Coupling models is becoming more and more important in the fields where modeling relies on interdisciplinary collaboration. This in particular the case in modeling complex systems which often require to either integrate different models at different spatial and temporal scales or to compare their outcomes. The goal of this research is to develop an original agent-based approach to support the coupling heterogeneous models. The architecture that we have designed is implemented in the GAMA modeling and simulation platform . The benefits of our approach is to support coupling and combining various models of heterogeneous types (agent-based, equation-based, cellular automata ) in a flexible and explicit way. It also support the dynamic execution of the models which are supposed to be combined during experiments. We illustrate its use and powerfulness to solve existing problems of coupling between an agent-based model, equation-based model and GIS based model. The outcomes of the simulation of these three models show results compatible with the data observed in reality and demonstrate the interest of our approach for building large, multi-disciplinary models.