Agentic Context Engineering: Learning Comprehensive Contexts for Self-Improving Language Models

Large language model (LLM) applications such as agents and domain-specific reasoning increasingly rely on context adaptation, modifying model inputs with instructions, strategies, or evidence, rather than weight updates. While prior methods improve usability, they often suffer from a brevity bias, discarding domain-specific insights in favor of short summaries, and from context collapse, where iterative rewriting erodes details over time. Building on the adaptive memory introduced by Dynamic Cheatsheet, we present ACE (Agentic Context Engineering), a framework that treats contexts as evolving playbooks that accumulate, refine, and organize strategies through a modular process of generation, reflection, and curation. ACE prevents collapse by applying structured, incremental updates that preserve detailed knowledge and scale with long-context models. Across agentic and domain-specific benchmarks, ACE consistently outperforms strong baselines, improving application performance by 9.0\% while reducing adaptation latency and rollout cost. Notably, ACE could adapt effectively without labeled supervision, instead leveraging natural execution feedback, and on the AppWorld leaderboard it matches the top-1-ranked production-level agent while using a smaller open-source model. These results demonstrate that comprehensive, evolving contexts enable scalable, efficient, and self-improving LLM systems.