[2510.04618] Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models

Computer Science > Machine Learning arXiv:2510.04618 (cs) [Submitted on 6 Oct 2025 (v1), last revised 29 Mar 2026 (this version, v3)] Title:Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models Authors:Qizheng Zhang, Changran Hu, Shubhangi Upasani, Boyuan Ma, Fenglu Hong, Vamsidhar Kamanuru, Jay Rainton, Chen Wu, Mengmeng Ji, Hanchen Li, Urmish Thakker, James Zou, Kunle Olukotun View a PDF of the paper titled Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models, by Qizheng Zhang and 12 other authors View PDF HTML (experimental) Abstract: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. 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...