Agentic RAG QE From Workflow Design to Automation Framework

Description

In this video, we explore how Agentic RAG (Retrieval-Augmented Generation with Agents) is transforming the future of Quality Engineering (QE). You’ll see how AI agents, retrieval systems, and orchestration frameworks come together to build a next-generation testing workflow — one that reasons, adapts, and learns continuously. We’ll cover: Designing the Agentic QE workflow — roles, reasoning steps, and feedback loops Building the automation framework and toolchain for scalable deployment Integrating Omniit.ai’s cloud testing orchestration for real-world execution How to measure intelligence, learning, and quality impact Ideal for QE leaders, automation architects, and AI testers aiming to build intelligent, self-evolving test systems. Https://Omniit.ai brings you this series to help QE teams go beyond automation — and build truly AI-first quality ecosystems. Subscript to us for updates. 📚 Further Reading 1. Ji Shin, Reem Aleithan, Hadi Hemmati, Song Wang (2024). Retrieval-Augmented Test Generation: How Far Are We? 👉 https://arxiv.org/abs/2409.12682 Explores how retrieval-augmented models can automate test generation, especially for API and software regression testing. 2. Mohanakrishnan Hariharan et al. (2025). Agentic RAG for Software Testing with Hybrid Vector-Graph and Multi-Agent Orchestration. arXiv preprint. 👉 https://arxiv.org/pdf/2510.10824 Focuses on how Agentic RAG can automate software testing workflows, integrating retrieval, reasoning, and CI/CD orchestration. 3. Top 20+ Agentic RAG Frameworks and Tools (2025). AI Multiple Research Blog. 👉 https://research.aimultiple.com/agentic-rag/ Compares existing Agentic RAG platforms and frameworks — from LangChain and LlamaIndex to emerging orchestration tools.