This study investigates inquiry and scaffolding patterns between students and MathPal, a math AI agent, during problem-solving tasks. Using qualitative coding, lag sequential analysis, and Epistemic Network Analysis, the study identifies distinct interaction profiles, revealing how personalized AI feedback shapes student learning behaviors and inquiry dynamics in mathematics problem-solving activities.