用AI代理动态协调工程分析，自动应对数据格式变化。
DUCTILE: Agentic LLM Orchestration of Engineering Analysis in Product Development Practice
- AI代理解读规范、调整流程路径，工具负责确定性执行。
- 10次独立测试中均生成合规结果，应对格式、单位等偏差。
- 适合需灵活处理复杂工程任务的团队，尤其在产品迭代中。
产品开发中的工程分析自动化依赖于工具、数据格式和流程文档间的固定接口。当这些接口随产品演化而改变时，自动化便失效。本文提出DUCTILE（委托式、用户监督的工具与文档集成型大模型代理编排）方法，实现基于大模型的代理自动化支持的设计、执行与评估。该方法将适应性编排（由大模型代理完成）与确定性执行（由已验证工程工具完成）分离。代理解析设计规范、检查输入数据并自适应调整处理路径，工程师则进行最终判断与监督。在某航空航天制造商的结构分析任务中，该代理成功处理了格式、单位、命名惯例和方法学上的输入偏差，传统脚本管道在此类情况下会崩溃。经专家定义的验收标准评估及工程师实际部署验证，该方法在10次独立运行中均生成正确且符合方法论的结果。论文还探讨了采用代理自动化带来的范式转变及其对工程工作本质的潜在影响，包括消除重复劳动后产生的高强度监督负担。
原文摘要 · Abstract (English)
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.
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