MegaFlow: Large-Scale Distributed Orchestration System for the Agentic Era

TL;DR

MegaFlow is a scalable distributed system designed to efficiently orchestrate large-scale agent training and evaluation, addressing infrastructure gaps in the emerging agentic AI era.

Contribution

It introduces a novel architecture that separates services for model, agent, and environment management, enabling flexible scaling and resource allocation for complex agentic tasks.

Findings

Orchestrates tens of thousands of concurrent agent tasks

Maintains high system stability during large-scale operations

Achieves efficient resource utilization in agent training

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 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.