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

• URL: http://arxiv.org/abs/2511.00122v1
• Date: Fri, 31 Oct 2025 08:00:48 GMT
• Title: Engineering.ai: A Platform for Teams of AI Engineers in Computational Design
• Authors: Ran Xu, Yupeng Qi, Jingsen Feng, Xu Chu,
• Abstract summary: We present Engineering.ai, a platform for teams of AI engineers in computational design.<n>The framework employs a hierarchical multi-agent architecture where a Chief Engineer coordinates specialized agents.<n>The system integrates FreeCAD, Gmsh, OpenFOAM, CalculiX, and BPM acoustic analysis, enabling parallel multidisciplinary simulations.
• Score: 8.217119500224284
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Agyn: A Multi-Agent System for Team-Based Autonomous Software Engineering [0.09046463333989574]
  Real-world software development is organized as a collaborative activity carried out by teams following shared methodologies.<n>We present a fully automated multi-agent system that explicitly models software engineering as an organizational process.<n>Our results suggest that replicating team structure, methodology, and communication is a powerful paradigm for autonomous software engineering.
  arXiv  Detail & Related papers  (2026-02-01T22:17:19Z)
• Report for NSF Workshop on AI for Electronic Design Automation [56.48556959103223]
  This report distills the discussions and recommendations from the NSF Workshop on AI for Electronic Design Automation (EDA)<n>Bringing together experts across machine learning and EDA, the workshop examined how AI-spanning large language models (LLMs), graph neural networks (GNNs), reinforcement learning (RL), neurosymbolic methods, etc.<n>The report recommends NSF to foster AI/EDA collaboration, invest in foundational AI for EDA, develop robust data infrastructures, promote scalable compute infrastructure, and invest in workforce development to democratize hardware design and enable next-generation hardware systems.
  arXiv  Detail & Related papers  (2026-01-20T23:45:40Z)
• Advances and Frontiers of LLM-based Issue Resolution in Software Engineering: A Comprehensive Survey [59.3507264893654]
  Issue resolution is a complex Software Engineering task integral to real-world development.<n> benchmarks like SWE-bench revealed this task as profoundly difficult for large language models.<n>This paper presents a systematic survey of this emerging domain.
  arXiv  Detail & Related papers  (2026-01-15T18:55:03Z)
• An Agentic Framework for Autonomous Materials Computation [70.24472585135929]
  Large Language Models (LLMs) have emerged as powerful tools for accelerating scientific discovery.<n>Recent advances integrate LLMs into agentic frameworks, enabling retrieval, reasoning, and tool use for complex scientific experiments.<n>Here, we present a domain-specialized agent designed for reliable automation of first-principles materials computations.
  arXiv  Detail & Related papers  (2025-12-22T15:03:57Z)
• Using GUI Agent for Electronic Design Automation [123.86509061313912]
  Graphical User Interface (GUI) agents adopt an end-to-end paradigm that maps a screenshot to an action sequence.<n>Existing GUI agents are evaluated almost exclusively on commodity software such as Microsoft Word and Excel.<n>This work extends GUI agents from generic office automation to specialized, high-value engineering domains.
  arXiv  Detail & Related papers  (2025-12-12T14:49:32Z)
• Toward Autonomous Engineering Design: A Knowledge-Guided Multi-Agent Framework [8.68512892112474]
  The framework introduces specialized knowledge-driven agents that collaborate to generate and refine design candidates.<n>The framework consists of three key AI agents: a Graph Ontologist, a Design Engineer, and a Systems Engineer.<n>Overall, this work demonstrates how collaborative AI agents equipped with structured knowledge representations can enhance efficiency, consistency, and quality in the engineering design process.
  arXiv  Detail & Related papers  (2025-11-05T04:55:25Z)
• Compiler.next: A Search-Based Compiler to Power the AI-Native Future of Software Engineering [3.8060608371616778]
  We present Compiler.next, a novel search-based compiler designed to enable the seamless evolution of AI-native software systems.<n>Unlike traditional static compilers, Compiler.next takes human-written intents and automatically generates working software by searching for an optimal solution.<n>We present a roadmap to address the core challenges in intent compilation, including developing quality programming constructs, effective search constructs, and interoperability between compilers.
  arXiv  Detail & Related papers  (2025-10-27T21:01:48Z)
• Automating Structural Engineering Workflows with Large Language Model Agents [4.896428524844242]
  $textbfMASSE$ is the first Multi-Agent System for Structural Engineering.<n>It effectively integrates large language model (LLM)-based agents with real-world engineering.
  arXiv  Detail & Related papers  (2025-10-13T04:38:46Z)
• How Software Engineers Engage with AI: A Pragmatic Process Model and Decision Framework Grounded in Industry Observations [1.516251872371896]
  GitHub Copilot and ChatGPT have given rise to "vibe coding"<n>This paper presents two complementary contributions.<n>First, a pragmatic process model capturing real-world AI-assisted SE activities, including prompt design, inspection, fallback, and refinement.<n>Second, a 2D decision framework that could help developers reason about trade-offs between effort saved and output quality.
  arXiv  Detail & Related papers  (2025-07-23T21:00:21Z)
• Unified Software Engineering agent as AI Software Engineer [14.733475669942276]
  Large Language Model (LLM) technology has raised expectations for automated coding.<n>In this paper, we seek to understand this question by developing a Unified Software Engineering agent or USEagent.<n>We envision USEagent as the first draft of a future AI Software Engineer which can be a team member in future software development teams involving both AI and humans.
  arXiv  Detail & Related papers  (2025-06-17T16:19:13Z)
• An LLM-enabled Multi-Agent Autonomous Mechatronics Design Framework [49.633199780510864]
  This work proposes a multi-agent autonomous mechatronics design framework, integrating expertise across mechanical design, optimization, electronics, and software engineering.<n> operating primarily through a language-driven workflow, the framework incorporates structured human feedback to ensure robust performance under real-world constraints.<n>A fully functional autonomous vessel was developed with optimized propulsion, cost-effective electronics, and advanced control.
  arXiv  Detail & Related papers  (2025-04-20T16:57:45Z)
• TheAgentCompany: Benchmarking LLM Agents on Consequential Real World Tasks [55.03911355902567]
  We introduce TheAgentCompany, a benchmark for evaluating AI agents that interact with the world in similar ways to those of a digital worker.<n>We find that the most competitive agent can complete 30% of tasks autonomously.<n>This paints a nuanced picture on task automation with simulating LM agents in a setting a real workplace.
  arXiv  Detail & Related papers  (2024-12-18T18:55:40Z)
• Knowledge Graph Modeling-Driven Large Language Model Operating System (LLM OS) for Task Automation in Process Engineering Problem-Solving [0.0]
  We present the Process Engineering Operations Assistant (PEOA), an AI-driven framework designed to solve complex problems in the chemical and process industries. The framework employs a modular architecture orchestrated by a meta-agent, which serves as the central coordinator. The results demonstrate the framework effectiveness in automating calculations, accelerating prototyping, and providing AI-augmented decision support for industrial processes.
  arXiv  Detail & Related papers  (2024-08-23T13:52:47Z)
• Spider2-V: How Far Are Multimodal Agents From Automating Data Science and Engineering Workflows? [73.81908518992161]
  We introduce Spider2-V, the first multimodal agent benchmark focusing on professional data science and engineering. Spider2-V features real-world tasks in authentic computer environments and incorporating 20 enterprise-level professional applications. These tasks evaluate the ability of a multimodal agent to perform data-related tasks by writing code and managing the GUI in enterprise data software systems.
  arXiv  Detail & Related papers  (2024-07-15T17:54:37Z)
• SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering [79.07755560048388]
  SWE-agent is a system that facilitates LM agents to autonomously use computers to solve software engineering tasks. SWE-agent's custom agent-computer interface (ACI) significantly enhances an agent's ability to create and edit code files, navigate entire repositories, and execute tests and other programs. We evaluate SWE-agent on SWE-bench and HumanEvalFix, achieving state-of-the-art performance on both with a pass@1 rate of 12.5% and 87.7%, respectively.
  arXiv  Detail & Related papers  (2024-05-06T17:41:33Z)
• OpenAGI: When LLM Meets Domain Experts [51.86179657467822]
  Human Intelligence (HI) excels at combining basic skills to solve complex tasks. This capability is vital for Artificial Intelligence (AI) and should be embedded in comprehensive AI Agents. We introduce OpenAGI, an open-source platform designed for solving multi-step, real-world tasks.
  arXiv  Detail & Related papers  (2023-04-10T03:55:35Z)
• Automated Machine Learning: A Case Study on Non-Intrusive Appliance Load Monitoring [81.06807079998117]
  We propose a novel approach to enable Automated Machine Learning (AutoML) for Non-Intrusive Appliance Load Monitoring (NIALM)<n>NIALM offers a cost-effective alternative to smart meters for measuring the energy consumption of electric devices and appliances.
  arXiv  Detail & Related papers  (2022-03-06T10:12:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.