Learn how to implement a continual learning system that automatically improves coding agents through dynamic system prompt optimization. Discover how to move beyond static, hand-edited system prompts and style guides by applying reinforcement learning techniques to prompts rather than model weights. Explore a practical system-prompt learning loop that enables coding agents to learn from pull request reviews, feedback, and evaluations to continuously update their instruction sets. Master techniques for capturing runtime signals and automatically tuning system prompts that can be applied to any type of agent development, addressing the scalability challenges of traditional RL approaches in enterprise environments.