@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://aclanthology.org/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."
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%0 Conference Proceedings
%T SlackAgents: Scalable Collaboration of AI Agents in Workspaces
%A Liu, Zhiwei
%A Yao, Weiran
%A Liu, Zuxin
%A Tan, Juntao
%A Zhang, Jianguo
%A Wang, Frank
%A Nahal, Sukhandeep
%A Wang, Huan
%A Heinecke, Shelby
%A Savarese, Silvio
%A Xiong, Caiming
%Y Habernal, Ivan
%Y Schulam, Peter
%Y Tiedemann, Jörg
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-334-0
%F liu-etal-2025-slackagents
%X 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.
%U https://aclanthology.org/2025.emnlp-demos.76/
%P 969-982
Markdown (Informal)
[SlackAgents: Scalable Collaboration of AI Agents in Workspaces](https://aclanthology.org/2025.emnlp-demos.76/) (Liu et al., EMNLP 2025)
ACL
- Zhiwei Liu, Weiran Yao, Zuxin Liu, Juntao Tan, Jianguo Zhang, Frank Wang, Sukhandeep Nahal, Huan Wang, Shelby Heinecke, Silvio Savarese, and Caiming Xiong. 2025. SlackAgents: Scalable Collaboration of AI Agents in Workspaces. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 969–982, Suzhou, China. Association for Computational Linguistics.