# Engineering.ai: A Platform for Teams of AI Engineers in Computational Design

arXiv: https://arxiv.org/abs/2511.00122 · HTML: https://mlsanity.com/paper/2511.00122/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 56 references that are in the map.
- First posted on arXiv 31 Oct 2025.

## Builds on (most cited first, 17 of 17)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [The Rise and Potential of Large Language Model Based Agents: A Survey](https://mlsanity.com/paper/2309.07864/) (2023)
- [The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery](https://mlsanity.com/paper/2408.06292/) (2024)
- [The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search](https://mlsanity.com/paper/2504.08066/) (2025)
- [Accelerating scientific discovery with Co-Scientist](https://mlsanity.com/paper/2502.18864/) (2025)
- [CAD-MLLM: Unifying Multimodality-Conditioned CAD Generation With MLLM](https://mlsanity.com/paper/2411.04954/) (2024)
- [An AI system to help scientists write expert-level empirical software](https://mlsanity.com/paper/2509.06503/) (2025)
- [MetaOpenFOAM: an LLM-based multi-agent framework for CFD](https://mlsanity.com/paper/2407.21320/) (2024)
- [AI Agents in Engineering Design: A Multi-Agent Framework for Aesthetic and Aerodynamic Car Design](https://mlsanity.com/paper/2503.23315/) (2025)
- [Fine-tuning a Large Language Model for Automating Computational Fluid Dynamics Simulations](https://mlsanity.com/paper/2504.09602/) (2025)
- [Foam-Agent 2.0: An End-to-End Composable Multi-Agent Framework for Automating CFD Simulation in OpenFOAM](https://mlsanity.com/paper/2509.18178/) (2025)
- [DeepSeek vs. ChatGPT vs. Claude: A Comparative Study for Scientific Computing and Scientific Machine Learning Tasks](https://mlsanity.com/paper/2502.17764/) (2025)
- [ChatCFD: An LLM-Driven Agent for End-to-End CFD Automation with Structured Knowledge and Reasoning](https://mlsanity.com/paper/2506.02019/) (2025)
- [Using Large Language Models for Parametric Shape Optimization](https://mlsanity.com/paper/2412.08072/) (2024)
- [CFDLLMBench: A Benchmark Suite for Evaluating Large Language Models in Computational Fluid Dynamics](https://mlsanity.com/paper/2509.20374/) (2025)

## Built on by (most cited first, 3 of 3)

- [Towards an AI Fluid Scientist: LLM-Powered Scientific Discovery in Experimental Fluid Mechanics](https://mlsanity.com/paper/2512.04716/) (2025)
- [CFD-copilot: leveraging domain-adapted large language model and model context protocol to enhance simulation automation](https://mlsanity.com/paper/2512.07917/) (2025)
- [A Case-Bundle Operating Model for Coding Agents in OpenFOAM-Based CFD](https://mlsanity.com/paper/2609.11941/) (2026)

## Abstract (arXiv)

In modern engineering practice, human engineers collaborate in specialized teams to design complex products, with each expert completing their respective tasks while communicating and exchanging results and data with one another. While this division of expertise is essential for managing multidisciplinary complexity, it demands substantial development time and cost. Recently, we introduced OpenFOAMGPT (1.0, 2.0), which functions as an autonomous AI engineer for computational fluid dynamics, and turbulence.ai, which can conduct end-to-end research in fluid mechanics draft publications and PhD theses. Building upon these foundations, we present Engineering.ai, a platform for teams of AI engineers in computational design. The framework employs a hierarchical multi-agent architecture where a Chief Engineer coordinates specialized agents consisting of Aerodynamics, Structural, Acoustic, and Optimization Engineers, each powered by LLM with domain-specific knowledge. Agent-agent collaboration is achieved through file-mediated communication for data provenance and reproducibility, while a comprehensive memory system maintains project context, execution history, and retrieval-augmented domain knowledge to ensure reliable decision-making across the workflow. The system integrates FreeCAD, Gmsh, OpenFOAM, CalculiX, and BPM acoustic analysis, enabling parallel multidisciplinary simulations while maintaining computational accuracy. The framework is validated through UAV wing optimization. This work demonstrates that agentic-AI-enabled AI engineers has the potential to perform complex engineering tasks autonomously. Remarkably, the automated workflow achieved a 100% success rate across over 400 parametric configurations, with zero mesh generation failures, solver convergence issues, or manual interventions required, validating that the framework is trustworthy.