Multi-agent systems built on large language models (LLMs) require many coordination choices that are difficult to fix a priori: which skill protocol to invoke, which agent role should perform a subtask, which model to bind to each role, how roles should interact, when to use retrieval or verification, and when to omit a step entirely. These choices interact with task regime and operational constraints, so static pipelines and one-off model comparisons provide only a limited view of the design space. This paper introduces AgensFlow, an open-source framework that treats multi-agent coordination as an online policy-learning problem under partial observability. The framework makes coordination decisions observable and learnable from repeated trajectories, rather than treating skill, role, model, topology, and evaluation choices as fixed pipeline design. AgensFlow is evaluated on two corpora: distributed-systems incident tasks and security-advisory tasks. The evaluation shows three main results: learned routing reaches a higher-quality operating point than a fixed pipeline baseline on coordination-heavy classes; skip:X isolates topology compression as a meaningful part of the substrate; and warm-started policy graphs can reduce exploration cost while preserving plateau quality. Overall, the results support that learned, auditable routing can improve coordination-heavy multi-agent workflows over static wiring.

翻译：基于大语言模型构建的多智能体系统需要大量难以先验固定的协调选择：调用何种技能协议、由哪个智能体角色执行子任务、每个角色绑定何种模型、角色间如何交互、何时使用检索或验证、以及何时完全省略某个步骤。这些选择与任务范式及运行约束相互影响，因此静态流水线和单次模型比较仅能提供设计空间的有限视图。本文提出AgensFlow——一个将多智能体协调视为部分可观测环境下在线策略学习问题的开源框架。该框架使协调决策可观测且可通过重复轨迹进行学习，而非将技能、角色、模型、拓扑结构和评估选择视为固定流水线设计。AgensFlow在两个语料库上进行了评估：分布式系统故障任务和安全公告任务。评估结果呈现三个主要发现：在协调密集型任务中，学习式路由比固定流水线基线能达到更高质量的工作点；skip:X机制将拓扑压缩作为基板中的有意义的组成部分；热启动策略图可在保持平台期质量的同时降低探索成本。总体而言，研究结果支持学习型、可审计路由能够改善静态连线下协调密集型的多智能体工作流。