AI-driven autonomous cloud monitoring and resilience in AWS Environments

Vemulapalli, Vamsi Krishna (2025) AI-driven autonomous cloud monitoring and resilience in AWS Environments. World Journal of Advanced Engineering Technology and Sciences, 15 (2). pp. 3043-3049. ISSN 2582-8266

Modern cloud infrastructures face escalating challenges from service disruptions that cause substantial business impact, with complexity growing exponentially as organizations embrace microservice architectures. This article explores a comprehensive AI-driven autonomous monitoring and resilience system designed specifically for AWS environments. The framework integrates multi-agent monitoring, intelligent anomaly detection, and automated failover orchestration to address the limitations of traditional monitoring approaches. By establishing dynamic baselines across monitored components, the system detects subtle anomalies before they escalate to service-impacting incidents, while sophisticated orchestration capabilities ensure rapid recovery when failures occur. The architecture leverages AWS native services including CloudWatch, X-Ray, CloudTrail, and Route 53, augmented with machine learning capabilities that dramatically improve detection accuracy while reducing false positives. This approach enables organizations to achieve recovery times significantly below industry averages while maintaining appropriate human oversight for critical decisions, creating a foundation for increasingly autonomous cloud operations that enhance resilience posture against an expanding range of failure modes.