Agent-based models explain how micro-level rules produce macro-level patterns, yet linking them to data remains a challenge. This paper presents a protocol for inferring latent agent variables through probabilistic modeling and gradient-based expectation maximization, demonstrated on a housing market simulation. The method enhances predictive accuracy and interpretability, promoting a data-driven way to calibrate generative models of collective behavior.