# DBA-Bench: A Production-Fidelity Benchmark for LLM-Based Database Operations Agents

arXiv: https://arxiv.org/abs/2607.22165 · HTML: https://mlsanity.com/paper/2607.22165/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use.
- Builds on 7 of its 38 references that are in the map.
- First posted on arXiv 24 Jul 2026.

## Placed in

- LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/)

## Builds on (most cited first, 7 of 7)

- [GAIA: a benchmark for General AI Assistants](https://mlsanity.com/paper/2311.12983/) (2023)
- [InterCode: Standardizing and Benchmarking Interactive Coding with Execution Feedback](https://mlsanity.com/paper/2306.14898/) (2023)
- [Recommending Root-Cause and Mitigation Steps for Cloud Incidents using Large Language Models](https://mlsanity.com/paper/2301.03797/) (2023)
- [GPTuner: A Manual-Reading Database Tuning System via GPT-Guided Bayesian Optimization](https://mlsanity.com/paper/2311.03157/) (2023)
- [Automatic Database Configuration Debugging using Retrieval-Augmented Language Models](https://mlsanity.com/paper/2412.07548/) (2024)
- [DBAIOps: A Reasoning LLM-Enhanced Database Operation and Maintenance System using Knowledge Graphs](https://mlsanity.com/paper/2508.01136/) (2025)
- [RCRank: Multimodal Ranking of Root Causes of Slow Queries in Cloud Database Systems](https://mlsanity.com/paper/2503.04252/) (2025)

## Built on by (most cited first, 1 of 1)

- [Incident-Arena: Getting agents to the last nine of reliability](https://mlsanity.com/paper/2610.00648/) (2026)

## Abstract (arXiv)

LLM-based database agents show promise, but differences in task scope, testbeds, and success criteria hinder direct comparison. A shared benchmark must also preserve the operational conditions that determine whether diagnosis and remediation succeed in practice. We present DBA-Bench, guided by operational realism, outcome-first evaluation, and controlled scenario reproducibility. It evaluates agents in live PostgreSQL and distributed TiDB deployments with active workloads, stateful behavior, and multi-source observations. Success requires satisfying scenario-specific operational objectives without violating safety constraints. The primary evaluation covers 231 bounded scenarios across five operational domains, while 10 persistent scenarios extend evaluation to continued operation across interacting events. We evaluate eight automated baselines, including four frontier foundation models, two open-weight models, and two GPT-5.5-backed database agents. Our evaluation exposes gaps between diagnosis, effective remediation, and safe execution across both database systems. Agents can identify root causes yet fail to restore operational objectives. Even when objectives are restored, the executed actions can violate safety constraints. Persistent scenarios expose a gap between clearing individual blockers and sustaining business service: agents leave competing workloads uncoordinated and previously completed repairs unchecked after subsequent changes.