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.