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.
@article{arxiv.2510.10824,
title = {Agentic RAG for Software Testing with Hybrid Vector-Graph and Multi-Agent Orchestration},
author = {Mohanakrishnan Hariharan and Satish Arvapalli and Seshu Barma and Evangeline Sheela},
journal= {arXiv preprint arXiv:2510.10824},
year = {2025}
}