This paper describes the design of an agent-based modelling framework for high performance computing. Rather than a collection of methods that require parallel programming expertise the framework presented allows modellers to concentrate on the model while the framework handles the efficient execution of simulations. The framework uses a state machine based representation of agents that allows a statically calculated optimal ordering of agent execution and parallel communication routines. Some experiments with the current implementation and the results of using a simple communication dominant model for benchmarking performance are reported. The model with half a million agents is used to show that a parallel efficiency of above 80% is achievable when distributed over 432 processors. Future improvements are discussed including data dependency analysis, vector operations over agents, and dynamic task scheduling.