This paper proposes using agentic AI-based dynamic resource allocation as a component of cloud-based distributed system optimization for supply chains. The solution proposed in this paper uses machine learning and agents based on large-scale experimentation to enhance real-time resource management and decision-making. The results based on large-scale experimentation provide a 25% gain in operational performance, a 30% reduction in resource allocation cost, and a 15% improvement in scalability. It reflects the tremendous potential of AI in optimizing supply chains through the automation of significant processes and enhancing responsiveness. It identifies major challenges like system complexity and integration issues and provides solutions to apply them successfully. The research validates supply chain management practices through adopting AI and cloud computing and their theoretical and practical consequences.