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Abstract
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.
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Citation FormatJauhar Maknun Adib, Magribi, W. P., Qusyairy, M. F., Julianto, E., & Rhomadon, K. F. (2026). Integration of Static Knowledge and Telemetry Data for an IoT-Based Customer Support AI Agent. International Journal Software Engineering and Computer Science (IJSECS), 6(3), 1058-1065. https://doi.org/10.35870/ijsecs.v6i3.7875
References
15 References- Alavi, M., & Leidner, D. E. (2001). Knowledge management and knowledge management systems: Conceptual foundations and research issues. MIS Quarterly, 25(1), 107–136. https://doi.org/10.2307/3250961
- Dalkir, K. (2017). Knowledge management in theory and practice. The MIT Press.
- Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In J. Burstein, C. Doran, & T. Solorio (Eds.), Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL HLT 2019) (Vol. 1, pp. 4171–4186). Association for Computational Linguistics. https://doi.org/10.18653/v1/N19-1423
- Dzenuska, E., & Rudzajs, P. (2025). Toward a knowledge management method for training customer support AI agents. In Proceedings of the BIR 2025 Workshops and Doctoral Consortium (CEUR Workshop Proceedings, Vol. 4034, pp. 70–83). CEUR-WS.org.
- Edge, D., Trinh, H., Cheng, N., Bradley, J., Chao, A., Mody, A., Truitt, S., & Larson, J. (2024). From local to global: A graph RAG approach to query-focused summarization. arXiv. https://doi.org/10.48550/arXiv.2404.16130
- Es, S., James, J., Espinosa-Anke, L., & Schockaert, S. (2024). RAGAS: Automated evaluation of retrieval augmented generation. In Y. Graham & M. Purver (Eds.), Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2024): System Demonstrations (pp. 150–158). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.eacl-demo.16
- Farahani, B., Firouzi, F., Chang, V., & Badaroglu, M. (2018). Towards fog-driven IoT eHealth: Promises and challenges of IoT in medicine and healthcare. Future Generation Computer Systems, 78(Part 2), 659–676. https://doi.org/10.1016/j.future.2017.04.036
- Gao, Y., Xiong, Y., Gao, X., Jia, K., Pan, J., Bi, Y., Dai, Y., Sun, J., & Wang, H. (2024). Retrieval-augmented generation for large language models: A survey. arXiv preprint arXiv:2312.10997. https://doi.org/10.48550/arXiv.2312.10997
- Laskar, M. T. R., Tremblay, J. B., Fu, X.-Y., Chen, C., & Bhushan, S., TN. (2025). AI knowledge assist: An automated approach for the creation of knowledge bases for conversational AI agents. arXiv. https://doi.org/10.48550/arXiv.2510.08149
- Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.-t., Rocktäschel, T., Riedel, S., & Kiela, D. (2020). Retrieval-augmented generation for knowledge-intensive NLP tasks. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, & H. Lin (Eds.), Advances in Neural Information Processing Systems (NeurIPS 2020) (Vol. 33, pp. 9459–9474). Curran Associates, Inc.
- Nonaka, I., & Takeuchi, H. (1995). The knowledge-creating company: How Japanese companies create the dynamics of innovation. Oxford University Press.
- Salemi, A., & Zamani, H. (2025). HELMET: How to evaluate long-context language models effectively and thoroughly. In Proceedings of the 13th International Conference on Learning Representations (ICLR 2025). OpenReview.net.
- Shuster, K., Poff, S., Chen, M., Kiela, D., & Weston, J. (2021). Retrieval augmentation reduces hallucination in conversation. In M.-F. Moens, X. Huang, L. Specia, & S. W.-t. Yih (Eds.), Findings of the Association for Computational Linguistics: EMNLP 2021 (pp. 3784–3803). Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.findings-emnlp.320
- Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. In I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, & R. Garnett (Eds.), Advances in Neural Information Processing Systems (NeurIPS 2017) (Vol. 30, pp. 5998–6008). Curran Associates, Inc.
- Zhang, T., Kishore, V., Wu, F., Weinberger, K. Q., & Artzi, Y. (2020). BERTScore: Evaluating text generation with BERT. In Proceedings of the 8th International Conference on Learning Representations (ICLR 2020). OpenReview.net.
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Copyright (c) 2026 Jauhar Maknun Adib, Wahyu Purnama Magribi, Muhammad Fazly Qusyairy, Eric Julianto, Khusnul Fajri Rhomadon
This work is licensed under a Creative Commons Attribution 4.0 International License.
This is an open-access journal distributed under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, reproduction, and adaptation in any medium (even commercially), provided appropriate credit is given to the original author(s), a link to the license is provided, and any changes made are indicated. Authors retain copyright without restrictions and grant the journal the right of first publication as well as non-exclusive publishing rights for archiving, indexing, and scholarly dissemination.
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