A Systematic Approach to Multi-Agent AI from Advanced Regulatory Control Theory: Safe and Auditable LLM Operator Agents for Process Control

How can large language models (LLMs) be integrated into industrial process control systems without sacrificing safety or stability? The authors investigate whether the principles of Advanced Regulatory Control (ARC)—specifically the decomposition of control problems into priority-ordered feedback loops—can provide a robust framework for organizing LLM-based agents.

The authors propose a multi-agent architecture where each feedback loop in an ARC chain is mapped to a specialized LLM operator agent. These agents are responsible for classifying the operating regime (e.g., IDLE, ACTIVE, SATURATED) and providing a rationale for their actions. Crucially, the interaction logic (MIN/MAX selectors and override paths) is encapsulated in a deterministic orchestrator agent. This ensures that even if an LLM agent produces an suboptimal or incorrect proposal, the structural priority of the control chain is enforced, preventing unstable or unsafe behavior. The system was tested on a dairy-barn ventilation case study using Qwen 2.5 7B Instruct models running on a consumer GPU.

The study demonstrates that this multi-agent reformulation successfully reproduces the behavior of classical PI-based ARC networks while adding the benefit of an auditable, human-readable rationale for every control decision. By using specific architectural levers—such as deterministic-direction mapping and operator memory—the LLM agents can effectively manage constraints and settle at intermediate operating points without the need for manual PID gain tuning. The authors show that the decomposition is essential; a monolithic LLM controller without this structure fails to coordinate competing constraints, whereas the decomposed multi-agent system maintains structural integrity and safety.

This work provides a practical, safe, and auditable path for deploying LLMs in industrial settings. By grounding agent behavior in established control theory rather than relying on end-to-end learning, the architecture allows for the use of smaller, locally-hosted models that are secure, reproducible, and compliant with industrial safety standards.