The Playbook Approach: How AI Agents Can Finally Learn and Improve Over Time

ACE introduces a structured framework for maintaining AI agent context through curator-driven refinement, addressing the persistent problem of agents with static prompts that never improve from production experience. The approach eliminates context collapse and brevity bias—silent failure modes that degrade agent performance over time—while demonstrating +10.6% performance gains on benchmarks. For teams deploying agents at scale, ACE represents a fundamental shift from static prompt engineering to dynamic, self-improving context management.