Batch Processing to Create 100+ AI Agent Configurations

This article discusses an automated approach to generating configurations for hundreds of AI agents in a social media simulation, using large language models (LLMs) and a multi-step pipeline process.

Details

Generating configurations for hundreds of AI agents in a social media simulation requires systematically defining various attributes like time, events, activity patterns, and response delays. Manually processing this can be time-consuming and error-prone. The article presents an approach using large language models (LLMs) to automate this configuration generation process. However, LLM limitations such as truncated output, JSON format errors, and token limits necessitate a multi-step pipeline strategy. This includes: 1) Staged configuration generation (time -> events -> agents -> platform), 2) Batch processing to avoid context overload, 3) JSON recovery and error handling, 4) Applying rule-based fallbacks when LLM fails, 5) Applying activity pattern templates for different agent types, and 6) Validating and auto-correcting the generated values. The pipeline architecture and file structure are detailed, showcasing how this approach can scale to 100+ agents while handling LLM constraints.