In the CityScope platform, we leverage humanized Agent-Based Models (h-ABMs) to elevate urban simulation to new heights of realism and complexity. Unlike traditional ABMs, which often rely on rigid rules and probabilities, our h-ABMs incorporate realistic choices, learning capabilities, social connections, and diversity. This sophisticated approach allows for a more nuanced understanding of urban dynamics and decision-making processes. By simulating how diverse individuals and groups interact within urban environments, h-ABMs facilitate the exploration of complex social phenomena and potential interventions in a more detailed and dynamic way. Integrating these advanced features into CityScope enables us to model and analyze urban scenarios with unprecedented accuracy, paving the way for more informed urban planning and policy development.