Agentic AI for Dynamic Cloud Resource Management: A Conceptual Framework for Dynamic Cloud Resource Management
Florina Asani, Jaumin Ajdari, Xhemal Zenuni · 2025
Effective cloud resource management is essential for modern computing, but traditional rule-based methods struggle to keep up with dynamic workloads. AI approaches like reinforcement learning and predictive analytics show promise but often work in isolation, lacking collaboration and adaptability for comprehensive management. This research introduces Agentic AI, a novel approach that integrates autonomous, goal-driven, and collaborative agents to optimize cloud resource allocation. Through a systematic literature review, the study highlights key gaps in current techniques and proposes a conceptual framework based on Agentic AI principles. The framework enables agents to predict workloads, set system-wide goals, and dynamically allocate resources in real time. By bridging existing gaps, this research offers a unified and adaptable solution to improve scalability, workload distribution, and cost efficiency, advancing intelligent cloud resource management.