Toward Autonomous Engineering Design: A Knowledge-Guided Multi-Agent Framework

• URL: http://arxiv.org/abs/2511.03179v2
• Date: Thu, 06 Nov 2025 16:54:41 GMT
• Title: Toward Autonomous Engineering Design: A Knowledge-Guided Multi-Agent Framework
• Authors: Varun Kumar, George Em Karniadakis,
• Abstract summary: 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.
• Score: 8.68512892112474
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The engineering design process often demands expertise from multiple domains, leading to complex collaborations and iterative refinements. Traditional methods can be resource-intensive and prone to inefficiencies. To address this, we formalize the engineering design process through a multi-agent AI framework that integrates structured design and review loops. The framework introduces specialized knowledge-driven agents that collaborate to generate and refine design candidates. As an exemplar, we demonstrate its application to the aerodynamic optimization of 4-digit NACA airfoils. The framework consists of three key AI agents: a Graph Ontologist, a Design Engineer, and a Systems Engineer. The Graph Ontologist employs a Large Language Model (LLM) to construct two domain-specific knowledge graphs from airfoil design literature. The Systems Engineer, informed by a human manager, formulates technical requirements that guide design generation and evaluation. The Design Engineer leverages the design knowledge graph and computational tools to propose candidate airfoils meeting these requirements. The Systems Engineer reviews and provides feedback both qualitative and quantitative using its own knowledge graph, forming an iterative feedback loop until a design is validated by the manager. The final design is then optimized to maximize performance metrics such as the lift-to-drag ratio. Overall, this work demonstrates how collaborative AI agents equipped with structured knowledge representations can enhance efficiency, consistency, and quality in the engineering design process.

Related papers

• CeProAgents: A Hierarchical Agents System for Automated Chemical Process Development [73.33844908703799]
  CeProAgents is a hierarchical multi-agent system designed to automate the development of chemical process through collaborative division of labor.<n>To rigorously evaluate the system, we establish CeProBench, a multi-dimensional benchmark structured around three core pillars of chemical engineering.
  arXiv  Detail & Related papers  (2026-03-02T09:37:18Z)
• Engineering.ai: A Platform for Teams of AI Engineers in Computational Design [8.217119500224284]
  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.
  arXiv  Detail & Related papers  (2025-10-31T08:00:48Z)
• A Multidisciplinary Design and Optimization (MDO) Agent Driven by Large Language Models [7.509742996975501]
  We present a Multidisciplinary Design and Optimization (MDO) Agent driven by Large Language Models (LLMs)<n>The agent semi-automates the end-to-end workflow by orchestrating three core capabilities: (i) natural-language-driven parametric modeling, (ii) retrieval-augmented generation (RAG) for knowledge-grounded conceptualization, and (iii) intelligent orchestration of engineering software for performance verification and optimization.
  arXiv  Detail & Related papers  (2025-10-06T01:26:55Z)
• Assemble Your Crew: Automatic Multi-agent Communication Topology Design via Autoregressive Graph Generation [72.44384066166147]
  Multi-agent systems (MAS) based on large language models (LLMs) have emerged as a powerful solution for dealing with complex problems across diverse domains.<n>Existing approaches are fundamentally constrained by their reliance on a template graph modification paradigm with a predefined set of agents and hard-coded interaction structures.<n>We propose ARG-Designer, a novel autoregressive model that operationalizes this paradigm by constructing the collaboration graph from scratch.
  arXiv  Detail & Related papers  (2025-07-24T09:17:41Z)
• AI Agents in Engineering Design: A Multi-Agent Framework for Aesthetic and Aerodynamic Car Design [24.258618104493532]
  We introduce the concept of "Design Agents" for engineering applications, particularly focusing on the automotive design process.<n>Our framework integrates AI-driven design agents into the traditional engineering workflow to augment creativity, enhance efficiency, and significantly accelerate the overall design cycle.
  arXiv  Detail & Related papers  (2025-03-30T04:57:17Z)
• Semi-Automated Design of Data-Intensive Architectures [49.1574468325115]
  This paper introduces a development methodology for data-intensive architectures.<n>It guides architects in (i) designing a suitable architecture for their specific application scenario, and (ii) selecting an appropriate set of concrete systems to implement the application.<n>We show that the description languages we adopt can capture the key aspects of data-intensive architectures proposed by researchers and practitioners.
  arXiv  Detail & Related papers  (2025-03-21T16:01:11Z)
• From Idea to CAD: A Language Model-Driven Multi-Agent System for Collaborative Design [0.06749750044497731]
  We present an approach that mirrors this team structure with a Vision Language Model (VLM)-based Multi Agent System.<n>A model is generated automatically from sketches and/ or textual descriptions.<n>The resulting model can be refined collaboratively in an iterative validation loop with the user.
  arXiv  Detail & Related papers  (2025-03-06T13:21:27Z)
• 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)
• Prototyping with Prompts: Emerging Approaches and Challenges in Generative AI Design for Collaborative Software Teams [2.237039275844699]
  Generative AI models are increasingly being integrated into human task, enabling the production of expressive content.<n>Unlike traditional human-AI design methods, the new approach to designing generative capabilities focuses heavily on prompt engineering strategies.<n>Our findings highlight emerging practices and role shifts in AI system prototyping among multistakeholder teams.
  arXiv  Detail & Related papers  (2024-02-27T17:56:10Z)
• Compositional Generative Inverse Design [69.22782875567547]
  Inverse design, where we seek to design input variables in order to optimize an underlying objective function, is an important problem. We show that by instead optimizing over the learned energy function captured by the diffusion model, we can avoid such adversarial examples. In an N-body interaction task and a challenging 2D multi-airfoil design task, we demonstrate that by composing the learned diffusion model at test time, our method allows us to design initial states and boundary shapes.
  arXiv  Detail & Related papers  (2024-01-24T01:33:39Z)
• Design Space Exploration and Explanation via Conditional Variational Autoencoders in Meta-model-based Conceptual Design of Pedestrian Bridges [52.77024349608834]
  This paper provides a performance-driven design exploration framework to augment the human designer through a Conditional Variational Autoencoder (CVAE) The CVAE is trained on 18'000 synthetically generated instances of a pedestrian bridge in Switzerland.
  arXiv  Detail & Related papers  (2022-11-29T17:28:31Z)
• Investigating Positive and Negative Qualities of Human-in-the-Loop Optimization for Designing Interaction Techniques [55.492211642128446]
  Designers reportedly struggle with design optimization tasks where they are asked to find a combination of design parameters that maximizes a given set of objectives. Model-based computational design algorithms assist designers by generating design examples during design. Black box methods for assistance, on the other hand, can work with any design problem.
  arXiv  Detail & Related papers  (2022-04-15T20:40:43Z)

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.