Engineering analysis automation in product development relies on rigid interfaces between tools, data formats and documented processes. When these interfaces change, as they routinely do as the product evolves in the engineering ecosystem, the automation support breaks. This paper presents a DUCTILE (Delegated, User-supervised Coordination of Tool- and document-Integrated LLM-Enabled) agentic orchestration, an approach for developing, executing and evaluating LLM-based agentic automation support of engineering analysis tasks. The approach separates adaptive orchestration, performed by the LLM agent, from deterministic execution, performed by verified engineering tools. The agent interprets documented design practices, inspects input data and adapts the processing path, while the engineer supervises and exercises final judgment. DUCTILE is demonstrated on an industrial structural analysis task at an aerospace manufacturer, where the agent handled input deviations in format, units, naming conventions and methodology that would break traditional scripted pipelines. Evaluation against expert-defined acceptance criteria and deployment with practicing engineers confirm that the approach produces correct, methodologically compliant results across 10 repeated independent runs. The paper discusses the paradigm shift and the practical consequences of adopting agentic automation, including unintended effects on the nature of engineering work when removing mundane tasks and creating an exhausting supervisory role.

翻译：产品开发中的工程分析自动化依赖于工具、数据格式和文档化流程之间的刚性接口。当这些接口发生变化时（这在工程生态系统中随着产品演化而常规发生），自动化支持便会失效。本文提出DUCTILE（委托式、用户监督的工具与文档集成LLM智能编排）智能编排方法，这是一种用于开发、执行和评估基于LLM的工程分析任务智能自动化支持的方法。该方法将由LLM智能体执行的适应性编排与经验证的工程工具执行的确定性执行相分离。智能体通过解读文档化的设计实践、检查输入数据并调整处理路径来运作，而工程师则负责监督并行使最终判断权。DUCTILE在航空航天制造商的工业结构分析任务中得到验证，该智能体成功处理了输入数据在格式、单位、命名惯例和方法论上的偏差——这些偏差足以导致传统的脚本化流程失效。基于专家定义的验收标准进行的评估，以及与执业工程师的实际部署测试均证实，该方法在10次独立重复运行中均能产生正确且符合方法论要求的结果。本文讨论了采用智能自动化所带来的范式转变与实际影响，包括在消除繁琐任务的同时可能对工程工作性质产生的意外效应，例如可能形成令人疲惫的监督角色。