从技能到人才：将异质智能体组织成现实企业

摘要

尽管个体智能体能力通过模块化技能与工具集成已实现飞速发展，但多智能体系统仍受限于固定的团队结构、紧耦合的协调逻辑以及会话绑定的学习模式。我们认为这反映出更深层次的缺失：一个能够管理智能体工作队伍如何组建、治理并持续优化的组织化层级，该层级应与个体智能体的知识体系解耦。为填补这一空白，我们提出OneManCompany（OMC）框架，将多智能体系统提升至组织化层面。OMC将技能、工具与运行时配置封装为可移植的智能体身份——人才单元，并通过类型化组织接口对异构后端进行抽象编排。社区驱动的人才市场支持按需招募，使组织能够在执行期间动态弥补能力缺口并重构自身。组织决策通过探索-执行-评审（E²R）树搜索实现操作化，该机制将规划、执行与评估统一于分层循环中：任务自上而下分解为可问责单元，执行结果自下而上聚合以驱动系统性评审与优化。此循环既提供终止性与无死锁的形式化保证，又镜像了人类企业的反馈机制。这些创新共同将多智能体系统从静态预配置流程转变为能够适应跨领域开放任务的自组织、自优化的AI组织。在PRDBench上的实证评估表明，OMC实现84.67%的成功率，较现有最优技术提升15.48个百分点，跨领域案例研究进一步验证了其普适性。

English

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 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 Talents, orchestrated through typed organisational interfaces that abstract over heterogeneous backends. A community-driven 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 Explore-Execute-Review (E^2R) 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.

从技能到人才：将异质智能体组织成现实企业

From Skills to Talent: Organising Heterogeneous Agents as a Real-World Company

摘要

Support