ReAct Meets Industrial IoT: Language Agents for Data Access

We present a robust framework for deploying domain-specific language agents that can query industrial sensor data using natural language. Grounded in the Reasoning and Acting (ReAct) paradigm, our system introduces three key innovations: (1) integration of the $\texttt{Self-Ask}$ method for compositional, multi-hop reasoning; (2) a multi-agent architecture with $\texttt{Review}$, $\texttt{Reflect}$ and $\texttt{Distillation}$ components to improve reliability and fault tolerance; and (3) a long-context prompting strategy leveraging curated in-context examples, which we call $\textit{Tiny Trajectory Store}$, eliminating the need for fine-tuning. We apply our method to Industry 4.0 scenarios, where agents query SCADA systems (e.g., SkySpark) using questions such as, “How much power did B002 AHU 2-1-1 use on 6/14/16 at the POKMAIN site?” To enable systematic evaluation, we introduce $\textbf{IoTBench}$, a benchmark of 400+ tasks across five industrial sites. Our experiments show that ReAct-style agents enhanced with long-context reasoning $(\texttt{ReActXen})$ significantly outperform standard prompting baselines across multiple LLMs including smaller models. This work repositions NLP agents as practical interfaces for industrial automation, bridging natural language understanding and sensor-driven environments.