This episode introduces GEPA (Genetic-Pareto Prompt Optimizer), a novel approach to optimizing prompts for Agentic AI systems. Unlike traditional methods like Reinforcement Learning (RL) that rely on sparse rewards, GEPA utilizes natural language reflection on execution traces and multi-objective evolutionary search to iteratively improve prompts, often outperforming RL with significantly fewer rollouts. It focuses on instruction evolution rather than example-based learning, making it highly efficient and well-suited for modular AI agents. While groundbreaking, the text also acknowledges potential limitations, such as a lack of weight-space adaptation and human control over the optimization process, before concluding with the planned integration of GEPA into the SuperOptiX framework for building self-refining AI agents.