This paper presents a framework for the development of agent-based models aimed at facilitating parameter space exploration by means of established parameter tuning strategies. Such simulations often require a high number of parameters to account for the complexity of the underlying processes. It is often the case that parameter values are not known, or that when they are known measurements are reported with a large margin of error. Despite this, publications in the eld often rely on single values rather than considering larger search spaces for their parameters. It is therefore uncertain whether results obtained are an artifact of a very specic combination of parameter values or truly representative of the underlying phenomenon. Our solution is applicable to any sort of agent-based model and can easily be expanded to incorporate further parameter tuning algorithms. We then tested our framework by reproducing an existing model of angiogenesis and exploring changes in simulation results across parameter values. Our case-study results suggest the aforementioned model is highly sensitive to the choice of parameter values, with even small changes in these causing signicant divergences in results