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

TL;DR

This paper introduces OMC, a framework elevating multi-agent systems to an organisational level with dynamic recruitment, hierarchical decision-making, and self-improvement, enabling adaptable and self-organising AI companies.

Contribution

It proposes a novel organisational layer for multi-agent systems, integrating talent markets and hierarchical planning to improve flexibility and scalability.

Findings

OMC achieves an 84.67% success rate on PRDBench, surpassing previous methods.

The framework enables dynamic reconfiguration and self-improvement in multi-agent systems.

Empirical results demonstrate OMC's effectiveness across diverse domains.

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.