When Does Personality Composition Matter for Multi-Agent LLM Teams?

2026-06-25

AI Summary

This study investigates the impact of personality prompting on task performance in large language model (LLM)-based multi-agent teams, with a particular focus on the moderating role of task structure. By manipulating personality traits such as agreeableness across three distinct task types—structured programming, open-ended research collaboration, and competitive negotiation—the authors systematically evaluate personality composition effects using a multi-agent simulation framework and cross-domain benchmarks. The findings reveal that personality cues exert minimal influence in coding tasks but significantly impair performance in open collaboration and negotiation scenarios, thereby challenging the assumption of universal efficacy for personality-based interventions. Although low agreeableness alters communication patterns, it does not affect the achievement of coding milestones. These results provide critical empirical insights for designing personality attributes in multi-agent systems.

Abstract

Personality prompting shapes how large language models communicate, yet whether these behavioral shifts affect objective task outcomes remains under-explored. Prior work shows that agents prompted with low agreeableness produce adversarial language, while those prompted with high agreeableness become cooperative, but the relationship between communication style and task performance has not been systematically examined across multiple domains. In this work, we investigate whether personality composition matters for multi-agent team performance by manipulating personality traits across frontier LLMs on three task domains: structured coding, open-ended research collaboration, and competitive bargaining. We find that personality effects depend critically on task structure. In coding tasks, low agreeableness leads to large communication shifts that have little effect on milestone completion. In open-ended collaboration and bargaining, the same manipulation substantially degrades performance. We discuss implications for multi-agent system design and the limits of personality manipulation.

Problem

• personality composition
• multi-agent LLM teams
• task performance
• communication style
• agreeableness

Innovation

• personality prompting
• multi-agent LLM teams
• task structure
• agreeableness
• communication style