NetLogo simulation engine generates synthetic data that feeds into a regression model for predictive analysis, which then informs an LLM-based policy generator. The pipeline ingests simulation outputs and external policy constraints, processes them through statistical regression to identify patterns, and serves refined policy proposals. This architecture demonstrates how agent-based modeling can augment machine learning workflows to generate evidence-backed policy recommendations. Fork this diagram on Diagrams.so to customize the regression model, swap the LLM component, or integrate live policy feedback loops.