Automotive Engineering-Centric Agentic AI Workflow Framework

TL;DR

This paper introduces AEI, a framework modeling engineering workflows as constrained decision processes supported by AI agents, demonstrated through automotive design use cases.

Contribution

It presents a novel AI workflow framework linking offline data processing with online decision support for engineering tasks.

Findings

AEI effectively models diverse automotive engineering workflows.

The framework supports both offline data processing and online decision-making.

Automotive use cases demonstrate AEI's versatility and practical relevance.

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.