Jev automation works by splitting your agents' work into what most of it actually is — tiny sorting decisions — and handing those to a model that answers picks, ratings and yes/nos in a few hundred milliseconds with a confidence score, so the big models only run when something genuinely needs writing or deep reasoning.
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That is the whole architecture. This page is the wiring guide: the seven automation patterns that keep showing up, and the one safety primitive — the confidence line — that makes it sane to leave any of them running while you sleep. The full catalogue of examples lives in my Jev AI use cases roundup; this page is about how to wire it.
What Your AI Agents Actually Do All Day
Here is the framing from my Awesome Jev video, and it changed how I look at every automation I own: watch what your AI actually spends its day doing.
Most of it is not writing. It is sorting.
Which folder does this go in. Which message deserves a reply. Is this a real lead or a time-waster. Is this task finished, or should a human look at it first.
Every one of those is a pick, a rating or a yes/no. And every one currently costs you a full round trip to a big, slow model built for essays.
Jev is the specialist for exactly that. It answers picks, ratings and yes/nos, and attaches a probability to every answer. Once you see your agent's day as a stream of small decisions, Jev automation becomes obvious plumbing.
This decision-first way of thinking is exactly what we drill inside AI Profit Lab — 3,000+ members building real automations together, with the price locked at $69/month (it is normally $110).
Jev Automation: The Seven Patterns
Every working Jev automation I have seen — mine, and the community's — is one of seven patterns. Learn the shapes and you can wire almost anything.
| Pattern | The decision Jev makes | Shape |
|---|---|---|
| Email intent routing | Which bucket does this message belong in? | Pick |
| Lead scoring | How strong is this lead, and does the draft fit? | Rating plus yes/no |
| Keyword pipeline | Buyer intent, informational or nonsense? | Pick |
| Model routing | Which model should handle this task? | Pick |
| Coding-agent routing | Which model, and how much reasoning effort? | Pick |
| Computer use | Is this action sensitive enough for a human? | Yes/no |
| Quality gates | Is this good enough to ship? | Yes/no |
1. Email intent routing
Treat each inbound email as the situation and your buckets as the options: invoice, general, needs-reply. Jev detects the intent and routes the message. The sure ones move on their own; the unsure ones queue for you. Only genuine judgement calls ever reach your inbox.
2. Lead scoring with mismatch flags
Score every lead weak, medium or strong. Then ask the second question almost everyone skips: does the drafted message actually fit this person? That mismatch flag is the difference between automation that books calls and automation that burns lists. A builder called Roman ran 700 leads through this exact pattern — 40 seconds, 9 cents — a run I cite in the Awesome Jev video.
3. The keyword and data pipeline
This one is mine. I take Google Search Console exports and have Jev sort every query: buyer intent, informational or nonsense. Thousands of rows become three clean piles, and the money pile gets my attention first. I show the workflow in my videos, and the full SEO build lives in my Jev SEO guide.
4. Model routing with a human in the loop
Describe each model's strengths in plain English and let Jev route every task between local, cheap and frontier models. If you are not sure which model is strong where, Goldie Bench is how I test them. The variant I describe in the video adds one more router question: does this action probably need a human? Anything risky gets held for approval instead of executed. That single question turns an agent from a liability into a colleague.
5. Coding-agent routing
From the community list: the PyJev router. Jev picks the model and the reasoning effort — auto means the lowest effort it judges sufficient, high forces it up — and keeps both fixed for the session, so your coding agent stops flip-flopping mid-task. The rest of the community projects live in my Awesome Jev GitHub roundup.
6. Computer use with safeguards
Jev Desktop for Codex, also from the community list, splits the job three ways: you give the goal, Codex plans and scopes it, and Jev operates fast — with sensitive actions diverting to a human before they happen. Yappy, the macOS voice agent, runs the same pattern on a Mac. Fast hands, careful planning, human veto.
7. Pre-publish quality gates
Before anything ships — a post, an email, a deploy — run yes/no checks with probabilities attached. Is the claim sourced? Does the tone match? The confidence line decides what publishes and what waits for review, which brings us to the part of Jev automation that matters most.
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The Confidence Line: The Safety Primitive
Every Jev answer carries a probability. That one sentence is the spine of this entire page.
You set one confidence line per workflow. Above the line, the automation acts alone. Below it, the item waits for you.
That single dial is what makes leave-it-running automation sane. Not a bigger model, not a longer prompt — a number you chose, sitting between the decision and the consequence.
It is also why the unsure pile stays small enough to actually review. The automation drains everything it is confident about, so what reaches you is short, genuinely ambiguous and worth your judgement. Compare the usual alternatives: review everything, and you never automated anything; review nothing, and one bad decision runs unsupervised at 3am.
Set the line strict where mistakes touch other people — outbound messages, published pages, money. Set it looser where a mistake just costs a re-run. Every pattern above is the same machine with a different line.
Not sure where to set your first line? Bring one workflow to a free AI automation strategy session and we will place it together.
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Speed and Cost: The Numbers
The tests referenced in my video measured 275 to 690 milliseconds per decision. That is fast enough to put a Jev question inside loops you would never trouble a big model with — per email, per lead, per row.
Established pricing is $0.042 per million input tokens, and output is free. That is how a 700-lead scoring run comes in at 9 cents.
Questions batch in parallel, so ten decisions cost about the latency of one. Score the lead, check the draft fits, ask whether a human should look — one beat of the loop, not three.
Test Before You Wire Anything
Do not start in your codebase. Start at jevplayground.com — the independent browser playground. No signup, and it ships with prefilled examples like support routing and agent-next-action, so you can watch how the confidence scores move before you commit to anything.
When a decision behaves in the playground, move it to the real API at console.typesafe.ai — or through OpenRouter, where Jev is in beta, as I cover in the SEO video. My how to use Jev walkthrough handles setup step by step, and the Jev AI API guide covers the request side in detail.
The order matters: playground to prove the decision, API to wire it, confidence line to make it safe.
Want my eyes on the wiring first? Book a free AI automation strategy session and bring your ugliest workflow.
Picking Your First Jev Automation
My long-standing rule: automate something boring you decide every week. Not automation for its own sake. A real, recurring, tedious decision where you already know the right answer most of the time — that is the perfect first Jev job, because you can check its work against your own instincts.
The shortcut: paste the Jev docs into your own agent and ask it for use cases based on what you actually do day-to-day. It will surface decisions you stopped noticing you make. If you run a full agent stack, my Agent OS guide shows where a decision layer like this slots in.
Then wire exactly one pattern, set the confidence line strict, and loosen it as trust builds. Members inside AI Profit Lab post these builds every week — stealing a working setup beats inventing your own mistakes.
Jev Automation FAQ
What can Jev automate?
Anything that reduces to a pick, a rating or a yes/no: routing email by intent, scoring leads and flagging mismatched drafts, sorting keyword exports, routing tasks between models, fixing a coding agent's model and effort, holding sensitive computer-use actions for approval, and pre-publish quality gates. Writing still goes to a big model — but Jev decides which one, and whether a human looks first.
Is Jev automation safe to leave running?
Yes — if you use the confidence line. Every Jev answer carries a probability; you set one line per workflow. Above it, the automation acts alone; below it, the item waits for you. Give risky actions their own does-this-need-a-human question, and the pile waiting for you stays small enough to actually review.
What does it cost?
Established pricing is $0.042 per million input tokens with output free, and the tests referenced in my video measured 275 to 690 milliseconds per decision. Batching runs in parallel, so ten questions cost about the latency of one. Trying it costs nothing: jevplayground.com runs in the browser with no signup.
Ship One Boring Decision This Week
That is Jev automation: your agents' day seen as sorting, one of seven patterns wired, and a confidence line between the automation and the world. Milliseconds and fractions of a cent for decisions; big models saved for real work; you reviewing only what deserves you.
If you want to build alongside people shipping these systems daily, join us in AI Profit Lab — 3,000+ members, with the price locked at $69/month instead of the usual $110.
Or book your free AI automation strategy session, tell me the boring decision you make every week, and we will design the automation that removes it.
