Large language model (LLM) agents have shown promise in automating complex data-analysis workflows, but their reliable deployment remains challenging in high-stakes industrial scenarios. Industrial anomaly detection (IAD) is essential for manufacturing quality, safety, and efficiency, yet existing LLM-based IAD agents mainly focus on execution while under-exploiting strategy formulation. Consequently, they struggle to handle heterogeneous modalities in a unified and cost-effective manner. Inspired by the DMAIC quality-management framework, we propose DMAIC-IAD (DMAIC-inspired Agentic Industrial Anomaly Detection), a "Plan First, Judge Later" multi-agent system that aligns LLM agents with structured industrial problem-solving. DMAIC-IAD distills heterogeneous references into standardized operating procedures (SOPs) before strategy generation, and introduces a pre-trained execution-free judge model to rank candidate strategies without costly runtime trials. Extensive experiments across four modalities show that DMAIC-IAD improves average detection performance over applicable agentic baselines by 37.76%.

翻译：大语言模型（LLM）智能体在自动化复杂数据分析工作流方面展现出潜力，但其在高风险工业场景中的可靠部署仍面临挑战。工业异常检测（IAD）对制造质量、安全性和效率至关重要，然而现有基于LLM的IAD智能体主要聚焦于执行层面，对策略制定的开发不足，因而难以以统一且经济高效的方式处理异构模态数据。受DMAIC质量管理框架启发，我们提出DMAIC-IAD（基于DMAIC理念的工业异常检测智能体系统），这是一种"先计划，后评判"的多智能体系统，能将LLM智能体与结构化工业问题求解流程对齐。DMAIC-IAD在策略生成前将异构参考信息提炼为标准操作程序（SOP），并引入预训练的无执行评判模型，无需代价高昂的运行中试验即可对候选策略进行排序。跨四种模态的大量实验表明，与可应用的智能体基线方法相比，DMAIC-IAD将平均检测性能提升了37.76%。