Agentic Root Cause Analysis through Evidence-Grounded Reasoning

• URL: http://arxiv.org/abs/2607.22385v1
• Date: Fri, 24 Jul 2026 15:10:41 GMT
• Title: Agentic Root Cause Analysis through Evidence-Grounded Reasoning
• Abstract summary: AgentRCA is a zero-shot agentic framework for evidence-grounded root cause analysis.<n>It performs inference-time reasoning by combining a data-driven digital twin with a tool-augmented large language model.<n>It achieves diagnostic performance competitive with fully supervised baselines without relying on fault-specific training.
• Score: 16.37572484400141
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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