Inside Every
Working with AI right now often means making the same judgment call dozens of times a day: Hand this task off to an agent or stay close to the process? “The landscape of working with AI is bifurcating,” is how CEO Dan Shipper put it in Every’s Monday standup. On one side is the agent you delegate to. On the other is the agent that sits beside you while you write, code, triage, revise, and decide.
Watching the Every team work, you can’t unsee it. Dan delegates bug reports for our collaborative document editor, Proof, to his OpenClaw agent, R2-C2. But he stays close to his inbox through a combination of Codex, Every’s AI email assistant Cora, and a document with custom rules (steal his workflow below). Kieran Klaassen hands the middle of his compound engineering workflow to the model but works closely with it to brainstorm at the beginning and polish at the end. I (Katie Parrott) send the model off to do research, but I’d never trust it to execute a full draft without my hands firmly on the wheel.
Which means the allocation economy thesis was only right about half the work. Some of it still wants delegation, but the other half wants you to stay close, pairing on every move with the model in the same window. The two halves demand different skills, and the meta-skill is knowing which is which.
Think of it as the AI version of the serenity prayer: Grant me the serenity to delegate the work I can, the expertise to sit with the model on the work I can’t, and the wisdom to know the difference.
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Steal this workflow
Get to inbox zero with Codex
The perfect email workflow is the white whale productivity people have chased for a decade, Dan included. His latest AI-native version puts the agent in the inbox and the human in a shared document, where every draft and decision stays visible. Here’s how he does it:
1. Write a one-page operating manual for your inbox. The document, which Dan keeps in Proof, names his VIPs, describes what to auto-archive, summarize, or draft, and explains how to handle scheduling.
2. Open your agent-native email tool in Codex. In Codex’s browser pane, Dan loads Cora, which gives the agent two ways to act: command line instructions to archive threads—but also the ability to click through the inbox like a person.
3. Work from a document instead of your email. Dan has Codex create a separate Proof document for each inbox run. Codex sweeps the inbox, archives what the operating manual says to archive, and adds every draft or decision to the bottom of the document. Dan replies inline: “Spam,” “archive,” “reply just to Willie asking what he wants to do here,” “send the invite, draft a reply to Tony.” Codex picks up each instruction, drafts in Cora simultaneously as Dan moves onto the next message, and waits for approval before sending.
Try it this week: Write a one-page “how to do my email” document with your own VIPs, auto-archive rules, scheduling preferences, and reply style. Then open Codex, load your email client in its browser pane, and paste in your instruction document and this prompt:
“Sweep my inbox using this operating manual. Put every draft and decision in this doc and wait for me before sending anything.”
New job alert
If the new meta-skill is knowing when to delegate and when to stay close, here it is in job-description form: Airtable is hiring an AI Agent Architect, Customer Experience.
Support software used to route tickets and surface help center articles. Now it can read context, act across tools, and decide what to do. Which means someone has to design the boundary around support agents—what knowledge they retrieve, which APIs they can use, when they can modify an account, how failures get measured, and where the agent hands the work back to a person.
Tool for thought
Musk’s five rules of automation, except for agents
In 2021, Elon Musk introduced his “algorithm,” a five-step rubric he uses at Tesla and SpaceX to figure out what a process needs before trying to make it faster or handing off any part of it to a machine. Willie Williams, Every’s head of platform, has been exploring how it might apply to agent workflows:
- Question every requirement. Every rule, checkpoint, and instruction in a workflow has to justify itself by naming the specific thing that goes wrong without it. If nobody can answer that, it shouldn’t be there.
- Delete what you can. Cut steps, approvals, reviews, and agents that don’t survive step one. If you’re not occasionally removing something you later need to restore, you haven’t cut enough.
- Simplify and clarify. Break the remaining work into smaller, clearer pieces. Each task should have a single owner, a defined output, and only the information and tools it actually needs.
- Accelerate feedback loops. Shorten the time between handing work to an agent and knowing whether it succeeded. Surface errors early, run independent tasks at the same time, and stop making the workflow wait on unneeded approvals.
- Automate last. Start with a checkpoint at every step. Only after a workflow is necessary, lean, and fast should you take the humans out of the loop.
Still, Musk’s algorithm was intended for factories building electric cars, rockets, and satellites—hardware. They don’t directly translate to AI agents. “These rules should apply to the world of software automation,” says Willie, “but we don’t actually have them yet. And we have to work on finding them.”
Model card
Signal
The hard part isn’t the model
The bifurcation Dan named in Monday’s standup—delegate to the agent, or sit beside it—is the same problem for which frontier labs are now selling enterprise solutions.
OpenAI made it explicit last month with its new Frontier Alliance initiative pairing OpenAI engineers with large enterprises to deploy agents inside their workflows. “The limiting factor for seeing value from AI in enterprises isn’t model intelligence,” writes OpenAI. “It’s how agents are built and run in their organizations.”
Then this week, Anthropic announced a parallel move—a new services firm with Blackstone, private equity firm Hellman & Friedman, and Goldman Sachs to help companies “design, build, and maintain” Claude deployments.
Both labs are saying the quiet part out loud: The hard part of deploying and working with agents is everything around the models themselves—the context, permissions, handoffs, evaluations, and human relationships that decide whether a model should run ahead or sit beside you. Dan’s inbox workflow and Airtable’s support-agent job are microcosms of the same problem, now landing on the enterprise balance sheet. (Every’s consulting practice also helps companies implement AI workflows and products.)
What to do this week:
- Write down how you want the work done before you prompt. WhatOpenAI and Anthropic are charging Fortune 500s millions for is the document Dan wrote himself in an afternoon: who counts as a VIP, what to auto-archive, when to escalate. Start there.
- Split your tasks into “hand off” versus “stay close.” Bug triage can run on its own. Important email drafts need you in the loop. Sort before you delegate.
- Keep the agent’s actions visible. Drafts in a shared document, tracked changes, an action log—whatever the form, you need a record. If you can’t audit the agent’s work and revert it if needed, you aren’t the one driving.
Katie Parrott is a staff writer at Every. You can read more of her work in her newsletter.
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