463 passing tests. A blank website. Both true at once.
Every test was green. Every page was empty. Here's how both can be true — and the one check that would have caught it.
Practical answers from real work
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Understand what AI can do in a business, how to hand it real work, and how to know whether that work was actually done. Start with a question or learn from a real failure.
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Real examples
What happened when AI did the work.
What I handed over, what came back, how I checked it — and the part you can try in your own business.
Real cases from the lab's businesses.
The constitution I gave my AI company
An AI company that runs on agents needs rules they can't argue their way out of. So I wrote them down, and I am wiring each one to a gate, because a rule an agent can ignore isn't a rule.
The smartest thing my night-shift agent did all month was refuse to publish
My content agent turned down a ninth near-identical post and worked on the sixteen already live. That's a better night's work than another URL.
The shared messaging system went dark for 72 minutes. We lost nothing, but the requests waiting on me looked ignored.
A dead notifier doesn't drop messages. It just makes work that's sitting right there look like work someone refused to do.
I don't let my own AI say a high-stakes release is done. A rival AI signs off first
The builder said ready. A different model family — a different line of AI models — disagreed. The useful part was making both of them prove it.
We gave two different agents the power to block a release — on purpose.
One agent decides if it's built right. A different one decides if anyone will pay for it. Neither can call it 'done' alone. That split is the whole point.
A green checkmark that isn't the AI's own word: building a quality check with teeth
My quality-check tool failed all 24 controls on a live app. Every failure was in the tool. A confident red can be just as fictional as a confident green.
I put my AI staff on a payroll page — with real commit counts and blanks where I don't measure yet
The commit counts come from real work. The salary column says '—.' I don't measure it yet, and a plausible number would make the page less honest.
1,000 commits looked lost. They were staged in a pull request nobody had merged for six days.
The scary number wasn't lost work — it was work waiting for a human to merge it, which nobody did. The fix was a check, not a rebuild.
A green checkmark said 4 people were reachable. The real number was 250.
The agent hadn't broken the rule. It had applied the rule to half the data — and every step after that looked correct.
98 commits in a week. The commit count tells me almost nothing.
Proof that software work can be handed over. Not proof that the software works.
21 identical blocked messages, for a credential that never existed
An agent invented a requirement, decided it could not proceed without it, and reported the exact same status update twenty-one times before anyone asked what it actually was.
Three ways to fake a receipt. All three got caught.
Once 'show me the receipt' became the standard, the obvious next question was: what happens when someone tries to forge one?
Three months. ~2,985 commits. 88–100% internal scores. Zero paying users.
Every gate the agent controlled turned green. The one gate that mattered — a real person using the product — never got touched.
We built the lie-detector. It just wasn't plugged in.
The mechanism that catches false status reports was built, tested, and verified un-bypassable — and then sat disconnected from the system it was supposed to be watching.
The rule wasn't 'don't lie to me.' It was 'don't lie to any agent, including yourself.'
A blank page reported as done and a safety check reported as armed both traced back to the same thing: a claim nobody could check. That became the one absolute rule.
250 subagents, 300 uncommitted files, and a 92% wrong answer
Handing work to more agents without a limit looked like speed. It was 250 agents editing the same shared files and grading the wrong source of truth, together, at once.