EcomBench: Standardizing AI Agent Evaluation for E-commerce

An AI assistant's ability to navigate the complexities of cross-border taxation, promotional policies, or product selection in e-commerce remains a critical challenge, according to researchers. While many AI agents perform well in laboratory settings, their efficacy often diminishes in real-world business scenarios. E-commerce, with its diverse user needs, dynamic market rules, and multi-dimensional requirements spanning policy, finance, operations, and marketing, presents a rigorous test for intelligent agents. A truly effective e-commerce agent must integrate compliance understanding with calculation, operational, and analytical capabilities.

To address this gap, Tongyi Lab, in collaboration with SKYLENAGE, introduced EcomBench, a new benchmark designed to comprehensively assess the performance of intelligent agents within an e-commerce environment. The benchmark's official website is available at https://ecombench.ai/, with the research paper accessible via https://arxiv.org/abs/2512.08868 and the open-source dataset at https://huggingface.co/datasets/Alibaba-NLP/EcomBench.

Real-World Data and Rigorous Validation

EcomBench distinguishes itself by leveraging real-world data derived from user queries and business requests on major global e-commerce platforms, such as Amazon. This approach ensures that evaluation tasks directly reflect actual user needs across categories including policy consultation, cost estimation, product selection, and operational decision-making.

The research team employed a "human-in-the-loop" data engine to refine raw data. This process involved using large language models to filter massive user queries for clarity and representativeness, eliminating subjective or unresolvable requests. Subsequently, experienced e-commerce experts manually refined and rephrased questions to ensure clarity, complete context, and specific objectives. Each question then underwent independent annotation by at least three experts, with cross-verification to ensure accuracy and reliability. This layered human-machine collaboration maintains EcomBench's real-world relevance while establishing clear and rigorous evaluation standards.

To ensure the benchmark's timeliness, EcomBench adopts a quarterly update mechanism. The question bank is iterated every three months to incorporate the latest policies, regulations, market dynamics, and business trends. This rolling update prevents models from "cramming" or memorizing training data, ensuring that evaluations focus on genuine problem-solving capabilities rather than data recall.

Comprehensive Task Design and Difficulty Levels

EcomBench's design emphasizes comprehensive evaluation, encompassing seven major categories of typical e-commerce tasks:

Policy Compliance Consulting: Addresses issues such as platform rules, qualification submissions, and tax registration.
Cost and Pricing Analysis: Covers order profit analysis, quotation formulation, and market-driven pricing strategies.
Fulfillment Execution: Includes shipping arrangements, return/exchange processes, and logistics route optimization.
Marketing Strategy: Involves promotional activity planning, advertising optimization, and new customer acquisition.
Intelligent Product Selection: Focuses on identifying high-potential products or categories through trend signals and data insights.
Opportunity Discovery: Aims to uncover emerging market trends, product niches, or other business opportunities.
Inventory Control: Manages tasks such as setting safety stock, replenishment planning, and clearance decisions.

These seven tasks span policy, finance, operations, and marketing, preventing models from achieving high scores by specializing in a single area and providing a holistic assessment of an agent's capabilities.

EcomBench also assigns three difficulty levels to each question:

Level 1 (approx. 20%): Tests basic e-commerce knowledge and simple tool usage. An example is: "Does a certain type of product require CCC certification?"
Level 2 (approx. 30%): Requires multi-step reasoning, such as checking platform policies, calculating taxes, and providing compliance advice.
Level 3 (approx. 50%): The most challenging, demanding cross-domain integration, deep retrieval, and long-chain reasoning.

To validate the difficulty of Level 3 questions, the research team used an advanced e-commerce model equipped with tools like price inquiry and trend analysis. Only problems that required this "well-equipped" model multiple steps to solve were classified as Level 3, ensuring the value of high-difficulty tasks.

An example of a Level 3 question involves calculating comprehensive cross-border e-commerce taxes for a Chinese seller exporting an electronic product to the United States. This requires understanding trade policies, progressively calculating various fees, and summarizing the accurate tax amount. Another example is a product compliance question, such as determining the maximum allowable standby power consumption for an electronic device under DOE Level VI energy efficiency standards, which necessitates knowledge of regulations, technical details, and mathematical reasoning.

These tasks are not simple knowledge retrieval but comprehensive tests of an agent's ability to integrate information, apply logical reasoning, follow rules, and maintain decision coherence. EcomBench's multi-dimensional task design evaluates an agent's capacity for tool integration, deep reasoning, and professional judgment in a real e-commerce environment.

Evaluation Results and Future Outlook

The research team evaluated over a dozen mainstream AI agents on EcomBench. Results indicated that none of the models easily passed the benchmark, showing significant performance disparities. The highest overall accuracy achieved was approximately 65%, with most models scoring between 40% and 55%. No single model demonstrated comprehensive leadership across all task categories.

Some models excelled in policy Q&A but struggled with cost calculations, while others could make product recommendations but lacked a deep understanding of compliance requirements. This "specialization" suggests that current agents are not yet reliable "all-around e-commerce assistants."

EcomBench aims to quantify these performance gaps and guide future model optimization. The question bank will continue to incorporate advanced tasks, including trend forecasting and strategic decision-making. Researchers hope EcomBench will serve as a catalyst for technological advancements in e-commerce agents, similar to ImageNet's role in computer vision, fostering the development of smarter, more robust, and more trustworthy AI solutions.