AI-Driven Autonomous Exception Handling in SAP S/4HANA Through Intelligent Agents and Event-Driven Supply Networks

Modern enterprise resource planning environments increasingly require autonomous mechanisms capable of detecting, interpreting, prioritizing, and resolving operational exceptions without relying exclusively on manual intervention. This paper develops a conceptual AI-driven framework for autonomous exception handling in SAP S/4HANA by integrating intelligent agents, event-driven supply-network coordination, anomaly detection, transfer learning, meta-learning, and prognostics-oriented reasoning. The research synthesizes the supplied literature on intelligent fault diagnosis, early warning, deep learning, few-shot learning, transfer learning, and meta-reinforcement learning to establish a theoretical foundation for autonomous enterprise exception management. The proposed framework treats supply-chain and manufacturing exceptions as dynamic events that require contextual diagnosis and adaptive response rather than static rule-based escalation. Intelligent agents are positioned as decision units that receive event signals, evaluate anomaly severity, retrieve contextual information, generate response alternatives, and coordinate corrective actions across production, procurement, inventory, maintenance, and logistics processes. The framework further incorporates confidence-aware human escalation to reduce the risks associated with autonomous decisions. The analysis indicates that combining early anomaly detection with adaptive learning can improve the responsiveness and scalability of exception management, particularly when operational conditions change across machines, plants, products, or supply-network configurations. However, autonomous execution introduces challenges related to model generalization, explainability, data quality, decision confidence, and governance. The study contributes a research-oriented architecture for connecting AI reasoning with event-driven SAP S/4HANA processes and identifies future research directions for empirical validation.