Agent-based models (ABMs) and state-and-transition simulation models (STSMs) are two classes of simulation models that have proven useful for understanding the processes underlying complex, dynamic ecosystems and evaluating practical questions about how ecosystems will respond to different scenarios of global change and environmental management. ABMs can simulate many types of agents (i.e., autonomous units, such as wildlife, livestock, people, or viruses) and are advantageous because they can represent agent characteristics, decision-making, adaptive behavior, mobility, and interactions, and can capture feedbacks between agents and their environment. STSMs are flexible and intuitive models of landscape dynamics that can track landscape attributes and management scenarios, and integrate diverse data types (e.g., output from correlative and mechanistic models). Both ABMs and STSMs can be run spatially and track important metrics of management success, including costs. Despite the complementarity of these two approaches, they have not been connected through a dynamic linkage until now. We report on analytical techniques and software tools that we developed to couple these modeling approaches using NetLogo, R, and the ST-Sim package for SyncroSim. We demonstrate the capabilities and value of this new approach through a proof-of-concept modeling example focused on bison-vegetation interactions in Badlands National Park. This coupled approach: 1) streamlines handling of model inputs and outputs; 2) increases the temporal resolution of agent-environment interactions that are available in ST-Sim; 3) minimizes assumptions; and 4) generates more realistic spatio-temporal patterns. With the developments presented here, modelers can now use output from an ABM to dictate changes in vegetation and their characteristics within an STSM, and create more realistic and management-relevant simulations.