A new framework, Agentic Real2Sim, enables physics-based world modeling with vision-language agents, streamlining real-to-simulation conversion for robotic interaction with objects. It integrates visual perception tools and simulators, reducing manual tuning and improving conversion success rates. The framework supports custom scene conversion, fine-tuning of pre-trained policies, and surrogate policy evaluation.