Title: MegaFlow: Large-Scale Distributed Orchestration System for the Agentic Era
Primary area: infrastructure, software libraries, hardware, systems, etc.
Keywords: Large Language Models, Reinforcement Learning, Agentic Coding

Abstract: 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 existing 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.