ReasoningBank provides a structured dataset for AI agents to learn from past mistakes and successes. It focuses on iterative refinement through experience rather than static prompting. This framework allows Google researchers to improve agent reliability in complex tasks. Practitioners can now better evaluate how agents adapt their logic over time.