The development of intelligent agents – particularly those powered by large language models (LLMs) – has emphasized the critical role of environments in shaping agent behavior and capabilities, especially for achieving end-to-end autonomy. Environments are not merely testing grounds; they are dynamic, interactive contexts that serve as the essential "data" for agents to learn adaptive behavior, complex reasoning, and long-term decision-making skills. Just as scaling the model size, dataset size, and training computation has led to emergent capabilities in LLMs, scaling the structure, fidelity, and diversity of environments is one of the crucial dimensions in advancing agent intelligence. Moreover, recent advances in end-to-end reinforcement learning (RL), particularly when paired with LLM-based agents, have made it increasingly viable to train agents through sustained interaction. These agents can now acquire skills, strategies, and planning abilities through environmental feedback, rather than relying solely on imitation learning or static prompt engineering. As we move toward more autonomous, general-purpose agents, the need for scalable, richly interactive, and diverse environments has become both urgent and foundational.