ROMAS: A Role-Based Multi-Agent System for Database monitoring and Planning
Yi Huang, Fangyin Cheng, Fan Zhou, Jiahui Li, Jian Gong, Hongjun Yang,, Zhidong Fan, Caigao Jiang, Siqiao Xue, Faqiang Chen

TL;DR
ROMAS introduces a role-based multi-agent system that enhances database monitoring and planning by enabling adaptive, collaborative, and low-code solutions, demonstrated through real-world deployment and experimental validation.
Contribution
This paper presents ROMAS, a novel role-based multi-agent system that improves adaptability, collaboration, and ease of deployment in database analytics tasks, addressing limitations of existing MAS.
Findings
ROMAS outperforms existing systems in multiple scenarios.
Effective integration with LLM-powered database analytics.
Demonstrated practical utility in real-world deployments.
Abstract
In recent years, Large Language Models (LLMs) have demonstrated remarkable capabilities in data analytics when integrated with Multi-Agent Systems (MAS). However, these systems often struggle with complex tasks that involve diverse functional requirements and intricate data processing challenges, necessitating customized solutions that lack broad applicability. Furthermore, current MAS fail to emulate essential human-like traits such as self-planning, self-monitoring, and collaborative work in dynamic environments, leading to inefficiencies and resource wastage. To address these limitations, we propose ROMAS, a novel Role-Based M ulti-A gent System designed to adapt to various scenarios while enabling low code development and one-click deployment. ROMAS has been effectively deployed in DB-GPT [Xue et al., 2023a, 2024b], a well-known project utilizing LLM-powered database analytics,…
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Taxonomy
TopicsAdvanced Database Systems and Queries · Multi-Agent Systems and Negotiation · Service-Oriented Architecture and Web Services
MethodsMixing Adam and SGD
