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.
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.
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.
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.
RentAHuman
Labor marketplace