Agentic Context Engineering: Evolving Contexts for Self-Improving
  Language Models
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.
68e48815e4e093a7044e4d75
Qizheng Zhang
claimed_verified
2025-10-09T07:58:00.681Z
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/avatars/2a4589fef05306ccde06728c752e5601.svg
Qizheng Zhang
user
qizhengz
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Changran Hu
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Shubhangi Upasani
68e48815e4e093a7044e4d78
Boyuan Ma
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Fenglu Hong
claimed_verified
2025-10-15T07:08:13.362Z
64237fdbd654afd6931b29ad
/avatars/ad7de02bb5c86544b3c1150ca5e8dfc1.svg
Fenglu Hong
user
hongfenglu
68e48815e4e093a7044e4d7a
Vamsidhar Kamanuru
68e48815e4e093a7044e4d7b
Jay Rainton
68e48815e4e093a7044e4d7c
Chen Wu
68e48815e4e093a7044e4d7d
Mengmeng Ji
68e48815e4e093a7044e4d7e
Hanchen Li
68e48815e4e093a7044e4d7f
Urmish Thakker
68e48815e4e093a7044e4d80
James Zou
68e48815e4e093a7044e4d81
Kunle Olukotun
Agentic Context Engineering: Evolving Contexts for Self-Improving
  Language Models
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.
ACE, a framework for adaptive context engineering, enhances LLM applications by preserving detailed knowledge through structured updates, outperforming baselines in agent and domain-specific tasks with reduced adaptation costs.
Large language model context adaptation Dynamic Cheatsheet ACE Agentic Context Engineering generation reflection curation context collapse adaptation latency rollout cost natural execution feedback AppWorld leaderboard