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This article focuses on integrating AI agents directly into enterprise collaboration workflows via Slack's Model Context Protocol (MCP) to solve data silos and drive ROI on existing software investments. By grounding generalist LLMs in conversational history and supporting model-agnostic architectures, the platform aims to create a scalable agentic work operating system that reduces technical debt.
Build a Scalable Agentic Work Operating System in Slack

I spent the last week at Dreamforce in conversations with customers wrestling with the same problem: they’ve invested heavily in AI, but most of it still lives in a browser tab, disconnected from how their teams actually work. Companies are running more than 1,000 separate software applications right now, and global AI spending is approaching $2.5 trillion this year. Most of that money is already spent, so the next challenge facing IT leaders is driving adoption and proving ROI on what they’ve already bought. Much of that value stays locked away because tools sit in isolation, outside the daily flow of work. That’s where Slack comes in. A year ago, we unveiled an updated Slack platform built for the agentic era, where humans and agents work…
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