The general goal of using multi agent networks for complex problem solving is the maximisation of the quality of the result to be obtained at minimum cost. Both the granularity of the agent society and the competence assigned to each individual agent determine the information flow in the network. The great number of parameters involved make it difficult for the designer to optimally adapt the structure of the network to a given class of tasks. In this paper we outline possible network structures and present an approach for determining a number of important statistical parameters characterising the network at a relatively abstract level. The abstraction enables a comparison of different network structures. The methods for the analysis may, however, be readily refined to evaluate a specific problem. As an example we discuss the use of the multiagent paradigm for structuring the cooperation of sensor networks in robotics. Our analysis is supplemented by simulation results, which prove a superiority of lateral over pure hierarchical coordination, particularly under severe environmental conditions, such as agent failure.