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This paper introduces an agentic calibration method that uses a large language model as an optimizer for calibrating grey-box simulation models in cost-effectiveness analysis. The LLM-driven approach achieves competitive performance with substantially fewer model evaluations compared to traditional methods like Nelder-Mead and Bayesian optimization.