@inproceedings{liu-etal-2025-slackagents,
    title = "{S}lack{A}gents: Scalable Collaboration of {AI} Agents in Workspaces",
    author = "Liu, Zhiwei  and
      Yao, Weiran  and
      Liu, Zuxin  and
      Tan, Juntao  and
      Zhang, Jianguo  and
      Wang, Frank  and
      Nahal, Sukhandeep  and
      Wang, Huan  and
      Heinecke, Shelby  and
      Savarese, Silvio  and
      Xiong, Caiming",
    editor = {Habernal, Ivan  and
      Schulam, Peter  and
      Tiedemann, J{\"o}rg},
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-demos.76/",
    pages = "969--982",
    ISBN = "979-8-89176-334-0",
    abstract = "In today{'}s rapidly evolving business landscape, organizations are turning to AI agents to automate tasks, streamline business operations, and improve decision-making processes. However, despite the flexibility offered by existing libraries, the developed agents often struggle with integration into organizational workflows, resulting in limited daily usage for work. In this paper, we present SlackAgents, a multi-agent library for scalable management and collaboration of AI agents on Slack. As an agentic layer developed upon the Slack platform, the framework offers instant AI integration into organizational workflows and enables AI-powered automation of real daily tasks. Furthermore, SLACKAGENTS facilitates scalable collaboration, allowing for effective communication and task orchestration. Our solution bridges existing gaps, offering a robust platform for developing, deploying and managing AI agents for workplace environments."
}