We formalize AI-human collaboration through an agent-based simulation that distinguishes optimization-based AI search from satisficing-based human adaptation. Using an NK model, we examine how these distinct decision heuristics interact across modular and sequenced task structures. For modular tasks, AI typically substitutes for humans, yet complementarities emerge when AI explores a moderately broad search space and human task complexity remains low. In sequenced tasks, we uncover a counterintuitive result: when a high-performing human initiates search and AI subsequently refines it, joint performance is maximized, contradicting the dominant AI-first design principle. Conversely, when AI leads and human satisficing follows, complementarities attenuate as task interdependence increases. We further show that memory-less random AI, despite lacking structured adaptation, can improve outcomes when augmenting low-capability humans by enabling escape from local optima. Collectively, our findings reveal that effective AI-human collaboration depends less on industry context and more on task architecture: the division of labor, sequencing, and interdependence structure. By elevating task decomposition as the central design principle, we provide a generalizable framework for strategic decision-making involving agentic AI across diverse organizational settings.

翻译：我们通过基于智能体的仿真形式化人工智能与人类协作，区分基于优化的人工智能搜索与基于满意化的人类适应行为。利用NK模型，我们研究了这两种不同的决策启发式如何在模块化与序列化任务结构中相互作用。对于模块化任务，人工智能通常替代人类角色，但当人工智能探索适度宽广的搜索空间且人类任务复杂度较低时，互补效应显现。在序列化任务中，我们发现了一个反直觉的结果：当高性能人类启动搜索并由人工智能后续优化时，联合绩效达到最大化，这与主流的人工智能优先设计原则相悖。相反，当人工智能主导搜索后人类进行满意化调整时，随着任务互依性增强，互补效应逐渐减弱。我们进一步证明，尽管缺乏结构化适应能力，无记忆随机人工智能通过帮助低能力人类逃离局部最优解，仍能改善协作结果。总体而言，我们的研究揭示有效的人机协作较少取决于行业背景，而更多取决于任务架构：包括劳动分工、序列化安排与互依性结构。通过将任务分解提升为核心设计原则，我们为跨不同组织情境的智能体人工智能战略决策提供了一个可推广的框架。