The design of reinforced concrete highway barriers is a safety-critical process that requires strict compliance with regulatory provisions such as the AASHTO-LRFD bridge design guidelines. Current engineering practice relies heavily on manual, iterative, and heuristic calculations to satisfy complex nonlinear material and mechanics constraints. Although Large Language Models (LLMs) demonstrate strong generative capabilities, their direct application to structural engineering remains limited by hallucination risks and insufficient physical grounding. To address these challenges, this study proposes a novel "generation-evaluation-optimization" closed-loop framework for automated concrete barrier design using the multi-agent orchestration capabilities of AutoGen. Experimental results demonstrate that the proposed agentic framework achieves over 98% design accuracy, significantly outperforming standalone general-purpose LLMs. More importantly, the study reveals that design performance is not necessarily correlated with model scale, where an 8B-parameter lightweight model could outperform unconstrained 631B-parameter flagship models. This finding highlights the potential to substantially reduce computational costs while improving the accessibility of AI-assisted engineering tools for industry applications. The source code for the proposed multi-agent design framework is available at the project GitHub repository: https://github.com/MXY820/barrier-design. Keywords: Structural Engineering; Multi-Agent Systems; Large Language Models; Concrete Barrier Design; AutoGen; Design Automation.

翻译：钢筋混凝土公路护栏的设计是一个关乎安全的关键过程，必须严格遵守AASHTO-LRFD桥梁设计指南等规范条文。当前的工程实践严重依赖人工、迭代和启发式计算方法来满足复杂的非线性材料与力学约束。尽管大型语言模型展示了强大的生成能力，但其在结构工程中的直接应用仍受限于幻觉风险和物理基础不足。为应对这些挑战，本研究提出了一种新颖的“生成-评估-优化”闭环框架，利用AutoGen的多智能体编排能力实现混凝土护栏的自动化设计。实验结果表明，所提出的智能体框架实现了超过98%的设计准确率，显著优于独立运行的通用型大型语言模型。更重要的是，研究表明设计性能并不必然与模型规模相关，一个80亿参数的轻量级模型能够超越不受约束的6310亿参数的旗舰模型。这一发现凸显了在降低计算成本的同时，提升工业应用中AI辅助工程工具可及性的潜力。所提出的多智能体设计框架的源代码可在项目GitHub仓库获取：https://github.com/MXY820/barrier-design。关键词：结构工程；多智能体系统；大型语言模型；混凝土护栏设计；AutoGen；设计自动化。