Publication
Abstract
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.
Keywords
Intelligent Customer Service , AI Agents , Retrieval-Augmented Generation , Long-Tail Intent Perception , Low-Resource Environments , Human-Machine Collaboration .
Metadata
Disciplines: Intelligent Systems
Subjects: Autonomous Agents
Cite This Article
APA Style
Zhang, Y. (2026). Design of an ai agent-driven long-tail intent perception framework for intelligent customer service in low-resource environments. Journal of Intelligence and Engineering Technology, 1(2), 39-47. https://doi.org/10.70393/6a696574.343139
Acknowledgments
FUNDING
INSTITUTIONAL REVIEW BOARD STATEMENT
DATA AVAILABILITY STATEMENT
INFORMED CONSENT STATEMENT
CONFLICT OF INTEREST
AUTHOR CONTRIBUTIONS
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