Organizations with complex supply chains, distributed teams, and multi-layered workflows often struggle to allocate resources efficiently. Traditional optimization systems are limited by static models and centralized architectures. Multi-Agent Artificial Intelligence (MAAI) offers a decentralized, autonomous, and dynamic solution for real-time resource optimization. This paper explores multi-agent system (MAS) theory, coordination mechanisms, communication protocols, distributed decision-making, and reinforcement learning techniques for optimizing labor, capital, logistics, inventory, and energy resources within large enterprises. A Multi-Agent Optimization Framework (MAOF) is proposed, integrating collaborative agents, negotiation engines, and adaptive reward mechanisms. Conceptual diagrams and tables illustrate agent hierarchies, coordination strategies, and system workflows. The findings highlight the potential of MAAI in enhancing efficiency, reducing operational costs, and improving decision accuracy in unpredictable business environments.