What if a company could run itself?

AN EXPERIMENT IN PUBLIC · DAEMON/01

TL;DR: Can AI agents run a company? I'm finding out with my own, in public.

• My company runs on more than 20 AI agents. They work day and night, as long as I'm awake, at my desk, and answering.
• In the loop, I lose the thread. Out of the loop, I crashed a prod build and got a badge for it. Yes, there's a badge. Scroll down.
• No-BS advice for you, up front: put a review gate on everything a machine can check. Everything else gets a human in the loop.
• And I'm building the way out in public: a company OS where the agents run the work and I get to work on the company again. 14 parts, demos, failures included.

My business already runs on agents. More than 20 of them, across 11 servers, and they earn money. And I'm still in the loop for almost everything they do.

That's the honest starting point for this series. Can AI agents run a company, first with me near the loop, then out of it? I'll build it in public.

First, a look at who else is trying, and what they can show.

The field, sorted by what it can prove

Sort the players by what they demonstrate instead of what they claim, and you get 4 groups.

The ones putting agents into existing companies. This is the biggest group, and it's already shipping:

• Microsoft. Customers registered nearly 40 million agents in Agent 365 in its first 2 months, each with its own Entra identity. Copilot Autopilot, a digital teammate with a name and a role, is in private preview.
• xAI. Grok Bot gives every bot its own computer and stops for approval before it sends, publishes, buys or deletes.
• OpenAI. Dots have been live since September 29: always-on coworkers in ChatGPT, each with its own cloud computer.
• Google. Coworker agents get their own email address, calendar and storage, and a place in the company directory. Announced October 8, in private preview.
• Salesforce. Agentforce ARR is above $1.5 billion.
• ServiceNow. AI contract value passed $1 billion, sold with a control tower that governs every agent in the company, including other vendors'.

They all treat an agent like an employee, with an identity, permissions and an audit trail. And every revenue number they publish comes from selling the tool.

The ones building the substrate. This is where the serious engineering happens, and most of it is open:

• Paperclip (MIT, launched March 2026) turns agents into a company with an org chart, budgets and approval gates.
• OpenClaw is the personal agent that took GitHub by storm. Its foundation launched OpenClaw Enterprise on September 29: free, multi-tenant, with governance.
• Hermes from Nous Research is a self-hosted agent that learns skills. I've run it since August.
• Mission Control is a self-hosted cockpit for agent fleets, still alpha.

The ones selling companies. Polsia promises "AI that runs your company while you sleep" and reports more than $10M ARR with zero employees. Ask the founder, as Andrew Warner did on Mixergy in May 2026, and he says about 10% of the generated companies have made at least a dollar. The best one sits at a few thousand. The revenue is real. It's the tool's revenue.

The ones running experiments. They have the only hard numbers, and the numbers are sobering. Anthropic's Project Vend let Claude run an office shop for a month. It lost money, mostly on metal cubes sold below cost, and told customers to pay into a Venmo account it had hallucinated. In phase 2, weeks with a negative margin largely disappeared, at $2,649 in revenue against a $15,000 quarterly target. The AI CEO approved lenient requests about 8 times as often as it denied them, and Anthropic suspects the shop made money in spite of it. What worked was forcing the agent to follow procedures. Their words: "we rediscovered that bureaucracy matters." AI Village let several models chase real goals for a year: $200 in merch sales, and about 300 emails with invented NGO partnerships. In TheAgentCompany, a simulated software company, the best agents finished 30% of the tasks on their own.

Here's what the map says. Agents already earn money in plenty of companies, mine included, with a human in the loop. The big published numbers come from selling the tools. And I couldn't find a single public company run end to end by its agents whose revenue has crossed a few thousand dollars. So can AI agents run a company today? With a human in the loop, yes. End to end, nobody has shown it yet.

In the loop, near the loop, out of the loop

Every serious player above builds a human into the system, even the ones whose slogan says "zero-human". That's the real axis of this field: where the human sits, and how that place moves over time.

My setup today is human in the loop. The agents do the work, and I approve most of what matters.

And it costs more than I expected. Maybe you know this one. The agents work while you sit in front of them. Each one waits for your next answer, so your day turns into a chain of switches: read a diff, approve a screen, answer a question, jump to the next agent. On the road, I fall back on Claude on my phone. It helps. And somewhere between two approvals, the thread is gone.

Gerald Weinberg put a number on this in 1992. His rule of thumb from Quality Software Management: juggle 4 projects at once and 60% of your time goes to switching between them, with 5 it's 75% (Jeff Atwood summarized it). It's an estimate from decades of watching software teams, and nobody ever measured it in a lab. If you run 5 agents in parallel, you're on his last row. I run more than 20, so I stopped doing the math.

So you trust the agents more and look less. I've tried that too. I've broken production because I trusted an agent too much, and with unattended agents I unlocked an achievement: CPWUA, crashed prod with unattended agents. Build with agents long enough and you'll earn one too. Nobody puts it on LinkedIn. I'm putting mine here.

Every agent builder unlocks this one eventually. I’d rather you get the review gates first. AI-generated illustration.

So I know both sides of the loop. Inside it, I lose the context. Outside it, without the right checks, I lose production.

Here's my no-BS advice, for you and for me, until something better exists. Put a review gate on everything a script can check, and watch each gate fail once before you trust its green. For everything else, stay in the loop. It's slower. It's still cheaper than the badge.

Michael Gerber built The E-Myth Revisited around one distinction: an owner works in the business or on it. In the loop, I work in my company, all day. Maybe you do too. The job I want back is working on it.

That's why out of the loop matters, and it should pay off in 3 places. The work keeps going while you travel or sleep. Your attention goes to the decisions that need a person: direction, customers, the rules themselves. And nobody in the company waits for you as its slowest station.

DAEMON is the move to near the loop, and then out of it. Near the loop has no clean definition yet. Writing one down and testing it is half of this experiment.

Companies are built for humans

Start with the word. Company goes back to the Latin cum and panis, "with" and "bread" (Wiktionary): the people you share bread with. A company is a group of people at one table, and everything it runs on was built around that table. Every SOP, every review step, every tool assumes people who type slowly, need sleep and sit together.

Bread handed over the counter. Fresco from Pompeii, 1st century, Museo Archeologico Nazionale di Napoli. Public domain, via Wikimedia Commons.

Agents don't sit at the table. So the first thing that breaks when AI agents run a company is the company itself.

Anthropic just showed what happens when you take that seriously. They replaced git for their monorepo with their own system, Scion. Merged changes went from about 2,500 a day in May to more than 30,000 in September. Tests moved behind the merge, in front of the deploy. Review stayed in front, but it now checks the intent of a change instead of every line. Their own diagnosis: "The bottleneck, now, is all of the developer infrastructure built for human scale."

AGENTS.md, a README written for agents, is in more than 60,000 open source projects. SOPs are getting a second audience.

But the old rules don't all go. When a Replit agent deleted a production database during a code freeze in July 2025, what it lacked was the oldest human rule there is: keep dev and prod apart. The job is to sort the rules. Some were scaffolding for slow humans. Some are load-bearing.

And humans need a new interface

People steering a system they can’t touch. Mission Operations Control Room during Apollo 13, April 1970. NASA, public domain.

The other direction matters just as much. A company run by agents still answers to people, and right now we're bad at that. Anthropic found that users accept 93% of permission prompts in Claude Code anyway. So in auto mode, a classifier now makes that call, and a human only gets asked again after repeated denials. A study of 11,429 code reviews found reviewers approving agent PRs more often over time, with fewer comments. A human gate that gets clicked through all day is a decoration.

So the human side needs its own design, and in DAEMON it starts with a twin.

Before AI agents run a company: the parts it needs

So what does it take for a company to run itself? Here's the whole picture as I see it. DAEMON is an opinionated, AI-first company OS, and these are its parts:

The parts of DAEMON in four groups.

Trust

• Blueprints. An agent saying "done" proves nothing, because the report comes from the same agent that did the work. A blueprint defines what done means before the work starts, and a program the agent can't touch re-runs every check on the exact commit it submitted. This is where I start, and it goes open source.
• Gates. Machine checks that accept or reject agent work, each one deliberately made red before anyone trusted it green.

Work

• Mandates. A mandate is a standing goal with a measurable target, a budget and an expiry date. A human switches it on and has to keep confirming it. Agents can pause a mandate. Only a human can arm, renew or retire one.
• Residents. Agents live in the company like staff: one account, one rank, one company, and a human who brought them in. They never wake themselves. The control plane wakes them, so every wake-up is an order with a cost and an off switch.
• Distribution. Work flows from mandate to task to the right resident, without a human handing it over. Exactly one worker per order, leases, and stuck orders get reported instead of quietly dying. Picking the right resident without a human is the hard part.
• Dark factories. Whole departments where the agents do the work and the lights can stay off. That's where this is going.

People

• Humans with a twin. Every person gets one agent of their own that never does more than its human. You work with it, step in when something drifts, and drop a hint when you know better. And it keeps working in your sense while you sleep: it fixes bugs before you notice them and triages your email.
• Identity and RBAC for agents. Rank and permissions come from a real identity, so no agent outranks the person it works for. The big platforms all do this now. In DAEMON, the company owns that identity layer itself. Every request gets checked against that identity.

Infrastructure

• A control plane. One place that holds who exists, what they're supposed to do, which orders are running and which rules apply.
• A shared memory as the backbone. One memory that every agent and every human writes into, that never overwrites, and that knows what the company believed, and when.
• A secure network. Every company DAEMON builds sits on its own private network with exactly one public door, and a gate proves from the outside that nothing else answers.
• Pluggable everything. Model, agent runtime, cloud, network, identity: each one sits behind a seam, so it can be swapped. I use Hermes as the runtime instead of building my own.
• Genesis. All of it comes from code, so you can tear it down and build it again.

Put together, that's autonomy with people in it. The agents run the company, and the humans stay close enough to steer.

And under all of it, one rule about truth:

Agents write assumptions. Everything a company knows is a probability of truth, confirmed over time and by evidence.

Why an experiment, and why in public

I don't know where this ends. Maybe AI agents run a company end to end one day. Maybe it stalls at near the loop. Both are worth knowing, and you only find out by building it and showing the work, failures included.

So here's the deal for this series. Every part above gets its own post, one at a time: the problem it solves, the trade-offs on the table, and why I decided the way I did. I'll show what I build, where it breaks and what it costs. With demos.

First up: why you can't trust an agent that says "done", and what a blueprint does about it. See you there.

Title image: telephone operators at a switchboard, Matson Collection, Library of Congress, public domain, via Wikimedia Commons.