Arxiv: https://arxiv.org/abs/2510.04618

This recent paper from Stanford University and UC Berkeley introduces Agentic Context Engineering (ACE), a novel framework designed to enhance the performance of large language models (LLMs) in complex applications like agents and domain-specific reasoning by evolving their context, such as prompts and memory. ACE addresses key limitations of prior context adaptation methods, specifically brevity bias (where context becomes too concise and loses crucial details) and context collapse (where iterative rewriting erodes information over time), by treating contexts as comprehensive, evolving "playbooks." The framework uses a modular architecture—consisting of a Generator, Reflector, and Curator—to incrementally accumulate, refine, and organize strategies, which significantly boosts accuracy on benchmarks like AppWorld and financial analysis tasks while drastically reducing adaptation latency and cost compared to strong baselines. Overall, ACE demonstrates that detailed, self-improving contexts enable more scalable and efficient LLM systems.