The Resource Friendly Experiment Agent

Augmenting large language models (LLMs) with external tools has proven to be effective for producing consistent, deterministic results. While capabilities of agentic AI systems are growing rapidly and the integration into various domain application speeds up, their resource consumption finds little attention in literature. With continuously more capable models requiring more energy, coupled with ever increasing numbers of users, there is a need for alternative solutions. We present an approach of using small-scale open-source models locally powering an agentic AI system in the context of production planning. By defining and iteratively executing experiments, the system can optimize discrete-event simulation models - while running entirely on-device. With significant drawbacks in speed and performance, the local configuration consumes only a fraction of the estimated resources of state-of-the-art models. By offering further benefits in data sovereignty, operational independence and cost control, the concept of locally deployed models presents an attractive alternative for industry-grade manufacturing applications.