Agentic Root Cause Analysis through Evidence-Grounded Reasoning

2026-07-24

AI Summary

Industrial root cause diagnosis typically relies on manual hypotheses and extensive fault labels, yet existing data-driven methods suffer from poor interpretability and limited generalization. This work proposes AgentRCA, a novel framework that achieves zero-shot, label-free root cause diagnosis for the first time. By integrating data-driven digital twins with tool-augmented large language models, AgentRCA adopts a hypothesis-driven approach to iteratively gather statistical evidence, evaluate competing hypotheses, and construct transparent reasoning chains that explicitly link observed symptoms to underlying physical faults. Evaluated on real-world multiphase flow facilities and large-scale chemical plants, the framework matches the diagnostic performance of fully supervised baselines while offering high interpretability.

Abstract

Diagnosing the root cause of anomalies is essential for safe industrial operation. Despite extensive sensor instrumentation, formulating hypotheses and gathering evidence remains a manual process, creating a major operational bottleneck. While existing data-driven approaches aim to automate this, two critical limitations restrict their deployment: their operate as black boxes unable to justify their diagnosis, and they require scarce labeled examples of faulty operation. To address this gap, we introduce AgentRCA, a zero-shot agentic framework for evidence-grounded root cause analysis. Rather than learning fault-specific mappings, AgentRCA performs inference-time reasoning by combining a data-driven digital twin (modeling normal system dynamics) with a tool-augmented large language model. The agent iteratively gathers statistical evidence, evaluates competing hypotheses, and identifies the physical fault that best explains the observed behavior. Evaluated on a real-world multiphase-flow facility and a large-scale chemical plant, AgentRCA achieves diagnostic performance competitive with fully supervised baselines without relying on fault-specific training. Crucially, it produces transparent reasoning traces that explicitly link observed symptoms to their underlying physical causes. These results establish autonomous hypothesis-driven reasoning as a practical foundation for scalable industrial root cause analysis.

Problem

• root cause analysis
• anomaly diagnosis
• industrial operation
• explainable AI
• labeled data scarcity

Innovation

• zero-shot
• agentic reasoning
• evidence-grounded diagnosis
• digital twin
• root cause analysis