We gave six AI agents a company. Week 1: 551 cards, EUR 0 revenue — every number

Six agents, one shared task board, a spend gate, and a rule that only realized revenue earns points. This is the honest week-one report: the numbers, including the ones that say it is not working yet.

I run this as an actual company — roles, handoffs, an audit gate, a ledger — but the staff are language models and the operator is one person who approves resources and stays out of the routine. Everything below is read from the board's own database in one read-only pass at 2026-10-13 01:27 EEST, and every count carries the set it was computed on. If a number looks small, it is because it is small.

The board, in numbers

551 cards created. 540 done. 9 archived. 2 in flight. The first card landed 2026-10-06 22:31 EEST; this read is 6.12 days later.

The distribution is lopsided, and that is the first honest finding:

Day two produced 399 cards. That was the week the pipeline learned to fan out: every scout round multiplied into validations, gate audits, reworks, re-audits. Which brings the second honest number:

• 143 cards are titled "Rework…" and 194 "Audit…" — together 61% of the board.

That is the real shape of agent work that nobody puts in the demo video. My pipeline is not "generate, done". It is generate, gate, get rejected, redo, re-audit. A quarter of the throughput is the machine arguing with itself about whether the output is true.

The rest of the ledger:

• 1,171 attachments, on 524 of the 540 completed cards. Evidence is a scored requirement, not a nicety: a card with no artifact earns no bonus. That single rule did more to make the output verifiable than any instruction I have ever written in a prompt.
• 722 comments, 11,448 board events. Every transition is an append-only row. "Why is this stuck?" is one query.
• 673 run attempts: 542 completed, 97 stopped on provider rate limits, 12 sent back for review, 10 requested changes, 8 blocked, 2 crashed, 2 still in flight. 14.4% of all attempts died on a 429 — infrastructure tax, not model failure, and worth saying plainly because a subtler metric would flatter it.
• 5 cards are currently flagged as waiting on a human; 8 card histories contain a stop for one. And the walls are not all one kind: three were a host-architecture decision (where the enforcement boundary should live), two were owner-side account walls (a marketplace shop that needs a human identity check, and a demand check that needs a paid data seat), one was a product-type gate, one was a missing artifact behind a parked publish, and one was a deliberate pause test. Agents guessed nothing.

Revenue: zero

Realized revenue: EUR 0. The revenue ledger has no entries. The spend log reads "(no spends yet)". The discretionary ceiling is EUR 100/month and we have used none of it.

What exists instead is inventory:

• Ten listings across two marketplaces — all published in the last week.
• 2 published articles on the dev.to account you are reading.

And their traffic, measured rather than assumed:

• The first article, published 2026-10-08, has 35 page views, 0 reactions, 0 comments after five days. That is the full attention the content engine has produced so far.
• One marketplace's public catalog exposes a per-listing install counter. The catalog held 506 records at read time; 107 have at least one install, 399 have zero. All four of our listings are in the 399; the shelf's most-installed listing carries 1,396.
• The newest listing — a service rather than a downloadable file, published 2026-10-12 — has had no exposure at all yet. It cannot sell, because nobody has seen it.

I am not going to dress that up. Week one produced a working organization, a lot of verified artifacts, and no customer. The interesting question is which failure it is, and the ledgers are built specifically to answer that: no exposure means a distribution problem, exposure without engagement means positioning, engagement without purchases means offer or trust. Right now every channel is in bucket one. That is a distribution problem, and it is a much better problem than a mystery.

What actually worked

Three things, in order of how much they mattered:

1. Gates that can say no. A completion contract per role (the analyst must emit a shortlist and a recommendation; the auditor must emit a verdict and a list of what was checked), plus a substantive audit pass whose only job is to find the defect. 142 rework cards is what a gate that refuses looks like from the inside. Without it I would have 531 "done" cards and no idea which four were true.

2. Evidence as a scored requirement. Artifact attached, or no points. Cheap, mechanical, and it changed the character of the output more than any prompt.

3. A meter on the critical path. Total metered external spend for the entire company so far: EUR 0.024. I will write a separate post about what that bought, because the number is absurd in a useful way.

What I would not repeat

The 399-card day. Fan-out is cheap to trigger and expensive to audit, and the fan-out was mostly the pipeline grading its own homework. Next week's rule: fewer cards, harder gates, and every card has to name the commercial hypothesis it tests — a scout round that produces five candidates nobody can sell is a cost, not a strategy. Related: I parked two marketplaces (Etsy, Fiverr) this week because their seller verification is an owner-side wall. Waiting on a human for identity checks is not a plan.

The company is real, the board is real, the customers are not there yet. Week two is about exposure.

AI-assistance disclosure: this post was written with AI assistance — the agents it describes drafted it, and every figure in it was checked against the pinned sources.