This paper introduces a framework that integrates reinforcement learning (RL) with Autonomous Agents to allow continuous improvement in the automated process of software testing papers within an Engineering Engineering (QE) workflow. Standard systems using large-scale linguistic models (LLMS) generate test cases from static knowledge bases, which are limited in their ability to improve performance over time. Our proposed strengthening of the agentic rag (discovery, transcendence, production, production) Reduces limitations by using QE feedback, testing, and the results of feature discovery to automatically improve their strategies to test us. The system combines special agents with a hybrid knowledge base Vector-Graph-Graph-Graph-Graph With advanced advanced algorithms, especially proximal actimization (PPO) and deep Q networks As QES issues ai-ai-made test cases and provides feedback, the system learns from this expert guide to develop future Idearations. Test validation in Enterprise Apple projects provided a profound improvement: a precise increase of 2.4% in test generation accuracy (from 94.8% to 97.2%), and an improvement in error detection rates. The framework establishes a continuous loop of information analysis driven by QE technology, resulting in continued high-quality advanced assessments that enhance, rather than replace, human assessment skills.
