Drop the Hierarchy and Roles: How Self-Organizing LLM Agents Outperform Designed Structures

TL;DR

This study demonstrates that self-organizing multi-agent LLM systems can spontaneously develop roles and outperform traditional hierarchical coordination, especially with capable models and minimal structural scaffolding.

Contribution

It provides extensive empirical evidence that emergent self-organization in LLM agents enhances performance without pre-defined roles, scaling effectively up to 256 agents.

Findings

Self-organizing agents spontaneously invent roles and form shallow hierarchies.

Hybrid protocols outperform centralized coordination by 14%.

Emergent autonomy scales with model capability and system size.

Abstract

How much autonomy can multi-agent LLM systems sustain -- and what enables it? We present a 25,000-task computational experiment spanning 8 models, 4--256 agents, and 8 coordination protocols ranging from externally imposed hierarchy to emergent self-organization. We observe that autonomous behavior already emerges in current LLM agents: given minimal structural scaffolding (fixed ordering), agents spontaneously invent specialized roles, voluntarily abstain from tasks outside their competence, and form shallow hierarchies -- without any pre-assigned roles or external design. A hybrid protocol (Sequential) that enables this autonomy outperforms centralized coordination by 14% (p<0.001), with a 44% quality spread between protocols (Cohen's d=1.86, p<0.0001). The degree of emergent autonomy scales with model capability: strong models self-organize effectively, while models below a capability threshold still benefit from rigid structure -- suggesting that as foundation models improve, the scope for autonomous coordination will expand. The system scales sub-linearly to 256 agents without quality degradation (p=0.61), producing 5,006 unique roles from just 8 agents. Results replicate across closed- and open-source models, with open-source achieving 95% of closed-source quality at 24x lower cost. The practical implication: give agents a mission, a protocol, and a capable model -- not a pre-assigned role.