ASE 2026 Sustainable AIOps

OpsAgent: An Evolving Multi-agent System for Incident Management in Microservices

Yu Luo¹, Jiamin Jiang¹, Jingfei Feng¹, Lei Tao¹, Qingliang Zhang¹, Xidao Wen², Yongqian Sun^1,*, Shenglin Zhang¹, Tong Liu³, Wenjie Zhang³, Dan Pei⁴

¹Nankai University ²Alibaba Cloud ³Lenovo ⁴Tsinghua University

ASE 2026

41st IEEE/ACM International Conference on Automated Software Engineering, Munich, Germany

* Corresponding author

Paper Code Story

OpsAgent teaser figure — OpsAgent turns a lightweight LLM into a deployable and sustainable incident management system through a training-free data processor, multi-agent collaboration, and self-evolution.

01 THE PAPER

Abstract

Incident management (IM) is central to the reliability of large-scale microservice systems. Yet manual IM, where on-call engineers examine metrics, logs, and traces, is labor-intensive and error-prone in the face of massive and heterogeneous observability data. Existing automated IM approaches often struggle to generalize across systems, provide limited interpretability, and incur high deployment costs, which hinders adoption in practice. In this paper, we present OpsAgent, a lightweight, self-evolving multi-agent system for IM that employs a training-free data processor to convert heterogeneous observability data into structured textual descriptions, along with a multi-agent collaboration framework that makes diagnostic inference transparent and auditable. To support continual capability growth, OpsAgent also introduces a dual self-evolution mechanism that integrates internal model updates with external experience accumulation, thereby closing the deployment loop. Comprehensive experiments on the OPENRCA benchmark demonstrate state-of-the-art performance and show that OpsAgent is generalizable, interpretable, cost-efficient, and self-evolving, making it a practically deployable and sustainable solution for long-term operation in real-world microservice systems. Notably, its deployment in Lenovo's production environment further validates its effectiveness in real-world industrial settings.

Project Overview

Training-Free Data Processor

OpsAgent first converts metrics, logs, and traces into structured textual descriptions without system-specific training, preserving useful anomaly, log, and latency evidence while keeping deployment cost low.

Auditable Multi-Agent Diagnosis

An intent interpreter, orchestrator, and specialized agents cooperate through agent profiles and cross-review, turning root-cause diagnosis into a transparent workflow that OCEs can inspect and act on.

Dual Self-Evolution Loop

The system improves through internal PPO-based optimization and external reflection-based experience accumulation, closing the loop between online incident handling and offline capability growth.

02 THE FRAMEWORK

Method

Training-Free Data Processor: Metrics, logs, and traces are processed separately into compact diagnostic evidence without retraining a system-specific model.

Illustrative example of textual data descriptions

Textual Descriptions: Observability signals are converted into structured descriptions that preserve anomaly rankings, failure-indicative logs, high-latency spans, and call-path statistics.

Multi-Agent Collaboration: An intent interpreter, orchestrator, and task-specific agents cooperate through profiles, cross-review, and root-cause reports.

Self-Evolution Mechanism: OpsAgent combines PPO-based internal optimization with reflection-based reusable knowledge construction and retrieval.

03 EVALUATION

Experimental Results

Table 1: Comparison with baselines across seed LLMs

Overall Performance: OpsAgent achieves state-of-the-art diagnosis quality across seed LLMs on OPENRCA while keeping inference cost practical.

Table 2: Ablation study on key components

Ablation Study: Removing the data processor, cross-review, reflection, or PPO weakens diagnosis quality, showing that each component contributes to the full system.

Table 3: Self-evolution capability under different training budgets

Self-Evolution Budget: Increasing the proportion of self-evolution training cases improves average Correct and Partial scores.

Deployment Case: The case study illustrates how OpsAgent turns incident tickets and observability evidence into actionable root-cause reports for OCEs.

Figure 7: Interpretability assessment in deployment

Interpretability Assessment: Frontline OCEs rate OpsAgent highly on consistency, clarity, relevance, and rationality.

04 CITE THIS WORK

BibTeX

@article{luo2025observability,
  title={From Observability Data to Diagnosis: An Evolving Multi-agent System for Incident Management in Cloud Systems},
  author={Luo, Yu and Jiang, Jiamin and Feng, Jingfei and Tao, Lei and Zhang, Qingliang and Wen, Xidao and Sun, Yongqian and Zhang, Shenglin and Pei, Dan},
  journal={arXiv preprint arXiv:2510.24145},
  year={2025}
}