Manufacturing environments operating on SAP S/4HANA increasingly depend on real-time coordination among production planning, inventory, procurement, maintenance, quality, logistics, and order-management processes. Exception conditions such as material shortages, production delays, capacity conflicts, quality deviations, and delivery disruptions therefore require decisions that are both rapid and context-aware. This paper proposes an Intelligent Multi-Agent Generative AI Framework for Autonomous Exception Resolution in SAP S/4HANA Manufacturing. The framework conceptualizes manufacturing exception management as a distributed decision problem in which specialized AI agents detect, interpret, negotiate, validate, and resolve operational deviations while maintaining authorization and security controls. The theoretical foundation is derived exclusively from the supplied literature, particularly research on delegation, proxy signatures, secure mobile agents, signcryption, identity-based security, and publicly verifiable cryptographic mechanisms. These studies provide a conceptual basis for trusted delegation, authenticated agent interaction, confidentiality, integrity, and verifiable autonomous actions (Mambo et al., 1996; Kim et al., 1997; Lee et al., 2001; Chow et al., 2004). The proposed framework integrates event interpretation, multi-agent reasoning, policy-constrained action selection, cryptographically protected agent communication, and human escalation. Analytical findings indicate that autonomous exception resolution should not be implemented as unrestricted generative decision-making; instead, it requires bounded delegation, explicit authorization, verifiable actions, and exception-specific governance. The resulting architecture provides a research-oriented model for connecting generative AI reasoning with enterprise manufacturing execution while addressing the trust and security challenges inherent in autonomous operational decisions.