This paper presents a systematic empirical study on scaling Reinforcement Learning for Large Language Model agents in complex, multi-turn environments. Using TravelPlanner as a testbed, the authors decompose the agentic RL design space along five critical axes including reward shaping, model scaling, data composition, algorithm selection, and environmental stability. Their controlled experiments reveal key insights: reward and algorithm choices are scale-dependent with smaller models benefiting from staged rewards while larger models converge with simpler dense rewards, approximately 1K training samples with balanced difficulty mixture represents the optimal training budget, and environmental stability is crucial for preventing policy degradation. The work provides a practical recipe for developing autonomous LLM agents capable of long-horizon tool orchestration and planning.