The growing scale, complexity, interconnectivity, and autonomy of modern software ecosystems introduce unprecedented uncertainty, challenging the foundations of traditional self-adaptation. Existing approaches, typically rule-driven controllers or isolated learning components, struggle to generalize to novel contexts or coordinate responses across distributed subsystems, leaving them ill-equipped for emergent unknown unknowns. Recent discussions on Self-Adaptation 2.0 emphasize an equal partnership between AI and adaptive systems, merging learning-driven intelligence with adaptive control for predictive and proactive behavior. Building on this foundation, we introduce POLARIS, a three-layer multi-agentic self-adaptation framework that advances beyond reactive adaptation. POLARIS integrates: (1) a low-latency Adapter layer for monitoring and safe execution, (2) a transparent Reasoning layer that generates and verifies plans using tool-aware, explainable agents, and (3) a Meta layer that records experiences and meta-learns improved adaptation policies over time. Through shared knowledge and predictive models, POLARIS handles uncertainty, learns from past actions, and evolves its strategies, enabling systems that anticipate change and maintain resilient, goal-directed behavior. Preliminary evaluation on two self-adaptive exemplars, SWIM and SWITCH, shows that POLARIS consistently outperforms state-of-the-art baselines. We argue this marks a shift toward Self-Adaptation 3.0, akin to Software 3.0: a paradigm where systems not only learn from their environment but also reason about and evolve their own adaptation processes, continuously improving to meet novel challenges.

翻译：现代软件生态系统日益增长的规模、复杂性、互联性和自主性引入了前所未有的不确定性，对传统自适应方法的理论基础构成了挑战。现有方法（通常是规则驱动的控制器或孤立的学习组件）难以泛化到新环境或在分布式子系统间协调响应，使其无法有效应对涌现的未知未知问题。近期关于“自适应2.0”的讨论强调人工智能与自适应系统间的平等协作，将学习驱动的智能与自适应控制相结合以实现预测性和主动性行为。基于此，我们提出POLARIS——一个三层多智能体自适应框架，其超越了反应式自适应。POLARIS整合了：（1）用于监控与安全执行的低延迟适配层，（2）通过工具感知、可解释的智能体生成并验证计划的透明推理层，以及（3）记录经验并随时间元学习改进自适应策略的元层。通过共享知识与预测模型，POLARIS能够处理不确定性、从历史行动中学习并演化其策略，使系统能够预测变化并保持具有韧性的目标导向行为。在两个自适应示例系统（SWIM和SWITCH）上的初步评估表明，POLARIS持续优于现有先进基线方法。我们认为这标志着向“自适应3.0”的转变——类似于“软件3.0”的范式：系统不仅从环境中学习，还能对其自身自适应过程进行推理与演化，通过持续改进以应对新型挑战。