用AI代理框架统一汽车工程中的迭代设计流程，实现智能决策支持。
Automotive Engineering-Centric Agentic AI Workflow Framework
- 将工程流程建模为有约束、历史感知的序列决策过程，由AI代理协同工程师干预工具链。
- 在悬架设计、控制调优等5个汽车场景中验证，可统一表达多样化工程工作流。
- 适合工业界研发团队，推动工程AI从任务级迈向流程级智能升级。
设计优化、基于仿真的诊断、控制调参和基于模型的系统工程（MBSE）等工程流程具有迭代性、约束性强且受历史决策影响。然而，许多AI方法仍将其视为孤立任务，而非整体工作流的一部分。本文提出面向工业应用的代理式工程智能（Agentic Engineering Intelligence, AEI）框架，将工程流程建模为有约束、历史感知的序列决策过程，其中AI代理在工程师监督下对工程工具链进行干预。AEI包含离线阶段（工程数据处理与工作流记忆构建）和在线阶段（工作流状态估计、检索与决策支持）。该框架亦可从控制论视角理解：工程目标作为参考信号，代理充当工作流控制器，工具链提供反馈以选择干预策略。在悬架设计、强化学习调优、多模态工程知识复用、气动探索及MBSE等代表性汽车应用场景中，展示了多种工作流如何被统一建模。总体而言，本文将工程AI定位为流程级智能问题，并为工业场景下的未来实证验证提供了可行路线图。
原文摘要 · Abstract (English)
Engineering workflows such as design optimization, simulation-based diagnosis, control tuning, and model-based systems engineering (MBSE) are iterative, constraint-driven, and shaped by prior decisions. Yet many AI methods still treat these activities as isolated tasks rather than as parts of a broader workflow. This paper presents Agentic Engineering Intelligence (AEI), an industrial vision framework that models engineering workflows as constrained, history-aware sequential decision processes in which AI agents support engineer-supervised interventions over engineering toolchains. AEI links an offline phase for engineering data processing and workflow-memory construction with an online phase for workflow-state estimation, retrieval, and decision support. A control-theoretic interpretation is also possible, in which engineering objectives act as reference signals, agents act as workflow controllers, and toolchains provide feedback for intervention selection. Representative automotive use cases in suspension design, reinforcement learning tuning, multimodal engineering knowledge reuse, aerodynamic exploration, and MBSE show how diverse workflows can be expressed within a common formulation. Overall, the paper positions engineering AI as a problem of process-level intelligence and outlines a practical roadmap for future empirical validation in industrial settings.
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