GaP introduces a Graph-as-Policy framework that enables multi‑agent self‑learning for Variational Automation (VA) tasks, which feature high geometric and pose variability. The approach blends interpretable robot programming with model‑free policy adaptability, closing the reliability gap for persistent, high‑variation industrial operations. By representing policies as graph structures, GaP facilitates scalable coordination among agents while preserving explainability.