Sukhandeep Nahal


2025

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SlackAgents: Scalable Collaboration of AI Agents in Workspaces
Zhiwei Liu | Weiran Yao | Zuxin Liu | Juntao Tan | Jianguo Zhang | Frank Wang | Sukhandeep Nahal | Huan Wang | Shelby Heinecke | Silvio Savarese | Caiming Xiong
Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

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.