Integration of Static Knowledge and Telemetry Data for an IoT-Based Customer Support AI Agent

The operational effectiveness of artificial intelligence agents in customer support is frequently attributed to the generative sophistication of underlying language models, although answer reliability primarily depends on the structure and quality of the referenced knowledge base. In complex technical domains such as the Internet of Things (IoT), static documentation alone often fails to resolve customer inquiries that involve real-time device states, sensor readings, and connectivity logs. This study investigates how progressive tiers of knowledge integration affect the response quality of an AI agent within an IoT customer support context. Employing a mixed-method pilot design combining literature synthesis, comparative analysis, experimental testing, and conceptual exploration, thirty representative questions were classified into informational, status, and diagnostic categories. These queries were evaluated across four operational scenarios: without retrieval-augmented generation (RAG), article-based RAG, article plus device metadata records, and a comprehensive hybrid framework integrating static articles, device records, and telemetry streams. Answer quality was measured using Cosine Similarity, BERTScore (F1), and a randomized blind human evaluation assessing relevance, correctness, and usefulness. The results demonstrate that while curated static documentation adequately addresses procedural informational queries, resolving status and diagnostic issues necessitates real-time operational context. The hybrid integration delivered the highest overall performance, achieving a human-evaluation score of 4.40 compared with 2.53 for article-based RAG (p = 0.0039). These empirical findings confirm that robust IoT support agents require dynamic knowledge governance that bridges versioned documentation, synchronized asset metadata, and live telemetry data within a unified retrieval framework.