From Slop to Skill: An Agent Proficiency Engine Using RLM (gonzo.engineer)

A developer has created a groundbreaking Agent Proficiency Engine using Recursive Language Models (RLM), designed to enhance the learning capabilities of AI agents. The project began with a keen focus on understanding the mem0 codebase, utilizing AsyncReview—a tool adept at recursive reasoning and evidence gathering in coding tasks. The key innovation is a structured approach called a "runbook," which sequences technical questions to provide context and deepen knowledge as the model progresses through a codebase. This method emphasizes the interdependency of questions, which leads to a more thorough understanding rather than superficial insights. Significantly, this development addresses a critical challenge in AI—enabling agents to learn effectively through structured feedback loops. The process involves generating a report from the runbook, identifying gaps, and refining the learning material iteratively until no new significant improvements emerge. This iterative learning model marks a shift from shallow prompting to a more robust training paradigm, allowing agents to achieve proficiency in unfamiliar projects by systematically developing their skills. This innovation could redefine how agent capabilities are built and heralds the rise of a new "skill development" category within AI, showcasing varied approaches to enhancing agent performance in complex tasks.