# GALA: Can Graph-Augmented Large Language Model Agentic Workflows Elevate Root Cause Analysis?

arXiv: https://arxiv.org/abs/2508.12472 · HTML: https://mlsanity.com/paper/2508.12472/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 15 of its 60 references that are in the map.
- First posted on arXiv 17 Aug 2025.

## Placed in

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

## Builds on (most cited first, 15 of 15)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation](https://mlsanity.com/paper/1703.09902/) (2017)
- [Causal Inference-Based Root Cause Analysis for Online Service Systems with Intervention Recognition](https://mlsanity.com/paper/2206.05871/) (2022)
- [A Quantitative and Qualitative Evaluation of LLM-Based Explainable Fault Localization](https://mlsanity.com/paper/2308.05487/) (2023)
- [Root Cause Analysis In Microservice Using Neural Granger Causal Discovery](https://mlsanity.com/paper/2402.01140/) (2024)
- [RCAEval: A Benchmark for Root Cause Analysis of Microservice Systems with Telemetry Data](https://mlsanity.com/paper/2412.17015/) (2024)
- [Automated Root Causing of Cloud Incidents using In-Context Learning with GPT-4](https://mlsanity.com/paper/2401.13810/) (2024)
- [CausalRCA: Causal Inference based Precise Fine-grained Root Cause Localization for Microservice Applications](https://mlsanity.com/paper/2209.02500/) (2022)
- [Cloud Atlas: Efficient Fault Localization for Cloud Systems using Language Models and Causal Insight](https://mlsanity.com/paper/2407.08694/) (2024)
- [Think Together and Work Better: Combining Humans' and LLMs' Think-Aloud Outcomes for Effective Text Evaluation](https://mlsanity.com/paper/2409.07355/) (2024)
- [Mining Root Cause Knowledge from Cloud Service Incident Investigations for AIOps](https://mlsanity.com/paper/2204.11598/) (2022)
- [Knowledge-aware Alert Aggregation in Large-scale Cloud Systems: a Hybrid Approach](https://mlsanity.com/paper/2403.06485/) (2024)
- [ESRO: Experience Assisted Service Reliability against Outages](https://mlsanity.com/paper/2309.07230/) (2023)

## Built on by (most cited first, 5 of 5)

- [SREGym: A Live Benchmark for AI SRE Agents with High-Fidelity Failure Scenarios](https://mlsanity.com/paper/2605.07161/) (2026)
- [Think Locally, Explain Globally: Graph-Guided LLM Investigations via Local Reasoning and Belief Propagation](https://mlsanity.com/paper/2601.17915/) (2026)
- [Towards In-Depth Root Cause Localization for Microservices with Multi-Agent Recursion-of-Thought](https://mlsanity.com/paper/2605.14866/) (2026)
- [How Far Can Root Cause Analysis Go on Real-World Telemetry Data?](https://mlsanity.com/paper/2607.13548/) (2026)
- [EviRCA: Decoupling Evidence Extraction from Reasoning for Microservice Root-Cause Analysis](https://mlsanity.com/paper/2609.19825/) (2026)

## Abstract (arXiv)

Root cause analysis (RCA) in microservice systems is challenging, requiring on-call engineers to rapidly diagnose failures across heterogeneous telemetry such as metrics, logs, and traces. Traditional RCA methods often focus on single modalities or merely rank suspect services, falling short of providing actionable diagnostic insights with remediation guidance. This paper introduces GALA, a novel multi-modal framework that combines statistical causal inference with LLM-driven iterative reasoning for enhanced RCA. Evaluated on an open-source benchmark, GALA achieves substantial improvements over state-of-the-art methods of up to 42.22% accuracy. Our novel human-guided LLM evaluation score shows GALA generates significantly more causally sound and actionable diagnostic outputs than existing methods. Through comprehensive experiments and a case study, we show that GALA bridges the gap between automated failure diagnosis and practical incident resolution by providing both accurate root cause identification and human-interpretable remediation guidance.