Agentic Context Engineering: Evolving Contexts for Self-Improving
  Language Models
Authors: name: Qizheng Zhang, name: Changran Hu, name: Shubhangi Upasani, name: Boyuan Ma, name: Fenglu Hong, name: Vamsidhar Kamanuru, name: Jay Rainton, name: Chen Wu, name: Mengmeng Ji, name: Hanchen Li, name: Urmish Thakker, name: James Zou, name: Kunle Olukotun

Large language model (LLM) applications such as agents and domain-specific
reasoning increasingly rely on context adaptation -- modifying inputs with
instructions, strategies, or evidence, rather than weight updates. Prior
approaches improve usability but often suffer from brevity bias, which drops
domain insights for concise summaries, and from context collapse, where
iterative rewriting erodes details over time. Building on the adaptive memory
introduced by Dynamic Cheatsheet, we introduce 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 with structured,
incremental updates that preserve detailed knowledge and scale with
long-context models. Across agent and domain-specific benchmarks, ACE optimizes
contexts both offline (e.g., system prompts) and online (e.g., agent memory),
consistently outperforming strong baselines: +10.6% on agents and +8.6% on
finance, while significantly reducing adaptation latency and rollout cost.
Notably, ACE could adapt effectively without labeled supervision and instead by
leveraging natural execution feedback. On the AppWorld leaderboard, ACE matches
the top-ranked production-level agent on the overall average and surpasses it
on the harder test-challenge split, despite using a smaller open-source model.
These results show that comprehensive, evolving contexts enable scalable,
efficient, and self-improving LLM systems with low overhead.