Emergent Failure Prediction in Multi-Agent AI Systems: A Causal Graph Framework for Interaction Anomaly Detection, Cascade Propagation, and Silent Degradation Analysis

Abstract

Multi-agent AI systems often fail through interaction instability rather than through a single visible component fault, which makes early diagnosis difficult when coordination breakdown, cascade propagation, and silent degradation develop gradually across the agent network. Existing work on anomaly detection, graph-based monitoring, and causal reasoning improves visibility into distributed system behavior, but practical frameworks for predicting emergent failure in multi-agent AI systems remain limited. This article presents a causal graph framework that models agent interactions as dependency pathways, detects anomalous coordination patterns, traces cascade propagation, and identifies silent degradation before overt system breakdown occurs. The results show that the framework maintains strong interaction anomaly detectability, high causal path confidence, meaningful silent degradation visibility, robust cascade sensitivity, accurate emergent failure prediction, and useful intervention lead time across diverse multi-agent breakdown scenarios. The study shows that causal interaction modeling can provide a practical foundation for early and interpretable failure prediction in multi-agent AI systems.