一句话：提出分布式、目标驱动的 AWARE 框架，以自主 AI 代理替代 MAPE-K 循环，实现主动自适应与持续学习。
传统自适应系统依赖 MAPE-K 循环，其集中、反应式与顺序式结构在现代系统复杂度下暴露出缺乏主动性、可扩展性差、难以集成持续学习和分布式决策等问题。本文提出 AWARE（Assess, Weigh, Act, Reflect, Enrich），一个分布式、目标驱动的自适应框架。AWARE 采用可主动适应、协作和持续学习的自主 AI 代理来增强决策与韧性；其模块化设计支持代理动态集成与资源优化，从而提升可扩展性和适应性。AWARE 能预测变化、优化响应，并依据上下文迭代改进策略。作者通过与传统 MAPE-K 的比较及一个真实用例，展示分布式智能如何重新定义自适应系统并更契合复杂现实系统需求。
Self-adaptive systems have traditionally relied on the MAPE-K loop. It consists of a centralized, reactive, and sequential loop for monitoring, analyzing, planning, and executing system adaptations. However, the increasing complexity and dynamic nature of modern systems have exposed the limitations of MAPE-K loops, including their lack of proactivity, scalability challenges, and difficulty integrating continuous learning or distributed decision-making. We introduce AWARE (Assess, Weigh, Act, Reflect, Enrich), a distributed, goal-driven framework that addresses these limitations. AWARE employs autonomous AI agents capable of proactive adaptation, collaboration, and continuous learning to enhance decision-making and system resilience. The modular design of our framework supports dynamic agent integration and optimized resource utilization, enabling seamless scalability and adaptability. AWARE not only anticipates changes and optimizes responses but also iteratively refines its strategies based on contextual insights. Through a comparison with MAPE-K and a real-world use case, we demonstrate how AWARE distributed intelligence redefines the capabilities of self-adaptive systems, offering a solution better aligned with the demands of complex real-world systems.