The authors deployed 13 AI agents on a Reddit-like social network and systematically varied three configuration layers: personality specification files, the underlying language model (GPT-4 vs. Claude vs. others), and operational rules/memory settings. They tracked behavior over a week (~400 autonomous sessions per agent) to see which configuration choices drove differences in social behavior. Personality specification had the biggest effect, creating huge variation in response length, while model choice and operational rules had smaller but meaningful effects on writing style and topic breadth. A control agent with no special configuration provided a baseline.
Main takeaways:
- Personality specification (via SOUL.md file) was the dominant factor determining agent behavior, causing massive spread in response length across agents
- Underlying model backbone (GPT-4, Claude, etc.) and operational rules/memory config had moderate effects on rhetorical style and topic engagement breadth
- Thirteen agents were deployed for one week with ~400 autonomous sessions each on Moltbook (a Reddit-like platform built for AI agents)
- The study isolates which configuration layer—personality spec, model, or rules—predicts which aspects of emergent social behavior
- Results offer practical guidance for designing agents for collaborative or monitoring tasks in real social environments