Tune in as we explore Agentic Context Engineering (ACE), a novel framework designed to overcome limitations like "brevity bias" and "context collapse" that plague traditional LLM context adaptation methods. ACE transforms model contexts into continuously evolving, structured "playbooks" by employing a modular process of generation, reflection, and curation. We discuss how this approach enables scalable, self-improving agents, yielding substantial performance gains on complex tasks—such as +10.6% on agent benchmarks—while significantly lowering adaptation latency and cost.