13707, https://ethics.ai/record/13707 (originally published by arXiv cs.AI).
Use and limitations
This page is a stable index and citation surface for a source record. ethics.ai did not author the underlying report and has not independently verified every claim. Automatic topics may be imperfect. For consequential use, quote and cite the original publisher.