  In the continued development of next-generation networking and artificial
intelligence content generation (AIGC) services, the integration of multi-agent
systems (MAS) and the mixture of experts (MoE) frameworks is becoming
increasingly important. Motivated by this, this article studies the contrasting
and converging of MAS and MoE in AIGC-enabled networking. First, we discuss the
architectural designs, operational procedures, and inherent advantages of using
MAS and MoE in generative AI to explore its functionality and applications
fully. Next, we review the applications of MAS and MoE frameworks in content
generation and resource allocation, emphasizing their impact on networking
operations. Subsequently, we propose a novel multi-agent-enabled MoE-proximal
policy optimization (MoE-PPO) framework for 3D object generation and data
transfer scenarios. The framework uses MAS for dynamic task coordination of
each network service provider agent and MoE for expert-driven execution of
respective tasks, thereby improving overall system efficiency and adaptability.
The simulation results demonstrate the effectiveness of our proposed framework
and significantly improve the performance indicators under different network
conditions. Finally, we outline potential future research directions.
