Agentic Driving Coach: Robustness and Determinism of Agentic AI-Powered Human-in-the-Loop Cyber-Physical Systems

2026-04-13

AI Summary

This work addresses the nondeterminism arising in human-in-the-loop cyber-physical systems due to human behavior, uncertainties in AI agents, and dynamic environments. It introduces, for the first time, the Reactor Model of Computation (Reactor MoC) into this domain, leveraging the Lingua Franca framework to construct a deterministic system architecture that integrates large language model–driven AI agents. Using an “intelligent driving coach” as a validation case study, the approach identifies and mitigates key challenges undermining system determinism, thereby significantly enhancing controllability and robustness. The proposed methodology offers a viable pathway toward restoring determinism in human-in-the-loop systems powered by AI agents.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Multiagent Systems: Modeling other Agents

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Foundation models, including large language models (LLMs), are increasingly used for human-in-the-loop (HITL) cyber-physical systems (CPS) because foundation model-based AI agents can potentially interact with both the physical environments and human users. However, the unpredictable behavior of human users and AI agents, in addition to the dynamically changing physical environments, leads to uncontrollable nondeterminism. To address this urgent challenge of enabling agentic AI-powered HITL CPS, we propose a reactor-model-of-computation (MoC)-based approach, realized by the open-source Lingua Franca (LF) framework. We also carry out a concrete case study using the agentic driving coach as an application of HITL CPS. By evaluating the LF-based agentic HITL CPS, we identify practical challenges in reintroducing determinism into such agentic HITL CPS and present pathways to address them.

Problem

• agentic AI
• human-in-the-loop
• cyber-physical systems
• nondeterminism
• robustness

Innovation

• reactor model of computation
• Lingua Franca
• agentic AI
• human-in-the-loop CPS
• determinism