Design of an AI Agent-Driven Long-Tail Intent Perception Framework for Intelligent Customer Service in Low-Resource Environments
DOI:
https://doi.org/10.70393/6a696574.343139ARK:
https://n2t.net/ark:/40704/JIET.v1n2a06Disciplines:
Intelligent SystemsSubjects:
Autonomous AgentsReferences:
28Keywords:
Intelligent Customer Service, AI Agents, Retrieval-Augmented Generation, Long-Tail Intent Perception, Low-Resource Environments, Human-Machine CollaborationAbstract
Intelligent customer service platforms have gained widespread deployment in numerous sectors in recent years. Even so, most mainstream systems run into bottlenecks when tackling rare, long-tail user questions. Issues like insufficient labeled data, varied expression habits and inadequate domain knowledge make such problems worse, especially for businesses lacking ample training datasets. Against this practical dilemma, this study puts forward the LTIP framework tailored for long-tail intent identification, which combines agent technology, retrieval-augmented generation, confidence scoring mechanisms as well as adaptive human-agent transfer logic. The framework employs multi-stage intent perception to identify long-tail inquiries, retrieves enterprise knowledge through RAG, and evaluates response reliability using a confidence-aware mechanism. Based on the confidence score, customer requests are dynamically routed to either AI services or human agents. The proposed framework improves long-tail issue handling while reducing dependence on large-scale labeled datasetsThis design delivers a streamlined, workable option to roll out smart customer service tools under constrained data and resource conditions.
References
[1] Wang, H., Li, Q., & Liu, Y. (2024). Multi-response Regression for Block-missing Multi-modal Data without Imputation. Statistica Sinica, 34(2), 527.
[2] Wu, X., Liu, X., Wu, Q., & Wang, X. (2021, July). Application of artificial intelligence in customer service field. In 2021 3rd International Conference on Applied Machine Learning (ICAML) (pp. 240–244). IEEE.
[3] Chen, J., Liu, Z., Huang, X., Wu, C., Liu, Q., Jiang, G., ... & Chen, E. (2024). When large language models meet personalization: Perspectives of challenges and opportunities. World Wide Web, 27(4), 42.
[4] Chen, Z., Gan, W., Wu, J., Hu, K., & Lin, H. (2025). Data scarcity in recommendation systems: A survey. ACM Transactions on Recommender Systems, 3(3), 1–31.
[5] Gupta, S., Ranjan, R., & Singh, S. N. (2024). A comprehensive survey of retrieval-augmented generation (RAG): Evolution, current landscape and future directions. arXiv preprint arXiv:2410.12837.
[6] Liu, X. (2025). Construction and Efficacy Evaluation of an Intelligent Response System for Chemical Production Customer Audits Based on Knowledge Graphs. Innovation in Science and Technology, 4(10), 22-28.
[7] Zhang, H., Guo, J., Li, K., Zhang, Y., & Zhao, Y. (2024, October). Offline signature verification based on feature disentangling aided variational autoencoder. In 2024 5th International Conference on Machine Learning and Computer Application (ICMLCA) (pp. 549-554). IEEE.
[8] Zhang, Z., Li, S., Zhang, Z., Liu, X., Jiang, H., Tang, X., ... & Jiang, M. (2025). IHEval: Evaluating language models on following the instruction hierarchy. arXiv preprint arXiv:2502.08745.
[9] Han, C. (2025). Can Language Models Follow Multiple Turns of Entangled Instructions?. arXiv preprint arXiv:2503.13222.
[10] TKarakurt, E., & Akbulut, A. (2025). Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs) for Enterprise Knowledge Management and Document Automation: A Systematic Literature Review. Applied Sciences, 16(1), 368.
[11] Jin, Y., Li, Z., Zhang, C., Cao, T., Gao, Y., Jayarao, P., ... & Yin, B. (2024). Shopping mmlu: A massive multi-task online shopping benchmark for large language models. Advances in Neural Information Processing Systems, 37, 18062-18089.
[12] Hao, Z., Yin, M., Xu, J., Liu, Z., & Chen, Y. (2026, March). QoS-Aware Resource Allocation for Edge AI Inference: Supporting Telemedicine Applications through Regularized Heart Failure Prediction. In 2026 IEEE 8th International Conference on Communications, Information System and Computer Engineering (CISCE) (pp. 477-480). IEEE.
[13] Hao, Z. (2026). Low-Overhead Scheduling for Real-Time AI Workloads on Multi-Core Edge Chips. International Journal of Advance in Applied Science Research, 5(3), 15-25.
[14] Xu, J. (2025, September). Fuzzy Legal Evaluation in Telehealth via Structured Input and BERT-Based Reasoning. In 2025 IEEE International Conference on eScience (eScience) (pp. 309-310). IEEE.
[15] Luo, M., Du, B., Zhang, W., Song, T., Li, K., Zhu, H., ... & Wen, H. (2023). Fleet rebalancing for expanding shared e-mobility systems: A multi-agent deep reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems, 24(4), 3868-3881.
[16] Hosseini, S., & Seilani, H. (2025). The role of agentic AI in shaping a smart future: A systematic review. Array, 26, 100399.
[17] Zhang, N. (2026). Research on the Long-Term Mechanism of Digital Transformation for Small and Medium-Sized Enterprises. Journal of Intelligence and Engineering Technology, 1(2), 19-28.
[18] Luo, M., Zhang, W., Song, T., Li, K., Zhu, H., Du, B., & Wen, H. (2021, January). Rebalancing expanding EV sharing systems with deep reinforcement learning. In Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence (pp. 1338-1344).
[19] Zhang, Q., Chen, S., Bei, Y., Yuan, Z., Zhou, H., Hong, Z., ... & Huang, X. (2025). A survey of graph retrieval-augmented generation for customized large language models. arXiv preprint arXiv:2501.13958.
[20] Gopinathan, V. R. (2024). AI-Driven Customer Support Automation: A Hybrid Human–Machine Collaboration Model for Real-Time Service Delivery. International Journal of Technology, Management and Humanities, 10(01), 67-83.
[21] Cheng, M., Luo, Y., Ouyang, J., Liu, Q., Liu, H., Li, L., ... & Chen, E. (2025). A survey on knowledge-oriented retrieval-augmented generation. arXiv preprint arXiv:2503.10677.
[22] Fan, H., Li, K., Li, X., Song, T., Zhang, W., Shi, Y., & Du, B. (2019). CoVSCode: a novel real-time collaborative programming environment for lightweight IDE. Applied Sciences, 9(21), 4642.
[23] Hao, Z. (2026). Dynamic Task Prioritization for Edge AI in Smart Cities: Balancing Latency and Energy Efficiency. Journal of Intelligence and Engineering Technology, 1(1), 60-69.
[24] Pang, F. (2020, November). Research on Incentive Mechanism of Teamwork Based on Unfairness Aversion Preference Model. In 2020 2nd International Conference on Economic Management and Model Engineering (ICEMME) (pp. 944-948). IEEE.
[25] Tao, Y. (2023, August). Meta learning enabled adversarial defense. In 2023 IEEE International Conference on Sensors, Electronics and Computer Engineering (ICSECE) (pp. 1326-1330). IEEE.
[26] Mingyang, W. (2026). Why Singapore’ s Healthcare Model Cannot Be Directly Replicated in Mainland China: A Comparative Policy Perspective. Insights in Social Science, 4(2), 24-31.
[27] Lu, L., Liang, C., Pin, G., & Gang, C. (2011, February). Study on water quality assessment of urban river. In 2011 International Conference on Computer Distributed Control and Intelligent Environmental Monitoring (pp. 2244-2247). IEEE.
[28] Chen, Y. (2025). Analysis of the Technical Application and Effectiveness of Intelligent Algorithms Empowering Regional Logistics Resource Matching. Engineering Frontiers, 1(3).
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