Agentic Anomaly Detection for Shipping

Abstract

Operational decision making in the shipping industry exemplifies a real-world challenge that extends beyond single tasks and static conditions. We introduce an agentic LLM system designed to enhance anomaly detection (AD) and maintenance processes within this highly dynamic domain, involving multi-persona stakeholder interactions. The method leverages the intrinsic knowledge and reasoning abilities of LLMs, augmented by a suite of external tools to reason on the severity of anomalies detected by an out-of-the-box AD tool. Our approach achieves this by considering environmental factors, interconnected system dynamics extracted from a knowledge graph, and broader operational parameters. Evaluations on large-scale shipping data demonstrate that our method effectively reasons about multimodal data, distilling complex system dynamics into operational insights. This represents the first agentic application in an open-world maritime environment.