# Automatic Root Cause Analysis via Large Language Models for Cloud Incidents

arXiv: https://arxiv.org/abs/2305.15778 · HTML: https://mlsanity.com/paper/2305.15778/

## Key facts

- Cited by 84 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2023.

## Built on by (most cited first, 30 of 84)

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- [Flow-of-Action: SOP Enhanced LLM-Based Multi-Agent System for Root Cause Analysis](https://mlsanity.com/paper/2502.08224/) (2025)
- [Automated Root Causing of Cloud Incidents using In-Context Learning with GPT-4](https://mlsanity.com/paper/2401.13810/) (2024)
- [mABC: multi-Agent Blockchain-Inspired Collaboration for root cause analysis in micro-services architecture](https://mlsanity.com/paper/2404.12135/) (2024)
- [CE-CoLLM: Efficient and Adaptive Large Language Models Through Cloud-Edge Collaboration](https://mlsanity.com/paper/2411.02829/) (2024)
- [A Survey on Large Language Models for Communication, Network, and Service Management: Application Insights, Challenges, and Future Directions](https://mlsanity.com/paper/2412.19823/) (2024)
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- [An Empirical Study on LLM-based Agents for Automated Bug Fixing](https://mlsanity.com/paper/2411.10213/) (2024)
- [X-lifecycle Learning for Cloud Incident Management using LLMs](https://mlsanity.com/paper/2404.03662/) (2024)
- [Building AI Agents for Autonomous Clouds: Challenges and Design Principles](https://mlsanity.com/paper/2407.12165/) (2024)
- [A Survey of AIOps in the Era of Large Language Models](https://mlsanity.com/paper/2507.12472/) (2025)
- [Face It Yourselves: An LLM-Based Two-Stage Strategy to Localize Configuration Errors via Logs](https://mlsanity.com/paper/2404.00640/) (2024)
- [LogLM: From Task-based to Instruction-based Automated Log Analysis](https://mlsanity.com/paper/2410.09352/) (2024)
- [Cloud-OpsBench: A Reproducible Benchmark for Agentic Root Cause Analysis in Cloud Systems](https://mlsanity.com/paper/2603.00468/) (2026)
- [TAMO: Fine-Grained Root Cause Analysis via Tool-Assisted LLM Agent with Multi-Modality Observation Data in Cloud-Native Systems](https://mlsanity.com/paper/2504.20462/) (2025)
- [Adaptive Root Cause Localization for Microservice Systems with Multi-Agent Recursion-of-Thought](https://mlsanity.com/paper/2508.20370/) (2025)
- [A Comprehensive Survey on Root Cause Analysis in (Micro) Services: Methodologies, Challenges, and Trends](https://mlsanity.com/paper/2408.00803/) (2024)
- [SREGym: A Live Benchmark for AI SRE Agents with High-Fidelity Failure Scenarios](https://mlsanity.com/paper/2605.07161/) (2026)
- [LEMMA-RCA: A Large Multi-modal Multi-domain Dataset for Root Cause Analysis](https://mlsanity.com/paper/2406.05375/) (2024)
- [NetArena: Dynamic Benchmarks for AI Agents in Network Automation](https://mlsanity.com/paper/2506.03231/) (2025)
- [Cloud Infrastructure Management in the Age of AI Agents](https://mlsanity.com/paper/2506.12270/) (2025)
- [Large Language Models for Security Operations Centers: A Comprehensive Survey](https://mlsanity.com/paper/2509.10858/) (2025)
- [Agentic Memory Enhanced Recursive Reasoning for Root Cause Localization in Microservices](https://mlsanity.com/paper/2601.02732/) (2026)
- [Adapting Large Language Models to Log Analysis with Interpretable Domain Knowledge](https://mlsanity.com/paper/2412.01377/) (2024)
- [The Multi-Agent Fault Localization System Based on Monte Carlo Tree Search Approach](https://mlsanity.com/paper/2507.22800/) (2025)

## Abstract (arXiv)

Ensuring the reliability and availability of cloud services necessitates efficient root cause analysis (RCA) for cloud incidents. Traditional RCA methods, which rely on manual investigations of data sources such as logs and traces, are often laborious, error-prone, and challenging for on-call engineers. In this paper, we introduce RCACopilot, an innovative on-call system empowered by the large language model for automating RCA of cloud incidents. RCACopilot matches incoming incidents to corresponding incident handlers based on their alert types, aggregates the critical runtime diagnostic information, predicts the incident's root cause category, and provides an explanatory narrative. We evaluate RCACopilot using a real-world dataset consisting of a year's worth of incidents from Microsoft. Our evaluation demonstrates that RCACopilot achieves RCA accuracy up to 0.766. Furthermore, the diagnostic information collection component of RCACopilot has been successfully in use at Microsoft for over four years.