With the development of distributed systems, microservices and cloud native technologies have become central to modern enterprise software development. Despite bringing significant advantages, these technologies also increase system complexity and operational challenges. Traditional root cause analysis (RCA) struggles to achieve automated fault response, heavily relying on manual intervention. In recent years, large language models (LLMs) have made breakthroughs in contextual inference and domain knowledge integration, providing new solutions for Artificial Intelligence for Operations (AIOps). However, Existing LLM-based approaches face three key challenges: text input constraints, dynamic service dependency hallucinations, and context window limitations. To address these issues, we propose a tool-assisted LLM agent with multi-modality observation data, namely TAMO, for fine-grained RCA. It unifies multi-modal observational data into time-aligned representations to extract consistent features and employs specialized root cause localization and fault classification tools for perceiving the contextual environment. This approach overcomes the limitations of LLM in handling real-time changing service dependencies and raw observational data and guides LLM to generate repair strategies aligned with system contexts by structuring key information into a prompt. Experimental results show that TAMO performs well in root cause analysis when dealing with public datasets characterized by heterogeneity and common fault types, demonstrating its effectiveness.

本研究解决了传统根本原因分析在自动故障响应中的局限性，通过提出TAMO，一个利用多模态观察数据的工具辅助LLM智能体，来实现细粒度的根本原因分析。TAMO通过统一多模态观察数据为时间对齐的表示，并结合专业的根本原因定位与故障分类工具，显著提高了在处理动态服务依赖和实时观测数据的能力，实验结果显示其在异构公共数据集上表现卓越。

TAMO：基于工具辅助的LLM智能体的细粒度根本原因分析与多模态观察数据