Multi-agent systems are at the forefront of artificial intelligence, offering innovative solutions for complex problem-solving through coordinated AI networks and transforming business operations by automating tasks efficiently. These systems are becoming integral to enterprises, likened to critical infrastructure like databases and cloud computing, as they manage tasks at scales and speeds beyond human capabilities. The strategic implementation of these systems allows businesses to focus on strategic initiatives, with multi-agent orchestration providing sophisticated coordination across diverse tasks, enhancing productivity, and enabling operational innovation. Various orchestration approaches, such as managerial, Directed Acyclic Graph (DAG)-based, and hybrid, cater to specific operational needs, allowing seamless integration of traditional AI models with newer architectures. Despite their potential, challenges remain in infrastructure integration, data preparation, and skill gaps, necessitating comprehensive strategies to maximize AI agent value. Ensuring responsible AI through protective constraints and continuous monitoring is crucial, especially in regulated industries. As multi-agent systems evolve, they promise to redefine enterprise AI capabilities, fostering new forms of human-AI collaboration and reshaping organizational structures, as exemplified by companies like Galileo.