Large Language Model (LLM) agents are increasingly deployed in environments that generate massive, dynamic contexts. However, a critical bottleneck remains: while agents have access to this context, their static prompts lack the mechanisms to manage it effectively, leading to recurring Corrective and Enhancement failures. To address this capability gap, we introduce \textbf{SCOPE} (Self-evolving Context Optimization via Prompt Evolution). SCOPE frames context management as an \textit{online optimization} problem, synthesizing guidelines from execution traces to automatically evolve the agent's prompt. We propose a Dual-Stream mechanism that balances tactical specificity (resolving immediate errors) with strategic generality (evolving long-term principles). Furthermore, we introduce Perspective-Driven Exploration to maximize strategy coverage, increasing the likelihood that the agent has the correct strategy for any given task. Experiments on the HLE benchmark show that SCOPE improves task success rates from 14.23\% to 38.64\% without human intervention. We make our code publicly available at https://github.com/JarvisPei/SCOPE.

翻译：大型语言模型（LLM）智能体正越来越多地被部署在产生海量动态上下文的环境中。然而，一个关键瓶颈依然存在：尽管智能体能够访问这些上下文，但其静态提示缺乏有效管理这些上下文的机制，导致反复出现纠正与增强失败。为弥补这一能力差距，我们引入了\\textbf{SCOPE}（通过提示演化的自演化上下文优化）。SCOPE将上下文管理视为一个\\textit{在线优化}问题，通过综合执行轨迹中的指导原则来自动演化智能体的提示。我们提出了一种双流机制，以平衡战术特异性（解决即时错误）与战略通用性（演化长期原则）。此外，我们引入了视角驱动探索以最大化策略覆盖范围，从而提高智能体在任何给定任务中拥有正确策略的可能性。在HLE基准测试上的实验表明，SCOPE在无需人工干预的情况下将任务成功率从14.23%提升至38.64%。我们的代码已在https://github.com/JarvisPei/SCOPE公开提供。