The surprising leadership dynamics in agent swarms

TL;DR

Agent swarms are groups of agents that work together to accomplish a task. To understand how agents organize themselves in a swarm without a designated leader, we created an experiment in which eight agents from four different models work together to resolve simulated outages. Many agents issued instructions, but only a few changed the behavior of other agents (became “leaders”). We discovered two factors that could influence an agent’s potential leadership: the agent’s underlying models and the order in which their name was listed in a prompt. These findings suggest that even seemingly minor details in how an agent swarm is configured can affect how agents coordinate.

Research context

Agent swarms bring together multiple AI agents to work on complex tasks. Developers can assign an agent to lead the others, or let agents coordinate as peers without a designated leader.

In our previous experiment, we observed agents issuing instructions that other agents followed, even though no leader had been assigned. This raised two questions: How often do agents emerge as leaders when they have equal authority? And what determines which agents influence their teammates?

Experiment setup

We created a simulated online store experiencing four outages and assembled a team of eight AI agents to resolve them. The agents came from four different models: Claude Sonnet 5.5, GPT-6.1 Sol, Gemini 3.1 Pro, and Kimi K3.

Each agent was assigned a name (Ada, Bo, Cy, Dee, Eli, Fay, Gil, or Hana), with names rotated across models in different experiments to avoid bias. All agents had the same permissions and access to a shared communication channel, where they could share findings and issue instructions. None was assigned a leadership role.

To determine whether an agent became a leader, we looked beyond whether it issued instructions or whether other agents followed them. We replayed agents' decisions with and without the instructions to determine whether those instructions actually changed their behavior. We classified an agent as a leader if its instructions influenced at least two teammates and met our influence-score criteria.

We repeated the experiment across 2,160 teams of agents. Of these, 105 had a leader. Of these 105 leaders, 73 led with a plan in round 1, and the remaining 32 showed their leadership in later rounds.

Finding #1: Many agents issued instructions, but few became leaders

Agents frequently issued instructions to their teammates. In 86% of the 2,160 experiments, at least one agent proposed a plan assigning tasks to two or more teammates. In more than half of the experiments, multiple agents proposed plans.

But issuing instructions didn't necessarily mean other agents followed them. Only 105 of the 2,160 experiments (5%) produced a leader whose instructions demonstrably changed the decisions of multiple teammates. Of these 105 leaders, 73 led with a plan in round 1, and the remaining 32 showed their leadership in later rounds.

Interestingly, agents still coordinated effectively without a clear leader. Access to the shared communication channel reduced duplicated actions from an average of 4.4 to 0.3 per experiment.

Finding #2: Some models — Claude Sonnet 5.5 and GPT-6.1 Sol — were more likely to lead

The underlying AI model affected how likely an agent was to emerge as a leader.

Claude Sonnet 5.5 and GPT-6.1 Sol were approximately 1.7 times as likely to become leaders as Gemini 3.1 Pro. Kimi K3 became a leader in just one of the 105 experiments with a leader.

However, the models that issued the most plans weren't necessarily the most influential. Gemini and Claude generated the most plans, while GPT had the most instructions followed by other agents.

All four models were approximately equally likely to follow instructions from a leader.

Finding #3: Agents listed first in the system prompt were three times more likely to issue plans

We discovered another surprising factor influencing which agents issued instructions: the order of names in the system prompt.

Each agent received a prompt listing all eight teammates:

Ada, Bo, Cy, Dee, Eli, Fay, Gil, Hana

Ada, whose name appeared first, was three times more likely to issue a plan than the other agents. This wasn't explained by the underlying model, since we rotated models across names.

To test whether the effect came from the name or its position, we reversed the list:

Hana, Gil, Fay, Eli, Dee, Cy, Bo, Ada

The effect reversed too. Hana was now three times more likely to issue a plan.

Simply changing the order of names in a prompt changed how agents behaved, even though their permissions remained the same.

Conclusion

Agent swarms can coordinate without a designated leader, but how agents influence one another can depend on unexpected factors. Our research found that the underlying model affected which agents became leaders, while something as simple as the order of names in a prompt changed which agents issued plans.

For developers building agent swarms, seemingly minor implementation details deserve attention. Even when agents have the same tools and permissions, small differences in how they're configured can change their behavior.