The rapid development of interactive and autonomous AI systems signals our entry into the agentic era. Training and evaluating agents on complex agentic tasks such as software engineering and computer use requires not only efficient model computation but also sophisticated infrastructure capable of coordinating vast agent-environment interactions. However, no open-source infrastructure can effectively support large-scale training and evaluation on such complex agentic tasks. To address this challenge, we present MegaFlow, a large-scale distributed orchestration system that enables efficient scheduling, resource allocation, and fine-grained task management for agent-environment workloads. MegaFlow abstracts agent training infrastructure into three independent services (Model Service, Agent Service, and Environment Service) that interact through unified interfaces, enabling independent scaling and flexible resource allocation across diverse agent-environment configurations. In our agent training deployments, MegaFlow successfully orchestrates tens of thousands of concurrent agent tasks while maintaining high system stability and achieving efficient resource utilization. By enabling such large-scale agent training, MegaFlow addresses a critical infrastructure gap in the emerging agentic AI landscape.

翻译：交互式与自主人工智能系统的快速发展标志着我们正迈入智能体时代。在软件工程与计算机使用等复杂智能体任务上训练与评估智能体，不仅需要高效的模型计算，更需要能够协调海量智能体-环境交互的复杂基础设施。然而，目前尚无开源基础设施能有效支持此类复杂智能体任务的大规模训练与评估。为应对这一挑战，我们提出了MegaFlow——一个面向智能体-环境工作负载的大规模分布式编排系统，能够实现高效的调度、资源分配与细粒度任务管理。MegaFlow将智能体训练基础设施抽象为三个独立服务（模型服务、智能体服务与环境服务），这些服务通过统一接口进行交互，从而实现在多样化智能体-环境配置中的独立扩展与灵活资源分配。在我们的智能体训练部署中，MegaFlow成功协调了数万个并发智能体任务，同时保持了较高的系统稳定性并实现了高效的资源利用率。通过支持如此大规模的智能体训练，MegaFlow填补了新兴智能体人工智能领域的关键基础设施空白。