通过模块化技能和工具集成，单个代理的能力已迅速提高,，但多代理系统仍然受到固定团队结构, 紧密耦合的协调逻辑, 和会话绑定学习的限制。我们认为，这反映了更深层次的缺失: 一个原则性的组织层，它管理着代理人员如何组装, 进行管理, 并随着时间的推移而改进, 与个体代理所了解的知识脱钩。为了填补这一空白,，我们引入了 \emph{OneManCompany (OMC)}, 一个将多代理系统提升到组织级别的框架。 OMC 将技能, 工具, 和运行时配置封装到名为\emph{Talents}, 的可移植代理身份中，通过在异构后端上抽象的类型化组织接口进行编排。社区驱动的 \emph{Talent Market} 支持按需招聘,，使组织能够缩小能力差距并在执行过程中动态重新配置自身。组织决策通过 \emph{探索-执行-审查} ($\text{E}^2$R) 树搜索, 进行操作，它将计划, 执行, 和评估统一在单个分层循环中: 任务自上而下分解为负责单元，执行结果自下而上聚合，以推动系统审查和细化。这个循环提供了终止和死锁自由的正式保证，同时反映了人类企业的反馈机制。这些贡献共同将, 多代理系统从静态, 预配置管道转变为能够适应跨不同领域的开放式任务的自组织和自我改进的人工智能组织。 PRDBench 的实证评估表明，OMC 实现了 $84.67\%$ 成功率,，超出了现有技术 $15.48$ 个百分点,，跨领域案例研究进一步证明了其通用性。
Individual agent capabilities have advanced rapidly through modular skills and tool integrations, yet multi-agent systems remain constrained by fixed team structures, tightly coupled coordination logic, and session-bound learning. We argue that this reflects a deeper absence: a principled organisational layer that governs how a workforce of agents is assembled, governed, and improved over time, decoupled from what individual agents know. To fill this gap, we introduce \emph{OneManCompany (OMC)}, a framework that elevates multi-agent systems to the organisational level. OMC encapsulates skills, tools, and runtime configurations into portable agent identities called \emph{Talents}, orchestrated through typed organisational interfaces that abstract over heterogeneous backends. A community-driven \emph{Talent Market} enables on-demand recruitment, allowing the organisation to close capability gaps and reconfigure itself dynamically during execution. Organisational decision-making is operationalised through an \emph{Explore-Execute-Review} ($\text{E}^2$R) tree search, which unifies planning, execution, and evaluation in a single hierarchical loop: tasks are decomposed top-down into accountable units and execution outcomes are aggregated bottom-up to drive systematic review and refinement. This loop provides formal guarantees on termination and deadlock freedom while mirroring the feedback mechanisms of human enterprises. Together, these contributions transform multi-agent systems from static, pre-configured pipelines into self-organising and self-improving AI organisations capable of adapting to open-ended tasks across diverse domains. Empirical evaluation on PRDBench shows that OMC achieves an $84.67\%$ success rate, surpassing the state of the art by $15.48$ percentage points, with cross-domain case studies further demonstrating its generality.
科目:人工智能(cs.AI)
Subjects: Artificial Intelligence (cs.AI)