Core Viewpoint - The article discusses the emerging trend of collaborative AI systems, where multiple AI agents work together like a human team, potentially surpassing the limitations of single large models [1][2]. Group 1: Collaborative AI Systems - According to IDC, by 2027, 60% of large enterprises are expected to adopt collaborative AI systems, improving business process efficiency by over 50% [2]. - Collaborative AI systems consist of multiple autonomous agents that can perceive, decide, act, and communicate with each other, leading to enhanced problem-solving capabilities [4]. - The performance of multi-agent systems can exceed that of the best single agent by significant margins, as demonstrated by the Claude Opus system, which outperformed the strongest single agent by 90.2% without a substantial increase in generation time [5]. Group 2: Advantages and Challenges - Multi-agent collaboration allows for parallel processing of tasks, significantly reducing task completion time without sacrificing efficiency [5]. - However, the complexity of coordination increases with the number of agents, leading to potential miscommunication and decreased accuracy in outputs [6][8]. - High communication costs can lead to increased computational resource consumption, with token usage in multi-agent interactions being up to 15 times higher than standard conversations [8]. Group 3: Management and Coordination - To manage the complexities of multi-agent systems, a coordinator agent can be introduced to oversee task distribution and conflict resolution, ensuring alignment towards common goals [10]. - Standardized communication protocols can help reduce integration complexity and facilitate efficient information exchange among agents [13]. - The balance between distributed decision-making and centralized control is crucial for the effective functioning of multi-agent systems, requiring ongoing advancements in technology for reliability and security [14].