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.