Learning-Based Intelligent Agents for Backend Resource Scheduling and Operational Decision Making

Abstract

This study addresses large-scale backend service environments characterized by intense resource competition, frequent workload fluctuations, and long-term decision impact. It proposes a unified modeling and decision approach based on intelligent decision agents. Backend service systems are abstracted as continuously evolving decision environments. From the perspective of system operation mechanisms, the relationships among state representation, decision actions, and long-term feedback are jointly modeled. Resource scheduling and service management are therefore optimized within a unified framework. The proposed method does not rely on manually defined rules or static policy configurations. It learns decision patterns suitable for complex operational conditions through joint modeling of system states and decision feedback. Under unified data and evaluation settings, the method is systematically compared with several representative decision models. The results show more consistent advantages in resource utilization efficiency, service stability, and overall operational quality. The approach adapts more effectively to uncertainty caused by workload variation and service coupling in large-scale backend services. These findings indicate that intelligent decision mechanisms oriented toward long-term operational objectives can enhance overall coordination and efficiency of backend service systems. They provide a practical technical pathway for managing complex backend services.