This paper presents AGILE, an end-to-end workflow designed to address the challenges of transferring reinforcement learning policies from simulation to real humanoid robots. The framework standardizes the policy-development lifecycle through four key stages: interactive environment verification, reproducible training, unified evaluation, and descriptor-driven deployment. By mitigating common sim-to-real failure modes, AGILE enables systematic development of loco-manipulation skills for humanoid robots. The approach includes scenario-based tests and randomized rollouts under motion-quality diagnostics for automated regression testing and robustness assessment.