Privacy-preserving Agentic Reporting for IoT Asset Monitoring: A Preliminary Case Study

Sensorized assets are increasingly used in smart and connected settings to support safe and effective operations.However, human operators often struggle to interpret monitoring outputs, especially under continuous monitoring.This paper presents a privacy-preserving LLM-based reporting framework that converts structured monitoring outputs into insights to support operators' decision-making. The framework combines overlap-aware chunking with hierarchical aggregation to address finite context-window constraints.The results support the feasibility of on-premise agentic AI as a compliant and interpretable decision-support layer for privacy-sensitive IoT monitoring contexts.