Image retouching requires sophisticated aesthetic understanding and precise parameter control, yet existing agents typically rely on fixed, open-loop execution or software-specific supervision. We introduce AdaRetouch, an interleaved vision-language agent for adaptive, multi-turn image retouching. Given an instruction and an image, AdaRetouch dynamically diagnoses the visual state, invokes non-destructive tools, and iteratively observes the results until the intent is satisfied. To train this closed-loop behavior, we propose a software-agnostic pipeline that constructs trajectories featuring curriculum-driven tool sequences and step-wise reasoning. AdaRetouch is initialized via supervised fine-tuning and further optimized using process-level reinforcement learning for quality and efficiency. Experiments on AdaRetouch-Bench show that AdaRetouch outperforms general MLLMs, existing agents, and diffusion editors in intent alignment, perceptual quality, and calibrated tool planning.