The corpus callosum is a core white matter structure that sits at the center of the brain, playing a role in both interhemispheric communication and the inhibition of hemispheric activity to promote lateralization. Structural connectivity is thought to underlie functional connectivity (FC), but cases of structural brain abnormalities allow for a better understanding of this relationship. Agenesis of the corpus callosum (AgCC) is a condition in which an individual is born without a corpus callosum. These individuals provide a unique opportunity to investigate ways in which the brain adapts its functional organization to the lack of interhemispheric structural connectivity, thereby providing unique insights into brain network organization within and between the two cerebral hemispheres. The present study uses resting-state functional magnetic resonance imaging (fMRI) to compare the network connectivity of an individual with AgCC without any significant comorbidities to a control group of neurotypical adults (n=30). Potential differences of FC within the default mode network and frontoparietal network, as well as FC between these networks and bilateral language networks were examined. The AgCC individual displayed significantly higher FC within the frontoparietal network (t(29)=1.84, p<0.05) and significantly lower FC between the default mode network and the right ventral language stream (t(29)=-1.81, p<0.05) compared to the control group. Further analyses suggest that the right hemisphere’s frontoparietal network is driving the significant difference between the case study and control group in the frontoparietal network. The stronger FC of the frontoparietal network may represent a compensatory strategy used to support lower overall levels of default mode network and dual stream language network connectivity. Overall, the findings suggest that decreased interhemispheric structural connectivity may lead to increased compensation via attention networks such as the frontoparietal network, and decreased right hemisphere language network involvement.