Core Viewpoint - The article discusses the advantages and disadvantages of using single-agent versus multi-agent models in AI product design, suggesting that a multi-agent collaboration approach mimics human teamwork and can lead to better outcomes in complex tasks [2][3][10]. Group 1: Single Intelligence vs. Collective Intelligence - Single intelligence relies on one large model to handle all aspects of a task, which can lead to issues when tasks become complex, as it struggles with context management and attention distribution [5][9]. - Collective intelligence involves breaking tasks into sub-roles managed by multiple agents, allowing for parallel processing and better handling of complex tasks through division of labor and communication [5][11]. - The article highlights that collective intelligence can produce more robust conclusions through internal evaluations and interactions among agents, leading to higher quality outputs [11][12]. Group 2: Non-linear Thinking in Complex Tasks - Complex tasks are not linear and require iterative processes similar to human meetings, where multiple perspectives are shared and refined to reach a consensus [13][14]. - The lack of support for non-linear processes in single intelligence models leads to unreliable outputs in complex scenarios, as they cannot effectively manage diverse inputs and iterative feedback [15]. Group 3: Human-AI Collaboration - The article emphasizes that successful human-AI collaboration requires aligning cognitive capabilities upward and value judgments downward, ensuring that AI enhances human decision-making while adhering to ethical standards [21][20]. - AI can expand human cognitive boundaries by providing extensive memory and parallel processing capabilities, but human judgment remains crucial for contextualizing AI outputs [19][20]. Group 4: New Product Paradigm - The traditional product design approach is shifting from a linear model to a multi-agent collaborative ecosystem, which allows for better task management and evidence tracking [22][28]. - This new paradigm emphasizes clear role definitions, effective communication among agents, and dynamic task allocation to enhance efficiency and reduce costs [30][31]. Group 5: Trust in AI Products - Trust is becoming a critical factor in AI product commercialization, as users seek reliable and verifiable results rather than mere attention-grabbing content [35]. - The article argues that the future of AI products will hinge on building trust through transparency and accountability in AI outputs [35]. Group 6: Conclusion - The article concludes that the era of human-machine collaboration is upon us, where AI not only executes tasks but also engages in meaningful dialogue, enhancing human capabilities while requiring human oversight to ensure ethical application [36][37].