Failure localization in microservice environments is increasingly challenging. While large language models (LLMs) have shown promise in software engineering tasks, existing approaches struggle to effectively integrate multi-modal telemetry data (e.g., log, metric and trace) and provide interpretable results. This paper presents LocaleXpert, a novel failure localization system that combines specialized LLM-based agents with traditional AIOps methods to diagnose issues in microservice environments. LocaleXpert introduces three key innovations: (1) a modular pipeline that transforms metrics, logs, and traces into natural language descriptions that LLMs can effectively process, (2) specialized expert agents that analyze each data type and collaborate to identify root causes, and (3) an interpretation mechanism that produces clear, actionable explanations of its reasoning process. Evaluation results show that LocaleXpert significantly outperforming baseline approaches in both accuracy and interpretability. The system has been successfully deployed in Microsoft's AIOpsLab benchmark, demonstrating its effectiveness.

Log-based fault root cause analysis (RCA) is paramount for ensuring the reliability of large-scale software systems. While small language model (SLM)-based methods offer efficiency and ease of deployment, their limited generalization across diverse fault scenarios often hinders their effectiveness. Conversely, large language model (LLM)-based methods demonstrate strong semantic understanding but can suffer from inaccuracies and hallucinations due to a lack of domain-specific knowledge. To overcome these limitations, we present AetherLog, a novel RCA framework synergistically integrating LLMs with knowledge graphs (KGs). In an offline phase, AetherLog employs LLMs to extract fault-relevant entities and relations, constructing a compact and semantically aligned KG through embedding-based clustering and normalization. During online analysis, the framework leverages an LLM to summarize fault logs and extract pertinent entities. Subsequently, it retrieves semantically similar entities from the KG to enrich the context and formulates context-enhanced prompts, leading to more accurate RCA. Extensive experiments conducted on two real-world datasets demonstrate that AetherLog consistently surpasses state-of-the-art baselines, achieving F1-scores of 0.93 and 0.97. These results represent significant improvements of 6% and 8% over the best existing methods, respectively, demonstrating AetherLog’s effectiveness and generalizability in log-based fault RCA.

In large-scale enterprises, on-call engineers (OCEs) are critical for ensuring service availability and reliability. However, as incidents grow in volume and complexity, traditional manual on-call processes are becoming increasingly inadequate. Recent advances in large language models (LLMs) have demonstrated remarkable capabilities in reasoning and multi-agent collaboration, presenting new opportunities for automation. We propose OncallX, an end-to-end automated on-call system designed for real-world industrial scenarios that integrates LLMs with multi-agent cooperation to enable intelligent and efficient incident management. OncallX first enhances user queries by leveraging external knowledge bases and multi-turn dialogue interactions. Subsequently, multiple expert agents collaborate through tree-search-based mechanisms to generate effective responses and solutions. When incidents cannot be resolved automatically, OncallX accurately assigns them to the most appropriate teams. Comprehensive experiments conducted in the real-world production environment of a top-tier global online video service provider demonstrate that OncallX efficiently responds to incidents and accurately triages tickets, significantly outperforming existing methods in both automated metrics and human evaluations. Furthermore, OncallX has been successfully deployed in production for two months, during which it has substantially enhanced on-call efficiency, reducing average incident response time to just 21 seconds and average triage time to 4 seconds—representing a transformative improvement in operational excellence.