LLM Agent Enhances Oil Well Anomaly Detection Explainability

Open-World Learning (OWL) pipelines are effective for detecting anomalies in oil wells, identifying "what happened" through autoencoders and classification. However, these systems often lack the ability to explain "why" an anomaly was detected or "what an operator should do next," and they struggle to assign human-readable names to newly discovered anomaly clusters. To address this explainability gap, researchers have developed an LLM agent layer designed to work alongside existing OWL pipelines. This agent, utilizing a Mixture-of-Experts model, receives structured sensor data and upstream anomaly assertions. It then generates natural-language justifications for decisions, provides confidence-ranked critiques, and assigns consolidated, human-readable names to novel anomaly clusters. Evaluations on a real-world dataset demonstrated the agent's effectiveness in confirming upstream decisions, justifying them with sensor-grounded language, flagging implausible labels, and naming novelties. This companion layer aims to make OWL pipelines more auditable and deployable in operational settings by providing crucial context and actionable insights to engineers.