Accurate Harmonized Tariff Schedule (HTS) code classification is essential for customs clearance, duty assessment, trade statistics, and regulatory compliance in maritime logistics. However, exact HTS classification remains challenging because product descriptions are often short, incomplete, or ambiguous, while correct classification depends on hierarchical tariff structures, legal notes, and jurisdiction-specific rules. This paper proposes an agentic large language model (LLM) framework for Canadian 10-digit HTS code classification in smart-port and maritime logistics environments. The framework integrates multi-agent information retrieval, semantic retrieval over official tariff documents, evidence-grounded reasoning, consensus-based validation, element-wise voting across hierarchical code components, confidence estimation, and human-in-the-loop escalation. We evaluate the framework on a private dataset of 3,300 domain-expert-labeled product records collected from logistics and delivery contexts. Experimental results show that exact 10-digit classification remains difficult even for advanced LLMs, with performance decreasing from coarse chapter-level prediction to fine-grained tariff and statistical suffix assignment. These findings demonstrate the need for evidence-grounded, uncertainty-aware, and human-centered classification workflows rather than fully autonomous single-step prediction. The proposed framework supports more interpretable, accountable, and compliance-oriented HTS classification for maritime logistics and smart-port operations. Our code is available at https://github.com/Analytics-Everywhere-Lab/hts.

翻译：精确的协调关税表（HTS）代码分类对于海关清关、税额评估、贸易统计以及海运物流中的合规性至关重要。然而，由于产品描述通常简短、不完整或存在歧义，且正确分类依赖于层级化的关税结构、法律注释和特定司法管辖区的规则，因此精确的HTS分类仍然具有挑战性。本文提出了一种面向智能港口与海运物流环境中的加拿大10位数字HTS代码分类的大语言模型（LLM）智能体框架。该框架整合了多智能体信息检索、官方关税文件的语义检索、基于证据的推理、共识驱动的验证、跨层级代码组件的逐元素投票、置信度估计以及人机协同升级机制。我们在一个包含3,300条领域专家标注的产品记录（来自物流和交付场景）的私有数据集上评估了该框架。实验结果表明，即使对于先进的LLM，精确的10位数字分类仍然困难，性能从粗略的章节级预测下降至细粒度的关税与统计后缀赋值。这些发现表明，需要采用基于证据、不确定性感知和以人为中心的分类工作流，而非完全自主的单步预测。所提出的框架为海运物流和智能港口运营提供了更具可解释性、可问责性且合规导向的HTS分类支持。我们的代码可在https://github.com/Analytics-Everywhere-Lab/hts获取。