Multiagent Artificial Intelligence for Autonomous Kubernetes Management and Cloud Resource Optimization

Abstract

As organizations migrate toward cloud-native applications, microservices, and container deployments, effectively managing cloud resources in large scale has become a challenging endeavor. Kubernetes has evolved as a container orchestration platform that provides automated deployment, scaling, and management of containerized applications. Nevertheless, most traditional Kubernetes controllers utilize static rules, fixed thresholds, and reactive strategies which are insufficient to handle the dynamic nature of cloud environments. In this study, we propose a multi-agent artificial intelligence (MAAI) framework for automating Kubernetes management and cloud resource optimization. The suggested framework deploys a number of AI agents that collaborate for workload prediction, intelligent scheduling, resource utilization, autoscaling, energy optimization, and fault recovery using deep reinforcement learning and predictive modeling. The framework learns continuously from Kubernetes logs to discover optimal resource allocation and control policies. It achieves autonomous Kubernetes management through a closed-loop control structure in which AI agents monitor, predict, decide, and act on Kubernetes operations. The agents optimize the usage of CPU, memory, storage, and network resources without compromising service-level agreements (SLA). In contrast to rule-based Kubernetes management and the horizontal pod autoscaler (HPA) that uses fixed metric-based thresholds, the proposed MAAI framework increases resource utilization, decreases latency, lowers energy usage, and improves reliability of cloud infrastructure.