本文提出一种利用Agentic Retrieval-Augmented Generation（RAG）系统实现软件测试自动化的方法，结合自主AI代理与混合向量-图知识系统，自动化测试计划、用例及QE度量生成。方法借助LLM如Gemini和Mistral、多智能体编排及增强情境化，显著提高测试准确性，实验证明可缩短测试时间85%，提高测试套件效率85%，预期节省35%成本，加速上线时间2个月。

arXiv:2510.10824v1 Announce Type: cross Abstract: We present an approach to software testing automation using Agentic Retrieval-Augmented Generation (RAG) systems for Quality Engineering (QE) artifact creation. We combine autonomous AI agents with hybrid vector-graph knowledge systems to automate test plan, case, and QE metric generation. Our approach addresses traditional software testing limitations by leveraging LLMs such as Gemini and Mistral, multi-agent orchestration, and enhanced contextualization. The system achieves remarkable accuracy improvements from 65% to 94.8% while ensuring comprehensive document traceability throughout the quality engineering lifecycle. Experimental validation of enterprise Corporate Systems Engineering and SAP migration projects demonstrates an 85% reduction in testing timeline, an 85% improvement in test suite efficiency, and projected 35% cost savings, resulting in a 2-month acceleration of go-live.