Multi-agent systems are transforming enterprise AI by enabling autonomous entities to solve complex problems that single agents cannot address alone. These systems fundamentally transform applications across finance, healthcare, and supply chain management by deploying agents that either work together toward shared goals or compete to optimize individual outcomes. The interaction paradigm chosen—collaborative or competitive—directly impacts business results, system architecture, and evaluation approaches. Collaborative multi-agent systems are environments where multiple agents share information, coordinate their roles, and pursue common goals, while competitive multi-agent systems are environments where agents act independently to optimize their own goals, similar to market dynamics. The key differences between these two paradigms lie in their fundamental objectives and information sharing, which shapes every aspect of system design, from communication protocols to resource allocation strategies. Collaborative systems work best when you need operational efficiency and innovation, while competitive systems excel when market optimization and strategic advantage matter most.