This paper proposes an agentic AI framework for intent-driven optimization in cell-free Open Radio Access Networks (O-RAN), where LLM-based agents collaborate to translate operator intents into network optimizations. The framework demonstrates significant reductions in active O-RUs for energy saving and memory usage through parameter-efficient fine-tuning.