Despite growing appreciation for their nimbleness and adaptability, predicting the strategic behavior of decentralized organizations continues to be a challenging task due to the emergent phenomena and fragmentation of decision-making authority. The present research uses Complex Adaptive Systems (CAS) theory to explain how strategic behavior emerges from the local interactions of self-governing actors in decentralized systems. We design a multi-agent simulation with feedback, adaptive learning, and stimuli from the environment to mimic organizational life under market and coordination pressures. In a set of experiments, we explore how coherence and performance are achieved in the absence of central command and how system microstructural configurations condition macro responses. The findings specify the boundaries of self-organization for innovation, strategic drift, and robustness. This work provides new perspectives on emergent strategy and considers how to effectively structure and manage adaptive, inter-organizational systems.