This paper introduces a framework that integrates reinforcement studying (RL) with autonomous brokers to allow steady enchancment within the automated technique of software program check instances authoring from enterprise requirement paperwork inside High quality Engineering (QE) workflows. Standard techniques using Massive Language Fashions (LLMs) generate check instances from static information bases, which basically limits their capability to boost 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 robotically enhance their check case era methods. The system combines specialised brokers with a hybrid vector-graph information base that shops and retrieves software program testing information. By way of superior RL algorithms, particularly Proximal Coverage Optimization (PPO) and Deep Q-Networks (DQN), these brokers optimize their habits based mostly 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 knowledgeable steering to enhance future iterations. Experimental validation on enterprise Apple tasks yielded substantive enhancements: a 2.4% improve in check era accuracy (from 94.8% to 97.2%), and a ten.8% enchancment in defect detection charges. The framework establishes a steady information refinement loop pushed by QE experience, leading to progressively superior check case high quality that enhances, relatively than replaces, human testing capabilities.
