Agentic AI Framework for Predictive Self-Healing and Resource Optimization in Multi-Cloud Environments

The rapid growth of multi-cloud computing has introduced significant challenges in infrastructure monitoring, fault detection, autonomous recovery, and resource optimization. Traditional cloud management systems rely heavily on manual monitoring and reactive recovery processes, resulting in delayed fault resolution, increased downtime, and elevated operational costs. This paper presents an Agentic AI Framework—named CloudGuard AI—for Predictive Self-Healing and Resource Optimization in Multi-Cloud Environments. The proposed framework employs four collaborating intelligent software agents: a Monitoring Agent that continuously collects infrastructure metrics (CPU, memory, disk), an Analysis Agent that detects anomalies through threshold evaluation, a Decision Agent that autonomously selects corrective actions, and a Recovery Agent that executes remediation procedures. The framework is implemented using Python Flask, SQLite, and Bootstrap, and provides a centralized web-based dashboard for infrastructure visibility, incident management, and performance reporting. Experimental evaluation demonstrates that the framework autonomously detects infrastructure anomalies and initiates recovery actions with minimal delay. Comparative results with traditional monitoring systems confirm improvements in response time, downtime reduction, and resource utilization efficiency. The framework validates the practical applicability of Agentic AI concepts in cloud infrastructure management.