Researchers introduced Monte Carlo Query Search (MCQS), a method for learning symbolic stochastic capability models of black-box AI agents. MCQS evaluates an agent's capabilities in novel settings by synthesizing queries that distinguish between optimistic and pessimistic models. Experiments show MCQS learns accurate models more efficiently than baseline strategies, enabling systematic characterization of agent capability boundaries.