随着汽车智能化与电动化进程不断加快，传统硬件在环（Hardware-in-the-Loop，HIL）测试面临人力依赖度高、测试效率低、知识复用难等问题。本文提出一种融合大语言模型（Large Language Model，LLM）与检索增强生成（Retrieval-Augmented Generation，RAG）的智能体架构，通过分层任务分解、RAG 知识增强、代码协同生成，实现测试用例与自动化测试脚本的高效生成。试验结果表明，该方案可大幅减少人工干预、降低 Token 消耗，有效提升测试开发效率。

With the continuous acceleration of the intelligentization and electrification of automobiles, traditional Hardware-in-the-Loop (HIL) testing is facing problems such as high dependence on human resources, low testing efficiency, and difficulty in knowledge reuse. This paper proposes an intelligent agent architecture that integrates Large Language Model (LLM) and Retrieval-Augmented Generation (RAG). Through hierarchical task decomposition, RAG knowledge enhancement, and code collaborative generation, it achieves efficient generation of test cases and automated test scripts. Experimental results show that this solution can significantly reduce human intervention, lower token consumption, and effectively improve testing development efficiency.