This paper introduces a framework that integrates reinforcement studying (RL) with autonomous brokers to allow steady enchancment within the automated strategy of software program check instances authoring from enterprise requirement paperwork inside High quality Engineering (QE) workflows. Typical methods using Giant Language Fashions (LLMs) generate check instances from static data bases, which essentially limits their capability to reinforce efficiency over time. Our proposed Reinforcement Infused Agentic RAG (Retrieve, Increase, Generate) framework overcomes this limitation by using AI brokers that study from QE suggestions, assessments, and defect discovery outcomes to routinely enhance their check case era methods. The system combines specialised brokers with a hybrid vector-graph data base that shops and retrieves software program testing data. By way of superior RL algorithms, particularly Proximal Coverage Optimization (PPO) and Deep Q-Networks (DQN), these brokers optimize their conduct primarily based on QE-reported check effectiveness, defect detection charges, and workflow metrics. As QEs execute AI-generated check instances and supply suggestions, the system learns from this skilled steering to enhance future iterations. Experimental validation on enterprise Apple tasks yielded substantive enhancements: a 2.4% enhance in check era accuracy (from 94.8% to 97.2%), and a ten.8% enchancment in defect detection charges. The framework establishes a steady data refinement loop pushed by QE experience, leading to progressively superior check case high quality that enhances, slightly than replaces, human testing capabilities.
