Generating 100+ Agent Configurations Using LLMs with Batch Processing

This article discusses an automated pipeline for generating detailed configurations for hundreds of AI agents in a social simulation, using large language models (LLMs) and batch processing to overcome context limitations.

Details

Configuring hundreds of AI agents for a social simulation can be a daunting task, requiring detailed settings for each agent's activity schedules, posting frequencies, response delays, influence weights, and stances. Manually creating these configurations is infeasible and time-consuming. The article presents an automated pipeline that leverages LLMs to generate the required configurations. To overcome the context limitations of LLMs, the process is divided into step-by-step stages, with batch processing to handle large numbers of agents. The pipeline also includes strategies to repair JSON outputs when the LLM generates truncated or invalid responses, as well as fallback rule-based configurations to handle LLM failures. The system also incorporates agent activity patterns based on different user types (e.g., student, official, media) to ensure realistic and diverse agent behaviors.