# mABC: multi-Agent Blockchain-Inspired Collaboration for root cause analysis in micro-services architecture

arXiv: https://arxiv.org/abs/2404.12135 · HTML: https://mlsanity.com/paper/2404.12135/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use.
- Builds on 26 of its 67 references that are in the map.
- First posted on arXiv 18 Apr 2024.

## Placed in

- LLM agents and tool use / [Agent threats, guardrails and governance](https://mlsanity.com/agents/agent-threats-guardrails-and-governance/)

## Builds on (most cited first, 26 of 26)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [PaLM 2 Technical Report](https://mlsanity.com/paper/2305.10403/) (2023)
- [Executable Code Actions Elicit Better LLM Agents](https://mlsanity.com/paper/2402.01030/) (2024)
- [Scaling Laws for Generative Mixed-Modal Language Models](https://mlsanity.com/paper/2301.03728/) (2023)
- [LogBERT: Log Anomaly Detection via BERT](https://mlsanity.com/paper/2103.04475/) (2021)
- [Automatic Root Cause Analysis via Large Language Models for Cloud Incidents](https://mlsanity.com/paper/2305.15778/) (2023)
- [RCAgent: Cloud Root Cause Analysis by Autonomous Agents with Tool-Augmented Large Language Models](https://mlsanity.com/paper/2310.16340/) (2023)
- [xCoT: Cross-lingual Instruction Tuning for Cross-lingual Chain-of-Thought Reasoning](https://mlsanity.com/paper/2401.07037/) (2024)
- [OWL: A Large Language Model for IT Operations](https://mlsanity.com/paper/2309.09298/) (2023)
- [D-Bot: Database Diagnosis System using Large Language Models](https://mlsanity.com/paper/2312.01454/) (2023)
- [GanLM: Encoder-Decoder Pre-training with an Auxiliary Discriminator](https://mlsanity.com/paper/2212.10218/) (2022)
- [Lemur: Log Parsing with Entropy Sampling and Chain-of-Thought Merging](https://mlsanity.com/paper/2402.18205/) (2024)
- [LogFormer: A Pre-train and Tuning Pipeline for Log Anomaly Detection](https://mlsanity.com/paper/2401.04749/) (2024)
- [LogLG: Weakly Supervised Log Anomaly Detection via Log-Event Graph Construction](https://mlsanity.com/paper/2208.10833/) (2022)
- [TransLog: A Unified Transformer-based Framework for Log Anomaly Detection](https://mlsanity.com/paper/2201.00016/) (2021)

## Built on by (most cited first, 26 of 26)

- [Flow-of-Action: SOP Enhanced LLM-Based Multi-Agent System for Root Cause Analysis](https://mlsanity.com/paper/2502.08224/) (2025)
- [AgentFM: Role-Aware Failure Management for Distributed Databases with LLM-Driven Multi-Agents](https://mlsanity.com/paper/2504.06614/) (2025)
- [A Survey of AIOps in the Era of Large Language Models](https://mlsanity.com/paper/2507.12472/) (2025)
- [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)
- [SREGym: A Live Benchmark for AI SRE Agents with High-Fidelity Failure Scenarios](https://mlsanity.com/paper/2605.07161/) (2026)
- [Agentic Memory Enhanced Recursive Reasoning for Root Cause Localization in Microservices](https://mlsanity.com/paper/2601.02732/) (2026)
- [Hypothesize-Then-Verify: Speculative Root Cause Analysis for Microservices with Pathwise Parallelism](https://mlsanity.com/paper/2601.02736/) (2026)
- [The Multi-Agent Fault Localization System Based on Monte Carlo Tree Search Approach](https://mlsanity.com/paper/2507.22800/) (2025)
- [OpsAgent: An Evolving Multi-agent System for Incident Management in Microservices](https://mlsanity.com/paper/2510.24145/) (2025)
- [ECLIPSE: Semantic Entropy-LCS for Cross-Lingual Industrial Log Parsing](https://mlsanity.com/paper/2405.13548/) (2024)
- [XFormParser: A Simple and Effective Multimodal Multilingual Semi-structured Form Parser](https://mlsanity.com/paper/2405.17336/) (2024)
- [ADC: Enhancing Function Calling Via Adversarial Datasets and Code Line-Level Feedback](https://mlsanity.com/paper/2412.17754/) (2024)
- [Graph of States: Solving Abductive Tasks with Large Language Models](https://mlsanity.com/paper/2603.21250/) (2026)
- [LumiMAS: A Comprehensive Framework for Real-Time Monitoring and Enhanced Observability in Multi-Agent Systems](https://mlsanity.com/paper/2508.12412/) (2025)
- [InfiCoEvalChain: A Blockchain-Based Decentralized Framework for Collaborative LLM Evaluation](https://mlsanity.com/paper/2602.08229/) (2026)
- [SiriusHelper: An LLM Agent-Based Operations Assistant for Big Data Platforms](https://mlsanity.com/paper/2605.00043/) (2026)
- [STAR: A Stage-attributed Triage and Repair framework for RCA Agents in Microservices](https://mlsanity.com/paper/2605.15581/) (2026)
- [TopoEvo: A Topology-Aware Self-Evolving Multi-Agent Framework for Root Cause Analysis in Microservices](https://mlsanity.com/paper/2605.15611/) (2026)
- [JustDiag!: A Diagnostic Justification Engine for Accountable Root Cause Analysis](https://mlsanity.com/paper/2606.19407/) (2026)
- [A Multi-Dataset Benchmark for Evaluating LLM Agents in Microservice Failure Diagnosis](https://mlsanity.com/paper/2606.29193/) (2026)
- [GALA: Graph-Augmented LLM Agents for Root Cause Analysis and Incident Response in Microservices](https://mlsanity.com/paper/2608.08968/) (2026)
- [Auditable Graph-Guided Root Cause Analysis for Kubernetes Incidents](https://mlsanity.com/paper/2606.08590/) (2026)
- [Beyond Fault Localization: A Trajectory-Level Study of LLM Agents for Microservice Root Cause Analysis](https://mlsanity.com/paper/2608.21310/) (2026)
- [From General Agents to RCA Experts: A Self-Evolving Harness for Root Cause Analysis](https://mlsanity.com/paper/2608.25661/) (2026)
- [ASTRA - Agentic System for Ticket Resolution and Analysis](https://mlsanity.com/paper/2608.28790/) (2026)

## Abstract (arXiv)

Root cause analysis (RCA) in Micro-services architecture (MSA) with escalating complexity encounters complex challenges in maintaining system stability and efficiency due to fault propagation and circular dependencies among nodes. Diverse root cause analysis faults require multi-agents with diverse expertise. To mitigate the hallucination problem of large language models (LLMs), we design blockchain-inspired voting to ensure the reliability of the analysis by using a decentralized decision-making process. To avoid non-terminating loops led by common circular dependency in MSA, we objectively limit steps and standardize task processing through Agent Workflow. We propose a pioneering framework, multi-Agent Blockchain-inspired Collaboration for root cause analysis in micro-services architecture (mABC), where multiple agents based on the powerful LLMs follow Agent Workflow and collaborate in blockchain-inspired voting. Specifically, seven specialized agents derived from Agent Workflow each provide valuable insights towards root cause analysis based on their expertise and the intrinsic software knowledge of LLMs collaborating within a decentralized chain. Our experiments on the AIOps challenge dataset and a newly created Train-Ticket dataset demonstrate superior performance in identifying root causes and generating effective resolutions. The ablation study further highlights Agent Workflow, multi-agent, and blockchain-inspired voting is crucial for achieving optimal performance. mABC offers a comprehensive automated root cause analysis and resolution in micro-services architecture and significantly improves the IT Operation domain. The code and dataset are in https://github.com/zwpride/mABC.