Agents with Feelings? Personality and Emotion in Multi-Agent Software Teams

2026-07-06

AI Summary

This study addresses a critical gap in existing large language model–based multi-agent software engineering systems, which typically differentiate agents solely by role and workflow while neglecting the impact of behavioral traits such as personality and emotion on team performance. To bridge this gap, the work introduces a psychology-driven behavioral modeling framework that integrates the Big Five personality traits, basic emotions, software engineering work styles, and task-specific roles. The authors systematically evaluate 78 agent configurations across code generation and code review tasks. Results demonstrate that heterogeneous behavioral configurations outperform the best homogeneous setups in six out of eight model–task combinations, with performance gains ranging from 7.1 to 11.3 percentage points. Although high conscientiousness or fear increases revision counts and token consumption, it does not consistently enhance performance. This research establishes behavioral diversity as a key factor in collaborative efficiency and proposes a new paradigm of mixed behavioral configurations.

Abstract

Multi-agent LLM systems for Software Engineering (SE) typically differentiate agents through roles and workflows, but little is known about how agents' behavioral profiles affect team performance. We investigate the impact of personality and emotion profiles on LLM agent teams using a psychology-informed framework that combines Big Five personality traits, basic emotions, SE-relevant work styles, and task roles. We evaluate 78 team-profile configurations across code generation and code review using four LLMs and 659 task instances. Results show that profile choice substantially affects both performance and team behavior. For code generation, the gap between the best and worst shared-profile configurations reaches 7.1-11.3 percentage points in pass@1 across models, while the best mixed-profile configuration outperforms the best shared-profile configuration in six of eight model-task settings. Profiles also influence collaboration dynamics and cost: fear and high-conscientiousness profiles increase revision activity, over-revision, and token usage without consistent performance gains. These findings identify agent profiles as an important design dimension in multi-agent SE systems, affecting not only task outcomes but also the efficiency of collaboration.

Problem

• multi-agent systems
• personality
• emotion
• software engineering
• LLM agents

Innovation

• personality-aware agents
• emotion modeling
• multi-agent LLM systems
• software engineering teams
• behavioral profiles