Harnessing Large Language Models and Agentic AI for Transformative Cloud Reliability and Incident Management: A Comprehensive Suggestive Review

Abstract

Cloud infrastructure reliability remains a critical challenge as organizations face escalating operational complexity, with the average enterprise experiencing 14-18 hours of downtime annually at costs reaching $14,056 per minute. Traditional incident management approaches, relying heavily on manual root cause analysis, static troubleshooting guides, and reactive remediation, struggle to meet the demands of modern distributed systems. This systematic study examines the emerging application of Large Language Models (LLMs) and Agentic AI in cloud reliability engineering and incident management. Through comprehensive analysis of 100+ research papers, industry reports, and production deployments spanning 2023-2026, we establish a novel taxonomy organized across four dimensions: scope (detection, diagnosis, remediation, prevention), cloud deployment models (single-cloud, multi-cloud, hybrid), autonomy levels (advisory, human-in-the-loop, fully automated), and compliance frameworks (GDPR, ISO 27001). Our findings reveal that LLM-powered incident assistants reduce Mean Time to Resolution (MTTR) by 40-60%, while multi-agent orchestration systems demonstrate 90% performance improvements for specific workloads. We propose a comprehensive evaluation framework integrating reliability metrics (MTTD, MTTR, incident recurrence), safety indicators (change failure rate, rollback frequency), human factors (cognitive load, trust, explainability), and data privacy governance. This research bridges critical gaps between DevOps/SRE literature and contemporary LLM applications, emphasizing production deployment guardrails, access control, observability patterns, and regulatory compliance, areas traditionally underdeveloped in academic AI research. Organizations implementing these frameworks can expect 30-70% reduction in incident response time, $400B annual savings potential across Global 2000 companies, and enhanced operational resilience through intelligent automation.