ACTIVE MONITORING AND AUTOMATED RECOVERY SYSTEMS USING AI AGENTS AND CONTINUOUS DATA PROCESSING

Abstract

Using artificial intelligence (AI) agents and continuous data processing frameworks, this study examined the design and assessment of automatic recovery and active monitoring systems. The shortcomings of conventional monitoring systems, which were reactive and necessitated manual intervention, frequently resulting in extended outages and service interruptions, served as the impetus for the study. AI agents trained on historical datasets were utilized to identify anomalies and start self-healing processes in a hybrid cloud environment that mimicked real-world situations. The findings showed that while unsupervised methods successfully detected new abnormalities, supervised learning models were more accurate in identifying predetermined failures. When compared to manual approaches, automated recovery mechanisms improved system availability by over four percent by dramatically lowering the Mean Time to Detect (MTTD) and Mean Time to Recovery (MTTR). The gains in efficiency and resilience surpassed the small computational burden that AI-driven monitoring brought. The study came to the conclusion that AI-based monitoring and recovery systems offered a scalable, flexible, and successful method of guaranteeing dependability in dynamic infrastructures like enterprise networks and hybrid clouds. Future research might concentrate on explainable AI for transparency, reinforcement learning for adaptive recovery, and practical deployment testing in expansive settings.