This study implements an agent-based computational model to examine the impact of network structure on simple two-person coordination tasks. The conceptual contribution of the paper is the concept of relational rules, which are heuristic devices that can help harmonize expectations as a function of network ties. With relational ties as a behavioral foundation, the embedded computer experiment manipulates network density in a parametric fashion—thus examining a wide variety of network structures--to examine its impact on coordination. The results indicate that a greater frequency of ties in general does have a positive impact on group coordination. A second manipulation involves variable knowledge of network structure by participants, in particular whether participants are aware of manifest networks or not (these are latent). The impact of network awareness (or lack thereof) does not produce consistent results, and is contingent on particular informational assumptions. In particular, assuming less knowledge of underlying social structures may lead to more coordination than in the case where people make random inferences (that is, they attempt to know more) about latent network ties. Whether the distinction between manifest (known) and latent networks matters also depends on the actual density of the network.