Core Concept - The article discusses the evolution and implementation of multi-agent systems in AI, highlighting the advantages of using multiple specialized agents for complex tasks over single-agent systems [3][9][11]. Group 1: Single-Agent vs Multi-Agent Architecture - Single-agent systems are suitable for simple tasks but struggle with complexity, leading to inefficiencies and increased error rates [9][10]. - Multi-agent systems allow for specialization, where different agents focus on specific tasks, improving overall solution quality and reducing development difficulty [9][11]. Group 2: Multi-Agent System Models - Multi-agent systems can operate in parallel, where multiple agents handle different parts of a task simultaneously, enhancing efficiency [12]. - Alternatively, they can function in a serial manner, where the output of one agent becomes the input for another, suitable for processes requiring sequential approvals [20][24]. Group 3: Practical Applications - The ChatDev collaborative system exemplifies a successful multi-agent architecture, where various roles such as CEO and developers work together to create a video game [6]. - The article emphasizes that while multi-agent systems can address many software engineering challenges, simpler architectures may be more effective in certain scenarios [8]. Group 4: Future Implications - The development of multi-agent systems is expected to play a significant role in the advancement of AI technologies, particularly in complex problem-solving environments [3][6].