TopoEvo: A Topology-Aware Self-Evolving Multi-Agent Framework for Root Cause Analysis in Microservices
The article introduces TopoEvo, a new framework designed for root cause analysis in microservices. This framework addresses challenges such as noisy observability and cascading failures by utilizing topology-aware reasoning. TopoEvo employs advanced techniques like Metric-orthogonal Multimodal Alignment and a Self-Evolving Mechanism to enhance the reliability of root cause identification.
- ▪TopoEvo is a topology-aware self-evolving multi-agent framework for root cause analysis in microservices.
- ▪The framework tackles issues like noisy multimodal observability and cascading failure propagation.
- ▪It incorporates techniques such as Metric-orthogonal Multimodal Alignment and a Hypothesis--Evidence--Test workflow.
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Computer Science > Artificial Intelligence arXiv:2605.15611 (cs) [Submitted on 15 May 2026] Title:TopoEvo: A Topology-Aware Self-Evolving Multi-Agent Framework for Root Cause Analysis in Microservices Authors:Junle Wang, Xingchuang Liao, Wenjun Wu View a PDF of the paper titled TopoEvo: A Topology-Aware Self-Evolving Multi-Agent Framework for Root Cause Analysis in Microservices, by Junle Wang and 2 other authors View PDF HTML (experimental) Abstract:Root cause analysis (RCA) in microservices is challenging due to (i) noisy and heterogeneous multimodal observability (metrics, logs, traces), (ii) cascading failure propagation that amplifies downstream symptoms, and (iii) non-stationary topology drift induced by autoscaling and rolling updates.
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