To support emerging low-altitude economic activities such as urban monitoring, unmanned aerial vehicles acting as non-terrestrial access points (NT-APs) play a pivot role in providing high data-rate communication and high-resolution sensing services in non-terrestrial networks (NTNs). To tackle optimization for matching spatial-temporally varying resources and demands, NT-APs can utilize artificial intelligence (AI) models to enable near-real-time decision-making. Nevertheless, due to the partial observability, those NT-APs as AI-agents struggle to actively perform long-horizon adaptability to the dynamic environment. Toward optimizing the overall performance, the agentic AI technology with coordination to AI-agents in different levels is a promising remedy to orchestrate communication, sensing services and resource control models. To this end, we propose a multi-AI-agent coordinated architecture to empower intelligent resource optimization in cell-free communication and sensing (C&S) services, in which the C&S service clustering and resource allocation are tackled by AI-agents at central processing units and NT-APs, respectively. By leveraging multi-agent rollout training and cross-task information exchange, AI-agents can tackle individual sub-problems such as trajectory design and beam management in a nearly-stationary environment. To enable continuous policy improvement, model-based planning is conducted using online-collected sensing results of unexplored environments. Finally, simulations on the obstacle-avoidance task of air traffic management demonstrate the effectiveness of the proposed design over the centralized evolutionary algorithm and centralized-training-and-decentralized-execution deep reinforcement learning (DRL).