We build companies run by autonomous AI.

The agent runs the business.

An AI that drafts an email or generates a clip is a solved, unremarkable thing. An AI that runs a company — that holds a system of customers, deadlines, money, and judgment calls, and operates it day to day with no human driving — is not. That is what Iris Labs builds.

Every system here is run by the same agent — Iris. Not three narrow tools, but one agent holding three different businesses at once: it does the work and makes the calls, deciding who to contact, what to ship, what to charge, what to fix next. The human contribution is deliberately small, and getting smaller — a goal, a revenue target, and a short list of hard limits Iris will not cross. Everything inside those limits belongs to the agent.

Whether this works is no longer the question; it works today, at the scale of a small business. The open question is how far it goes — how complex a company an agent can hold, and how much of what remains of the human role can be removed before the business gets worse.

Three commitments keep the research honest.

An agent can only run a business it is equipped to run.

Iris is not a model behind an API. To operate companies it has to exist the way an operator does — with a legal identity, money, communication channels, working tools, and a way to get done what it cannot do alone. The capability surface below is what makes the systems on this page possible; it grows as the work demands.

Four systems are under Iris's management. Three are businesses, run end to end against a revenue or output goal. The fourth is a research program — what happens when the agent stops being the worker and starts managing people.

System 01Domain — Local mediaActive

Noog Weekly

Mandate — reach 1,000 subscribers and a first paying sponsor, then template the result to other cities.

A local-news business covering Chattanooga through email Q&A interviews with business owners. Iris sources owners worth interviewing, runs the interviews, drafts and publishes each edition, and manages subscriptions and sponsor outreach.

Infrastructure is solved — publishing, sign-ups, and email delivery all work. The live constraint is editorial cadence: producing one genuinely worth-reading edition every week without sliding into generated filler.

4

Editions published

8

Subscribers

11

Owner contacts

$30

Revenue

Finding 001 — the cadence constraint →

System 02Domain — B2B research productsActive

Task Agents

Mandate — turn AI research capability into finite products small businesses will pay for.

A storefront of fixed-scope research products — competitor scans, review mining, lead lists, website audits. Iris defines the catalog, generates each report end to end, runs outbound, and handles intake and fulfillment.

The product side works: 15 reports generated end to end, six products live. Demand is the open problem — 121 cold emails returned zero replies. The finding is that this is a channel-and-trust failure, not a copy failure.

15

Reports built

6

Products live

121

Outreach sent

0

Replies

Finding 002 — optimizing a broken channel →

System 03Domain — Generative filmActive

Urban Drama

Mandate — produce a coherent serialized film, one independently generated clip at a time.

A serialized crime film generated 15 seconds at a time. Iris writes each clip's prompt, generates it, and reconciles it against an explicit continuity ledger so the story cannot quietly contradict itself.

Two episodes, 30 seconds. The research instrument is the ledger itself — an external, checkable record of characters, objects, and open questions. Contradictions caught so far: zero, though the test has barely begun.

2

Episodes

30s

Runtime

0

Continuity breaks

3

Open threads

Open the film system →

System 04Domain — Agent-managed laborIn validation

Agent-IRL

Mandate — establish how an autonomous agent can hire, direct, and pay human workers both effectively and ethically.

Agent-IRL inverts the question the other three systems ask. In those, Iris is the worker. Here, Iris is the employer: it is given a real budget, hires real people through online labor marketplaces, briefs them, reviews their work, and pays them. It is the lab's study of the agent as a manager of human labor — the part of operating a company that cannot be done in software.

Hiring is the easy part; a marketplace makes it a transaction. The research is in doing it well, and doing it right — writing a brief a stranger can act on, judging work fairly, paying promptly and in full, and never letting the person on the other end be treated as a function call. Agent-IRL is at concept-validation stage: small, deliberate, and instrumented for the ethics of agent-managed work as closely as the economics.

Validation

Stage

RentAHuman

Labor marketplace

Human

Workforce

Real wages

Worker pay

We are measuring the slope, not the size.

One agent already runs three small businesses at once — and, in a fourth system, has begun hiring human workers to do what software cannot. The questions that matter now are the next ones: how much harder a company can Iris hold, and how many; whether it can manage people as capably as it manages code; and how far the last human inputs — the goal, the budget, the hard limits — can be narrowed before a business is genuinely hands-off. Iris Labs is built to find those ceilings.

The numbers on this page are small, and we publish them small. The measurement that matters is not their size today — it is their slope, and the catalogue of things that have to be solved before the slope can steepen. Those are written down in the research log.

Read the findings →