This research presents a compelling advancement in agentic AI, particularly in addressing the long-standing challenge of continuous learning post-deployment. The methodology is robust, leveraging structured memory distillation and self-assessment to extract generalizable reasoning patterns—a significant improvement over prior approaches that either over-index on low-level action trajectories or ignore failures entirely. The introduction of MaTTS is particularly innovative, as it bridges the gap ...