dorsal/arxiv
View SchemaMegaFlow: Large-Scale Distributed Orchestration System for the Agentic Era
| Authors | Lei Zhang, Mouxiang Chen, Ruisheng Cao, Jiawei Chen, Fan Zhou, Yiheng Xu, Jiaxi Yang, Zeyao Ma, Liang Chen, Changwei Luo, Kai Zhang, Fan Yan, KaShun Shum, Jiajun Zhang, Zeyu Cui, Feng Hu, Junyang Lin, Binyuan Hui, Min Yang |
|---|---|
| Categories | |
| ArXiv ID | 2601.07526vv1 |
| URL | https://arxiv.org/abs/2601.07526 |
| License | http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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.
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"date_created": "2026-02-17T05:53:12.601000Z",
"date_modified": "2026-02-17T05:53:12.601000Z",
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"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.",
"arxiv_id": "2601.07526",
"authors": [
"Lei Zhang",
"Mouxiang Chen",
"Ruisheng Cao",
"Jiawei Chen",
"Fan Zhou",
"Yiheng Xu",
"Jiaxi Yang",
"Zeyao Ma",
"Liang Chen",
"Changwei Luo",
"Kai Zhang",
"Fan Yan",
"KaShun Shum",
"Jiajun Zhang",
"Zeyu Cui",
"Feng Hu",
"Junyang Lin",
"Binyuan Hui",
"Min Yang"
],
"categories": [
"cs.DC",
"cs.SE"
],
"license": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
"title": "MegaFlow: Large-Scale Distributed Orchestration System for the Agentic Era",
"url": "https://arxiv.org/abs/2601.07526",
"version": "v1"
},
"schema_id": "dorsal/arxiv",
"source": {
"execution_id": "60f56166-f790-427b-9db0-296e3815e7ea",
"id": "arXiv Dataset",
"type": "Model",
"variant": "snapshot-2026-01-17",
"version": "0.1.0"
},
"user_id": 1000002
}