Automotive Engineering-Centric Agentic AI Workflow Framework
2026-04-09 • Artificial Intelligence
Artificial IntelligenceMultiagent Systems
AI summaryⓘ
The authors describe Agentic Engineering Intelligence (AEI), a system that helps engineers by viewing their complex work as a series of linked steps influenced by past decisions and constraints. AEI combines offline data analysis with online decision-making to support engineers in making better choices across different tools. They also explain how this approach can be seen like a control system, where goals guide actions and tools provide feedback. The paper shows examples from car design and other fields to illustrate how AEI can unify various engineering tasks into one approach.
engineering workflowsdesign optimizationmodel-based systems engineering (MBSE)sequential decision processescontrol theoryreinforcement learningworkflow-state estimationautomotive engineeringaerodynamicsknowledge reuse
Authors
Tong Duy Son, Zhihao Liu, Piero Brigida, Yerlan Akhmetov, Gurudevan Devarajan, Kai Liu, Ajinkya Bhave
Abstract
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.