The growing scale, complexity, interconnectivity, and autonomy of modern software ecosystems introduce unprecedented levels of uncertainty, challenging the foundations of traditional self-adaptation. Existing self-adaptive techniques, often based on rule-driven controllers or isolated learning components, struggle to generalize across novel contexts and coordinate responses across distributed subsystems, leaving them ill-equipped to handle emergent \textit{``unknown unknowns''} in complex, dynamic environments. Recent discussions on \textit{Self Adaptation 2.0} established an equal partnership between AI and adaptive systems, merging learning-driven intelligence with adaptive control to enable predictive, data-driven, and proactive adaptation. Building on this foundation, we introduce a new wave of self-adaptation through \textit{POLARIS} a three-layer multi-agentic self-adaptation framework that moves beyond reactive adaptation by combining (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. By using shared knowledge and predictive models, POLARIS can handle uncertainty, learn from its past actions, and evolve its strategies, enabling the engineering of autonomous systems that can anticipate change to ensure resilient and goal-directed behavior under uncertainty. Preliminary evaluation across two distinct self-adaptive exemplars, SWIM and SWITCH, demonstrates that POLARIS consistently outperforms existing state-of-the-art baselines. With this, we motivate a shift towards \textit{Self-Adaptation 3.0} akin to \textit{Software 3.0}, a new paradigm where systems move beyond merely learning from their environment to reasoning about and evolving their own adaptation. In this vision, adaptation contributes to a self-learning process that enables systems to continuously improve and respond to novel challenges.