Abstract

For over two decades, the management of deeply integrated enterprise ecosystems has relied on the rigid, human-in-the-loop governance of traditional IT Service Management (ITSM) frameworks a paradigm that sprawling SAP and hybrid cloud infrastructures have rendered functionally obsolete. The industry’s subsequent pivot to legacy AIOps attempted to stem this operational bleeding through deterministic log parsing and alert suppression, which has become the approach for a decade. Such deterministic tools operate as narrow, procedural toys that flag anomalies but fundamentally lack the distributed cognitive architectures required for automated remediation. But what, then, is the actual operational utility of merely observing a failure without the algorithmic capacity to heal it? To move beyond the methodological navel-gazing that currently plagues the field, this research proposes a foundational reorientation: an Integrated Incident Response Model (IIRM) wherein goal-driven Agentic AI autonomously ingests unstructured SAP application logs and AWS telemetry, utilizing contextual multi-armed bandit optimization to dynamically negotiate root cause analysis. Empirical evaluation within a rigorously simulated, high-volatility hybrid cloud deployment demonstrates that these communicating agents profoundly reduce Mean Time to Resolution (MTTR) and maintain high diagnostic accuracy, entirely decoupling system recovery from human latency. While mathematically provable guardrails remain an absolute necessity to prevent catastrophic compliance failures, this transition from reactive monitoring to proactive algorithmic agency capable of reflecting, learning, and improving over time finally provides the architecture required to redefine the twenty-year arc of enterprise system resilience.