# AI Agents in Engineering Design: A Multi-Agent Framework for Aesthetic and Aerodynamic Car Design

arXiv: https://arxiv.org/abs/2503.23315 · HTML: https://mlsanity.com/paper/2503.23315/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use.
- Builds on 20 of its 44 references that are in the map.
- First posted on arXiv 30 Mar 2025.

## Placed in

- LLM agents and tool use / [Multimodal and embodied agents](https://mlsanity.com/agents/multimodal-and-embodied-agents/)

## Builds on (most cited first, 20 of 20)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation](https://mlsanity.com/paper/1901.05103/) (2019)
- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation](https://mlsanity.com/paper/2308.08155/) (2023)
- [The Rise and Potential of Large Language Model Based Agents: A Survey](https://mlsanity.com/paper/2309.07864/) (2023)
- [DrivAerNet++: A Large-Scale Multimodal Car Dataset with Computational Fluid Dynamics Simulations and Deep Learning Benchmarks](https://mlsanity.com/paper/2406.09624/) (2024)
- [MetaOpenFOAM: an LLM-based multi-agent framework for CFD](https://mlsanity.com/paper/2407.21320/) (2024)
- [DrivAerNet: A Parametric Car Dataset for Data-Driven Aerodynamic Design and Prediction](https://mlsanity.com/paper/2403.08055/) (2024)
- [From Idea to CAD: A Language Model-Driven Multi-Agent System for Collaborative Design](https://mlsanity.com/paper/2503.04417/) (2025)
- [TripNet: Learning Large-scale High-fidelity 3D Car Aerodynamics with Triplane Networks](https://mlsanity.com/paper/2503.17400/) (2025)
- [Drag-guided diffusion models for vehicle image generation](https://mlsanity.com/paper/2306.09935/) (2023)
- [Sketch2Prototype: Rapid Conceptual Design Exploration and Prototyping with Generative AI](https://mlsanity.com/paper/2405.12985/) (2024)
- [LLM Agent for Fire Dynamics Simulations](https://mlsanity.com/paper/2412.17146/) (2024)
- [VehicleSDF: A 3D generative model for constrained engineering design via surrogate modeling](https://mlsanity.com/paper/2410.18986/) (2024)
- [Manipulating Vehicle 3D Shapes through Latent Space Editing](https://mlsanity.com/paper/2410.23931/) (2024)

## Built on by (most cited first, 21 of 21)

- [Large Language Models for Computer-Aided Design: A Survey](https://mlsanity.com/paper/2505.08137/) (2025)
- [CarBench: A Comprehensive Benchmark for Neural Surrogates on High-Fidelity 3D Car Aerodynamics](https://mlsanity.com/paper/2512.07847/) (2025)
- [A Lightweight Large Language Model-Based Multi-Agent System for 2D Frame Structural Analysis](https://mlsanity.com/paper/2510.05414/) (2025)
- [Agentic Large Language Models for Conceptual Systems Engineering and Design](https://mlsanity.com/paper/2507.08619/) (2025)
- [TripOptimizer: Generative 3D Shape Optimization and Drag Prediction using Triplane VAE Networks](https://mlsanity.com/paper/2509.12224/) (2025)
- [Toward Autonomous Engineering Design: A Knowledge-Guided Multi-Agent Framework](https://mlsanity.com/paper/2511.03179/) (2025)
- [A Hierarchical Agentic Framework for Autonomous Drone-Based Visual Inspection](https://mlsanity.com/paper/2510.00259/) (2025)
- [Engineering.ai: A Platform for Teams of AI Engineers in Computational Design](https://mlsanity.com/paper/2511.00122/) (2025)
- [Agentic AI Systems in Electrical Power Systems Engineering: Current State-of-the-Art and Challenges](https://mlsanity.com/paper/2511.14478/) (2025)
- [Supervising Ralph Wiggum: Exploring a Metacognitive Co-Regulation Agentic AI Loop for Engineering Design](https://mlsanity.com/paper/2603.24768/) (2026)
- [EngiAI: Capability-Based Evaluation of Tool-Connected LLM Agents for Engineering Design](https://mlsanity.com/paper/2605.19743/) (2026)
- [Empowering Real-World: A Survey on the Technology, Practice, and Evaluation of LLM-driven Industry Agents](https://mlsanity.com/paper/2510.17491/) (2025)
- [TurboAgent: An LLM-Driven Autonomous Multi-Agent Framework for Turbomachinery Aerodynamic Design](https://mlsanity.com/paper/2604.06747/) (2026)
- [Intelligent Design 4.0: Paradigm Evolution Toward the Agentic AI Era](https://mlsanity.com/paper/2506.09755/) (2025)
- [Agentic Risk-Aware Set-Based Engineering Design](https://mlsanity.com/paper/2604.16687/) (2026)
- [Multi-Agentic Approach for History Matching of Oil Reservoirs](https://mlsanity.com/paper/2605.15028/) (2026)
- [MasFACT: Continual Multi-Agent Topology Learning via Geometry-Aware Posterior Transfer](https://mlsanity.com/paper/2605.17361/) (2026)
- [TO-Agents: A Multi-Agent AI Framework for Subjective Preference-Guided Topology Optimization](https://mlsanity.com/paper/2605.21622/) (2026)
- [Enhancing Creativity in 3D Generative Design via a TRIZ-Inspired Text-to-CAD Framework](https://mlsanity.com/paper/2606.21378/) (2026)
- [Knowledge-Constrained Shape Optimization with a Mixture-of-Experts Neural Operator for High-Confidence Design](https://mlsanity.com/paper/2607.09763/) (2026)
- [AnchorGen: Anchored Optimization for Customizable Generative 3D Design](https://mlsanity.com/paper/2610.06135/) (2026)

## Abstract (arXiv)

We introduce the concept of "Design Agents" for engineering applications, particularly focusing on the automotive design process, while emphasizing that our approach can be readily extended to other engineering and design domains. Our framework integrates AI-driven design agents into the traditional engineering workflow, demonstrating how these specialized computational agents interact seamlessly with engineers and designers to augment creativity, enhance efficiency, and significantly accelerate the overall design cycle. By automating and streamlining tasks traditionally performed manually, such as conceptual sketching, styling enhancements, 3D shape retrieval and generative modeling, computational fluid dynamics (CFD) meshing, and aerodynamic simulations, our approach reduces certain aspects of the conventional workflow from weeks and days down to minutes. These agents leverage state-of-the-art vision-language models (VLMs), large language models (LLMs), and geometric deep learning techniques, providing rapid iteration and comprehensive design exploration capabilities. We ground our methodology in industry-standard benchmarks, encompassing a wide variety of conventional automotive designs, and utilize high-fidelity aerodynamic simulations to ensure practical and applicable outcomes. Furthermore, we present design agents that can swiftly and accurately predict simulation outcomes, empowering engineers and designers to engage in more informed design optimization and exploration. This research underscores the transformative potential of integrating advanced generative AI techniques into complex engineering tasks, paving the way for broader adoption and innovation across multiple engineering disciplines.