Agent-Based Modelling and Simulation (ABMS) is widely used in System-of-Systems (SoS) studies to represent constituent systems operating in dynamic environments.However, the absence of standardised agent architectures hinders scalability, behaviour trace-ability, and comparative evaluation of emergence, coordination, and operational perfor-mance. This work introduces a modular agent design based on Observe–Orient–Decide–Act(OODA) loops as a framework for SoS simulations, enabling transparent information flow andArtificial Intelligence (AI) integration at the decision layer. A wildfire suppression scenariois used for the study, modelling firefighting crews, aircraft, helicopters, bulldozers, and anincident commander as OODA-driven agents. Results show that OODA-based agents exhibitcoherent and traceable team behaviours, adaptive task switching, and communication-drivencoordination, supporting the study of emergence and interoperability in SoS operations. TheDecision stage is designed for future improvements in the form of learning-based AI and LargeLanguage Models to enhance autonomy and operational fidelity.