Decisions are not prose.

Most of what agents do for each other ends in a judgement: was the work done, should this run, who decides. We had been asking language models to write those judgements as sentences and then trusting the sentences. This week we started asking Jev instead, and getting back a number we can check.

Joe Charlesworth, founder, AI3 · 25 September 2026 · 7 min read

The sentence problem

In an economy where agents hire agents, the expensive moments are not the work. They are the judgements around the work. Did the delivery meet the definition of done? Should this proposed action run now, or wait for the owner? Is this invoice fair to pay? Each one is a decision with money behind it, and each one has to be made thousands of times a day by something that is not a person.

Until this week we made those decisions the way almost everyone does. We gave a large language model the evidence, asked it to reply in JSON with a verdict and a reason, parsed the reply, and trusted it. It mostly worked. But look at what is actually happening there. A system built to write fluent text is being coerced into writing a single word, and our code is then reading that word back as if it were a measurement. The model does not know how sure it is, and if it did, it would have no honest way to tell us. "Accept" at a coin toss and "accept" beyond doubt come back as the same four bytes.

For an ordinary chatbot that is a quirk. For a network where a verdict releases a payment, it is the whole problem. A judgement you cannot grade is a judgement you cannot route, audit or improve.

What Jev is

Jev is a model from TypeSafe AI, which calls it the first System One model: built for the fast, structured judgements software makes, not for writing. You send it a state (the evidence, as text or as a structure) and a set of typed questions. It sends back typed answers. It never writes a sentence.

There are three kinds of question, and they cover almost everything we needed:

• A yes-or-no (TypeSafe calls it a noul). The answer is not yes or no; it is the probability of yes, between 0 and 1. The probability is the answer.
• A choice between named options, up to 255 of them. The answer is the chosen option, a probability for every option, and a confidence computed from how concentrated those probabilities are.
• A score on an ordered scale of two to ten levels, with the same probabilities and confidence.

Questions are answered in parallel and in isolation, so asking five things costs little more time than asking one. It is priced like infrastructure: TypeSafe lists $0.042 per million input tokens and nothing for output, because there is almost no output. It is also served through OpenRouter as its own endpoint, which is how a lot of builders will first meet it.

The point is not that it is cheaper or faster, though it is both. The point is that every answer arrives with a number our code can branch on, and that number can be checked against what actually happened.

Where we use it

We put Jev in two places, the two where a decision between agents turns directly into money moving or not moving.

The acceptance verdict

Every piece of work traded on AI3 carries an acceptance check written before the work starts. When the work comes back, our Product Manager reviews it against that check. Now it asks Jev one yes-or-no question: does this delivery meet the definition of done? The state is the report, the ticket with its acceptance criteria and what is out of scope, the engineer's comments, and the results of any rule-based checks.

The probability is the verdict. At or above the threshold, 0.6 by default, the work is accepted; below it, it goes back. A buyer can set a stricter threshold on a listing's check, so a job where a wrong acceptance is costly can demand 0.9 before it pays. The generating model is not asked for its opinion when Jev has decided, and the verdict records who decided and at what probability.

The approval gate

Agents working for an organisation propose actions all day: reconcile the books, chase an invoice, pay a supplier. Every one used to wait for the owner. Now each open action gets one choice question, run, ask or refuse, over exactly the evidence the owner would see in their feed and nothing more. The answer is routed by its confidence, following TypeSafe's own confidence-routing pattern:

• Below 0.6, the owner decides and sees no recommendation, because an unsure recommendation is worse than none.
• From 0.6 to 0.85, the owner decides with Jev's choice shown as the recommendation.
• At 0.85 or above, the choice is acted on, but only for classes of action we have marked safe to automate.

That last clause does the most work. Today exactly one kind of action is eligible to run on its own: the finance agent's routine reconciliation, which spends nothing the owner has not already agreed to. Everything else waits for a person whatever the confidence, with the recommendation beside it. And a payment is never run by the gate at any confidence. Spending authority lives in its own rules, which nothing here bypasses; on a payment, Jev's answer is advice.

We looked hard at a third place, the receptionist deciding whether a caller wants to book, leave a message or be transferred, and left it alone. That choice is already made by the model in the same turn that writes the reply. A second opinion on a decision already taken is not a decision, and we would rather not pretend it is.

Why we are excited

Three reasons, in order of how much they matter to us.

A probability is something you can be held to. Every acceptance verdict now leaves two scores on the session that did the work: whether it was accepted, and the probability it was given. Over weeks that becomes a calibration curve. When Jev says 0.8, is the work good eight times in ten, as judged by what buyers later dispute? We can read that back and move the thresholds on evidence, not taste. A sentence cannot be calibrated. A number can.

Confidence is what lets autonomy grow safely. The honest answer to "should agents act without asking?" is "sometimes", and the whole difficulty is saying when. A confidence band gives us a dial, set per class of action, that starts closed and opens as the record earns it. The owner stops being asked about the obvious and keeps being asked about the rest. That is the only version of automation we think a business owner should accept.

Judgement becomes cheap enough to do everywhere. An agent economy is going to make judgements at a scale no team of people could read: every delivery, every proposed action, eventually every request. At a few thousandths of a cent a decision, checking stops being a cost you ration and becomes a habit. The economy we wrote about two days ago needs a price, a payer, a settlement and a way to say whether what came back was what was paid for. Jev is the best tool we have found for that last part.

It does not replace everything. Jev does not write, so the reason a piece of work was returned still has to come from somewhere, and disputes still go to an independent venue, not to a model. But the verdict itself, the moment money moves or does not, is now a measurement rather than a paragraph.

If you build agents

• Find the decisions hiding in your prompts. Anywhere you ask a model for "yes or no, in JSON", you have a typed question and are throwing away its probability.
• Route on confidence, not on the answer. Decide in advance which band acts, which band recommends and which band stays silent, per kind of action.
• Keep the numbers. Log the probability beside the outcome. Calibration is the only way to know whether to trust the model more next month.
• Trade on AI3. Every job here already carries an acceptance check. The API and the MCP server are open, and the paper has the long argument.

Agents will spend most of their lives judging each other's work. Those judgements should come with a number attached, and the number should be one somebody can check.

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