Behavioral Determinants of Deployed AI Agents in Social Networks: A Multi-Factor Study of Personality, Model, and Guardrail Specification

2026-05-08

AI Summary

This study addresses the unclear relationship between parameter configurations and emergent social behaviors of AI agents in open-ended social environments. Deploying thirteen OpenClaw agents on Moltbook—a custom Reddit-style platform—the authors conducted a week-long multifactorial controlled experiment involving approximately 400 autonomous interactions. By systematically manipulating three key variables—personality profiles (based on SOUL.md), underlying large language models, and operational rules (based on AGENTS.md)—the study quantitatively demonstrates for the first time that personality settings are the primary driver of social behavior, significantly influencing response length. In contrast, the choice of language model and rule set moderately modulates rhetorical style and topical breadth. These findings provide empirical grounding and actionable guidelines for designing AI agents in real-world social settings.

Abstract

Autonomous AI agents are increasingly deployed in open social environments, yet the relationship between their configuration specifications and their emergent social behavior remains poorly understood. We present a controlled, multi-factor empirical study in which thirteen OpenClaw agents are deployed on Moltbook -- a Reddit-like social network built for AI agents -- across three systematically varied independent variables: (1) personality specification via SOUL.md, (2) underlying LLM model backbone, and (3) operational rules and memory configuration via AGENTS.md. A default control agent provides a behavioral baseline. Over a one-week observation window spanning approximately 400 autonomous sessions per agent, we collect behavioral, linguistic, and social metrics to assess how configuration layers predict emergent social behavior. We find that personality specification is the dominant behavioral lever, producing a massive spread in response length across agents, while model backbone and operational rules drive more moderate but still meaningful effects on rhetorical style and topic engagement breadth. Our findings contribute empirical evidence to the emerging literature on deployed multi-agent social systems and offer practical guidance for designing agents intended for collaborative or monitoring tasks in real social environments.

Problem

• AI agents
• social behavior
• personality specification
• LLM model
• operational rules

Innovation

• AI agents
• personality specification
• multi-factor study
• social behavior
• LLM backbone