arXiv：cs.AI（全量分类）
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从技能到人才：将异构智能体组织为现实世界的公司
精选理由
这篇论文把多智能体系统从固定的管道变成了一个自组织、可进化的『AI公司』，对做Agent框架的人来说，这个『组织层』的抽象和动态招聘机制是个值得借鉴的新思路。
AI 摘要
研究提出 OneManCompany (OMC) 框架，将多智能体系统提升至组织层面。该框架将技能、工具与配置封装为可移植的“人才”身份，通过类型化接口协调异构后端，并借助社区驱动的“人才市场”实现按需招募，动态弥补能力缺口。组织决策通过“探索-执行-评审”树搜索实现，将规划、执行与评估统一为分层循环，并提供终止与无死锁的形式化保证。在 PRDBench 上的实验显示，OMC 达到 84.67% 的成功率，较现有最佳技术提升 15.48 个百分点，跨领域案例验证了其通用性与自组织能力。
原文 · 保持原样，未翻译
Computer Science > Artificial Intelligence
[Submitted on 24 Apr 2026]
Title:From Skills to Talent: Organising Heterogeneous Agents as a Real-World Company
View PDF HTML (experimental)Abstract: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.
| Comments: | |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2604.22446 [cs.AI] |
| (or arXiv:2604.22446v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2604.22446 arXiv-issued DOI via DataCite |
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