One Agent for the Whole Company

Hello, and happy Sunday. We launched the Every Agent this week, an AI coworker in Slack that your whole company shares. Your team can give it work in a thread, and when a workflow is successful, you can ask it to save the workflow as a skill anyone on the team can run. Also: The Thesis: 2027 after-party on November 5 at Pioneer Works in Red Hook, Brooklyn, is free for Every subscribers—RSVP to reserve your spot. Lastly, we’re off Monday for the U.S. holiday of Indigenous Peoples’ Day and will be back in your inbox on Tuesday.—Kate Lee

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The Every Agent, explained

“Introducing the Every Agent” by Dan Shipper/On Every: The Every Agent does its work in public Slack threads, so your colleagues see both the request and the corrections. It replaces Plus One, which we launched in March to give each of us our own agent, after Dan noticed that people learned the most from watching someone else’s agent at work. Head of evals Mike Taylor built a skill that resets his priorities every Monday, and engineer Nityesh Agarwal runs it every week, too. Each person on your team gets $15 in free credits to try it, with 0 percent markup on tokens: What we pay is what you pay.

🖥 🎧 “Why Every Traded Personal Agents for One Company Agent” by Laura Entis/The Every Podcast: Willie Williams, Every’s head of platform, joined Dan to explain why the personal bots our team built in January mostly died off: They were hard to maintain, and no one could remember which one did what. This is a must-watch or must-listen for anyone deciding whether to give every employee a personal agent or build one shared agent for the whole company. 🖥 🎧 Listen on Spotify or Apple Podcasts, watch on X or YouTube, or read the transcript.

“Why We Handed Our Agent’s Infrastructure to Anthropic” by Paridhi Agarwal/Source Code: Paridhi Agarwal, an engineer on the Every Agent, explains in her first piece for Every why we stopped hosting our own agents and moved to Anthropic’s Claude Managed Agents: Keeping the servers running took time we wanted to spend on the agent itself. The trade-offs are that the service runs only Claude models and can’t offer the zero-data-retention option some companies require. 🖥 Watch Dan and Willie explain how we built the agent on Claude Managed Agents in a video we shot with Anthropic.

“Building a More Efficient Agent” by Laura Entis/Context Window: The team first tried routing the Every Agent’s tasks through a cheaper model, and costs went up because many requests ended up running on a more advanced model as well. Paridhi went looking for waste in the agent’s own code, and a model upgrade plus a handful of small fixes cut token costs across 11 common tasks by more than 80 percent. Also in the edition: her four-step workflow, with prompts, for finding waste in your own software, the models the team is using this week, and Willie on managing engineers with AI.