# Agentic RAG for Software Testing with Hybrid Vector-Graph and Multi-Agent Orchestration

arXiv: https://arxiv.org/abs/2510.10824 · HTML: https://mlsanity.com/paper/2510.10824/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Agentic RAG, agents and memory, one of 15 directions in Retrieval-augmented generation.
- Builds on 8 of its 29 references that are in the map.
- First posted on arXiv 12 Oct 2025.

## Placed in

- Retrieval-augmented generation / [Agentic RAG, agents and memory](https://mlsanity.com/rag/agentic-rag-agents-and-memory/)
- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 8 of 8)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection](https://mlsanity.com/paper/2310.11511/) (2023)
- [Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering](https://mlsanity.com/paper/2007.01282/) (2020)
- [Semantic Mastery: Enhancing LLMs with Advanced Natural Language Understanding](https://mlsanity.com/paper/2504.00409/) (2025)
- [Enhancing Software Quality Assurance with an Adaptive Differential Evolution based Quantum Variational Autoencoder-Transformer Model](https://mlsanity.com/paper/2503.16335/) (2025)

## Built on by (most cited first, 2 of 2)

- [Reinforcement Learning Integrated Agentic RAG for Software Test Cases Authoring](https://mlsanity.com/paper/2512.06060/) (2025)
- [Assistance to Autonomy: A Systematic Literature Review of Agentic AI across the Software Development Life Cycle](https://mlsanity.com/paper/2605.15245/) (2026)

## Abstract (arXiv)

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.