@inproceedings{huang-etal-2025-crmarena,
    title = "{CRMA}rena: Understanding the Capacity of {LLM} Agents to Perform Professional {CRM} Tasks in Realistic Environments",
    author = "Huang, Kung-Hsiang  and
      Prabhakar, Akshara  and
      Dhawan, Sidharth  and
      Mao, Yixin  and
      Wang, Huan  and
      Savarese, Silvio  and
      Xiong, Caiming  and
      Laban, Philippe  and
      Wu, Chien-Sheng",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.naacl-long.194/",
    pages = "3830--3850",
    ISBN = "979-8-89176-189-6",
    abstract = "Customer Relationship Management (CRM) systems are vital for modern enterprises, providing a foundation for managing customer interactions and data. Integrating AI agents into CRM systems can automate routine processes and enhance personalized service. However, deploying and evaluating these agents is challenging due to the lack of realistic benchmarks that reflect the complexity of real-world CRM tasks. To address this issue, we introduce CRMArena, a novel benchmark designed to evaluate AI agents on realistic tasks grounded in professional work environments. Following guidance from CRM experts and industry best practices, we designed CRMArena with nine customer service tasks distributed across three personas: service agent, analyst, and manager. The benchmark includes 16 commonly used industrial objects (e.g., account, order, knowledge article, case) with high interconnectivity, along with latent variables (e.g., complaint habits, policy violations) to simulate realistic data distributions. Experimental results reveal that state-of-the-art LLM agents succeed in less than 58{\%} of the tasks with ReAct prompting, and less than 65{\%} even with function-calling abilities. Our findings highlight the need for enhanced agent capabilities in function-calling and rule-following to be deployed in real-world work environments."
}