content type paperpublished October 2025
Agentic RAG for Software Testing with Hybrid Vector-Graph and Multi-Agent Orchestration
AuthorsMohanakrishnan Hariharan, Seshu Babu Barma, Satish Arvapalli, Evangeline Sheela Arulanandam†
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.
- † Independent Researcher
Related readings and updates.
Reinforcement Learning Integrated Agentic RAG for Software Test Cases Authoring
December 9, 2025research area Methods and Algorithms, research area Tools, Platforms, Frameworks
This paper introduces a framework that integrates reinforcement learning (RL) with autonomous agents to enable continuous improvement in the automated process of software test cases authoring from business requirement documents within Quality Engineering (QE) workflows. Conventional systems employing Large Language Models (LLMs) generate test cases from static knowledge bases, which fundamentally limits their capacity to enhance performance over…
Towards Learning Multi-Agent Negotiations via Self-Play
January 28, 2019research area Computer VisionWorkshop at ICCV
Making sophisticated, robust, and safe sequential decisions is at the heart of intelligent systems. This is especially critical for planning in complex multi-agent environments, where agents need to anticipate other agents’ intentions and possible future actions. Traditional methods formulate the problem as a Markov Decision Process, but the solutions often rely on various assumptions and become brittle when presented with corner cases. In…
Discover opportunities in Machine Learning.
Our research in machine learning breaks new ground every day.