Networks are fundamental for our understanding of complex systems. Interactions between individual nodes in networks generate network motifs - small recurrent patterns that can be considered the network's building-block components, providing certain dynamical properties. However, it remains unclear how network motifs are arranged within networks and what properties emerge from interactions between network motifs. Here we develop a framework to explore the mesoscale-level behavior of complex networks. Considering network motifs as hyper-nodes, we define the rules for their interaction at the network's next level of organization. We infer the favorable arrangements of interactions between network motifs into hyper-motifs from real evolved and designed networks data including biological, neuronal, social, linguistic and electronic networks. We mathematically explore the emergent properties of these higher-order circuits and their relations to the properties of the individual minimal circuit components they combine. This framework provides a basis for exploring the mesoscale structure and behavior of complex systems where it can be used to reveal intermediate patterns in complex networks and to identify specific nodes and links in the network that are the key drivers of the network's emergent properties.