# LATS-RCA: Language Agent Tree Search for Root Cause Analysis in Microservices

arXiv: https://arxiv.org/abs/2605.03505 · HTML: https://mlsanity.com/paper/2605.03505/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 14 of its 22 references that are in the map.
- First posted on arXiv 5 May 2026.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 14 of 14)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models](https://mlsanity.com/paper/2310.04406/) (2023)
- [Token-Budget-Aware LLM Reasoning](https://mlsanity.com/paper/2412.18547/) (2024)
- [RCAgent: Cloud Root Cause Analysis by Autonomous Agents with Tool-Augmented Large Language Models](https://mlsanity.com/paper/2310.16340/) (2023)
- [Robust Failure Diagnosis of Microservice System through Multimodal Data](https://mlsanity.com/paper/2302.10512/) (2023)
- [CausalRCA: Causal Inference based Precise Fine-grained Root Cause Localization for Microservice Applications](https://mlsanity.com/paper/2209.02500/) (2022)
- [Constructing Large-Scale Real-World Benchmark Datasets for AIOps](https://mlsanity.com/paper/2208.03938/) (2022)
- [A Comprehensive Survey on Root Cause Analysis in (Micro) Services: Methodologies, Challenges, and Trends](https://mlsanity.com/paper/2408.00803/) (2024)
- [Rethinking the Evaluation of Microservice RCA with a Fault Propagation-Aware Benchmark](https://mlsanity.com/paper/2510.04711/) (2025)
- [TraceDiag: Adaptive, Interpretable, and Efficient Root Cause Analysis on Large-Scale Microservice Systems](https://mlsanity.com/paper/2310.18740/) (2023)
- [Cross-System Software Log-based Anomaly Detection Using Meta-Learning](https://mlsanity.com/paper/2412.15445/) (2024)
- [LO2: Microservice API Anomaly Dataset of Logs and Metrics](https://mlsanity.com/paper/2504.12067/) (2025)
- [AnoMod: A Dataset for Anomaly Detection and Root Cause Analysis in Microservice Systems](https://mlsanity.com/paper/2601.22881/) (2026)
- [Cross-System Categorization of Abnormal Traces in Microservice-Based Systems via Meta-Learning](https://mlsanity.com/paper/2403.18998/) (2024)

## Abstract (arXiv)

Recent advances in large language models (LLMs) have enabled early attempts to automate root cause analysis (RCA) in microservice systems (MSS). However, existing approaches typically rely on a linear reasoning process that proceeds along a single diagnostic path. In this paper, we propose the Language Agent Tree Search for RCA (LATS-RCA) in MSS. LATS-RCA formulates RCA as a reflection-guided tree-structured search over root-cause hypotheses, where multiple agents iteratively analyze logs and metrics to collect evidence, and reflection scores guide the search toward the most likely root causes for abnormal services. We evaluate LATS-RCA on the open benchmark (LO2), achieving 91.3% diagnostic accuracy and analyzing the associated computational cost. Variation among the frontier-tier LLMs (Claude Sonnet 4.5, GPT-5, and Gemini 3 Pro) is small, between 89.7% and 91.3%, demonstrating our approach is model-agnostic. We also conduct an exploratory study by evaluating LATS-RCA on real-world incidents from a web-hosting company's (Zoner Oy) production MSS that serves over 300,000 websites across Europe. We find that LATS-RCA correctly diagnoses 65.1% of the production incidents on average over multiple runs. This reveals key challenges of real-world RCA, including multi-factor root causes, large-scale system complexity, and incomplete observability, which are absent from open benchmarks. Future work should develop more realistic open datasets for RCA and validate LATS-RCA with additional datasets. Our replication package is available at https://github.com/kottinov/lats-rca.