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

• URL: http://arxiv.org/abs/2604.22446v1
• Date: Fri, 24 Apr 2026 11:02:44 GMT
• Title: From Skills to Talent: Organising Heterogeneous Agents as a Real-World Company
• Abstract summary: We introduce emphOneManCompany (OMC), a framework that elevates multi-agent systems to the organisational level.<n>OMC encapsulates skills, tools, and runtime configurations into portable agent identities called emphTalents<n>A community-driven emphTalent Market enables on-demand recruitment, allowing the organisation to close capability gaps.
• Score: 37.358554029394604
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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