TopoEvo: A Topology-Aware Self-Evolving Multi-Agent Framework for Root Cause Analysis in Microservices

2024-11-20 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Cloud Computing & IT Infrastructure · Depth: Expert, extended

Summary

TopoEvo is a topology-aware, self-evolving multi-agent framework designed for root cause analysis (RCA) in microservices, addressing challenges like noisy multimodal observability, cascading failures, and non-stationary topology drift. The framework introduces Metric-orthogonal Multimodal Alignment (MOMA) to decompose metric embeddings and align logs/traces, reducing redundancy and sparsity for stable node representations. It then uses Vector Quantization (VQ) to discretize topology-enhanced states into auditable "symptom tokens" with a symptom lexicon, enabling reliable retrieval and evidence grounding. TopoEvo employs a multi-agent Hypothesis–Evidence–Test (HET) workflow to verify propagation-consistent explanations and distinguish initiating anomalies from amplified downstream symptoms. Finally, a Self-Evolving Mechanism refreshes hierarchical incident memory and performs conservative test-time adaptation using high-confidence pseudo-labels to maintain robustness under drift. Evaluated on a public AIOps benchmark and a real-world production dataset, TopoEvo achieved up to 3.44% improvement in root cause localization accuracy and 4.39% to 16.81% in fault-type classification performance compared to state-of-the-art baselines.

Key takeaway

For research scientists developing advanced AIOps solutions, TopoEvo demonstrates that integrating topology-aware representation learning with structured multi-agent reasoning significantly enhances root cause localization and fault classification in dynamic microservice environments. You should consider adopting similar mechanisms for multimodal alignment, symptom tokenization, and self-evolving adaptation to build more robust and explainable diagnostic systems, particularly when dealing with symptom amplification bias and topology drift.

Key insights

Topology-aware, self-evolving multi-agent frameworks improve microservice root cause analysis by integrating multimodal data and structured reasoning.

Principles

• Explicitly model fault propagation paths.
• Discretize complex states into auditable tokens.
• Adapt to system drift with continuous learning.

Method

TopoEvo preprocesses multimodal data, constructs a fine-grained dependency graph, performs metric-orthogonal multimodal alignment, discretizes states via VQ into symptom tokens, and uses a multi-agent HET workflow with a self-evolving mechanism.

In practice

• Use VQ to create interpretable "symptom tokens" for reasoning.
• Implement a Hypothesis–Evidence–Test loop for causal verification.
• Employ orthogonal regularization for multimodal data alignment.

Topics

• Microservice Root Cause Analysis
• Multi-Agent Frameworks
• Topology-Aware Reasoning
• Multimodal Observability
• Vector Quantization

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, MLOps Engineer

Related on AIssential

• TopoEvo: A Topology-Aware Self-Evolving Multi-Agent Framework for Root Cause Analysis in Microservices — TopoEvo uses topology-aware multi-agent reasoning to accurately identify root causes in dynamic microservice environments.
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• How Netflix Maps Thousands of Microservices in Real-Time — Unifying diverse data sources into a real-time, queryable service topology graph enhances incident resolution.
• LATS-RCA: Language Agent Tree Search for Root Cause Analysis in Microservices — LATS-RCA uses LLM-driven multi-agent tree search with reflection to diagnose microservice anomalies.

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.