An Intelligent Agent for Root-Cause Detection and Remediation

Abstract

Modern cloud environments generate massive volumes of operational data from infrastructure metrics, application logs, distributed traces, events, and monitoring alerts, making rapid identification of the underlying causes of failures increasingly challenging. Traditional incident-management approaches often depend on manual investigation and predefined rules, which can increase mean time to detect and resolve incidents. This paper presents an intelligent agent-based framework for automated root-cause detection and remediation across cloud infrastructure, applications, and distributed services. The proposed approach integrates artificial intelligence and machine learning techniques to continuously analyze heterogeneous operational signals, correlate abnormal behaviors, identify probable root causes, and recommend or execute appropriate remediation actions. The intelligent agent combines observability data, dependency relationships, anomaly detection, predictive analysis, and automated decision-making to support end-to-end incident resolution. By reducing dependence on manual troubleshooting, the framework aims to improve incident detection speed, diagnostic accuracy, service reliability, and operational efficiency. The approach further supports continuous learning from historical incidents and remediation outcomes, enabling the system to adapt to evolving cloud workloads and failure patterns. The proposed framework demonstrates the potential of intelligent automation to transform conventional cloud operations toward proactive, self-healing, and resilient infrastructure management.