Episode Details

Jaime DeLanghe has spent nine years at Slack turning search, machine learning, and now agents into product. Her team just shipped Slack Code: tag a coding agent like Claude Code, Devin, Codex, or the GitHub agent in a conversation, and it spins up a code channel where everyone in that conversation gets a live development environment, diffs post as artifacts, and the channel winds down when the task is done.

Slack's bet is that AI at work is multiplayer. Agents belong in the channels where teams already work, not in a private chat with one person. Jaime explains why Anthropic pushes so much of its code through Slack, how the channel permission model became the agent context model, and what has to change in engineering culture when the branch is public and the whole team is steering the same agent.

The bigger question is whether Slack becomes the context harness where enterprise agents actually run.

In this conversation:

• What happens mechanically when an agent creates a code channel, from authentication to diffs as artifacts
• Why engineering at Slack now looks like delegating discrete tasks to agents instead of copy-pasting from a chat
• How Slack's channel permission model doubles as the context and access model for agents
• Why Anthropic ships code through Slack: the conversation is where the issue emerges
• How culture decides whether a multiplayer coding session converges or splits
• Why solo-terminal coding with an "army of Claudes" reinforces bias, and what social spaces fix
• Slack as an accidental knowledge management system that ranks recency and engagement over correctness

(0:00) Slack as an IDE and a GitHub for your team
(0:29) Who is Jaime DeLanghe
(1:21) The reaction to the Slack Code launch
(5:30) Why coding agents belong in a context-rich environment
(6:08) Engineers now manage agents, not copy-paste code
(7:24) The permission model: agents get the channel's context
(11:44) What happens when a code channel is created
(15:00) Why Anthropic pushes so much code through Slack
(19:14) Steering one agent with many people: culture decides
(24:54) Slackbot, skills, and MCPs: agents go where the work is
(30:53) The solo terminal vs. agents in social spaces
(33:53) Org charts and ownership when agents join the team
(39:33) Learning loops and shared agent memory
(42:39) Citations, recency, and accidental knowledge management
(46:50) Context bloat and multi-pass search for agents
(50:01) How Jaime uses Slackbot as CPO
(52:38) Slack Code is V1 of multiplayer AI

Connect with Jaime DeLanghe:

• LinkedIn: https://www.linkedin.com/in/jaime-delanghe-aba59b1a/
• Slack Code announcement: https://slack.com/blog/news/slack-code-channels-for-agents
• Introducing Slack Code (Salesforce): https://www.salesforce.com/introducing-slack-code/

Connect with Chain of Thought host Conor Bronsdon:

• Newsletter: https://newsletter.chainofthought.show/
• Twitter/X: https://x.com/ConorBronsdon
• LinkedIn: https://www.linkedin.com/in/conorbronsdon/
• YouTube: https://www.youtube.com/@ConorBronsdon

More episodes: https://chainofthought.show

Thanks to Walrus Memory, presenting sponsor of season four of Chain of Thought. Walrus Memory gives AI agents portable, verifiable memory that carries context across apps, sessions, and other agents. Get started a