Advanced agentic intelligence is a prerequisite for deploying Large LanguageModels in practical, real-world applications. Diverse real-world APIs demandprecise, robust function-calling intelligence, which needs agents to developthese capabilities through interaction in varied environments. The breadth offunction-calling competence is closely tied to the diversity of environments inwhich agents are trained. In this work, we scale up environments as a steptowards advancing general agentic intelligence. This gives rise to two centralchallenges: (i) how to scale environments in a principled manner, and (ii) howto effectively train agentic capabilities from experiences derived throughinteractions with these environments. To address these, we design a scalableframework that automatically constructs heterogeneous environments that arefully simulated, systematically broadening the space of function-callingscenarios. We further adapt a two-phase agent fine-tuning strategy: firstendowing agents with fundamental agentic capabilities, then specializing themfor domain-specific contexts. Extensive experiments on agentic benchmarks,tau-bench, tau2-Bench, and ACEBench, demonstrate that our trained model,AgentScaler, significantly enhances the function-calling capability of models.