Agentic AI in Commodity Trading: A Comparative Simulation Study

Agent Based Modeling (ABM) has long been used to study emergent market behavior, but most prior financial ABM frameworks rely on reactive rule-based or reinforcement learning agents with limited cognitive capability. This study introduces a novel integration of agentic artificial intelligence (AI) featuring autonomous goal setting, persistent memory, and multi-step planning into commodity trading simulations. We develop a hybrid ABM-Agentic AI framework and comparatively evaluate 20 traditional agents and 20 Agentic AI agents across Natural Gas and WTI Crude Oil markets over multiple horizons (1M–3Y). To address external validity concerns, synthetic price series are calibrated to historical volatility regimes. Results show consistent performance improvements for Agentic AI, with large practical effect sizes, although statistical significance is limited due to small sample sizes. We also identify sources of potential bias, such as higher initial skill ranges and frictionless execution, and present controlled adjustments to mitigate them. The study makes four contributions: 1) a novel simulation architecture for integrating cognitive AI into ABM; 2) explicit operationalization of agentic capabilities; 3) a controlled comparative evaluation across commodities; and 4) robustness checks examining sensitivity to volatility and parameter shifts. Limitations and recommendations for real data validation and realistic microstructure modeling are also discussed.