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AI does the work. Your people decide what ships.

cken gives every agent a queue to pull from, a spending ceiling it can't cross, and a reviewer at the end — so you can put AI on real work and still explain what happened.

No card. Failed runs are never billed.

Most AI work fails on the second question.

The first question — can a model do this task? — got answered a while ago. The second one is where teams stall. Who is accountable when it's wrong? What did it cost? What exactly did it do? Who said it was fine to send? A chat window can't answer any of that, so the work either stays a demo forever, or it ships and nobody can explain it.

  • 01

    It ran, and nobody can say what it did. The output is in a Slack thread, the prompt has been edited twice since, and the person who ran it has left.

  • 02

    It cost real money before anyone noticed. There was no ceiling, so the ceiling was the invoice.

  • 03

    It got better at the demo and worse at the job. A prompt change fixed the case someone complained about and quietly broke four that nobody was watching.

Four steps, in the order they happen.

A task moves through these four states and can't skip one.

  1. 01

    Give it work.

    Point cken at a queue, a schedule, or an inbound webhook. A task is a row with a status, not a conversation you have to babysit. Ten thousand of them behave the same as one.

  2. 02

    It runs under a ceiling.

    Every run gets a spending cap, a timeout and a retry policy before it starts. When it hits the cap it stops and says so. There is no version of this where you find out from the bill.

  3. 03

    A person signs off.

    Finished work lands in a review queue — approve, edit, or reject, keyboard-first, routed by rule to whoever owns that kind of work. Approval is the only thing that releases a result.

  4. 04

    Evals keep it honest.

    The examples your reviewers approved become the test set. Change a prompt or a model and cken scores the change against them before it reaches production, so improvements are measured rather than hoped for.

Six blocks. Each one is a control, not a capability.

Every headline here is written from the operator's side: what you can now guarantee.

Queues and schedules

Work comes in. Something picks it up.

Push a task, schedule one, or let an event create it. Every task is durable — a run that dies mid-flight is picked up again, not lost.

  • Priorities, retries and dead-letter handling
  • Recurring work on a schedule
  • Backpressure instead of silent drops
Budgets

Nothing runs without a ceiling.

Set a cap per run, per workspace, per month. A run that would exceed it stops before spending, and tells the person who queued it why.

  • Per-run and per-workspace caps
  • Real cost per run, not an estimate
  • Failed runs are never billed
Review and approval

Nothing ships without a name on it.

Results queue up for a person. Approve, edit or reject — and the edit is kept, because an edited result is the most valuable training signal you own.

  • Routing by rule, assignment, and an SLA clock
  • Keyboard-first for people reviewing all day
  • Edits captured as examples, not overwritten
Evals

Catch the drift before your customers do.

Your approved and rejected results become a scored test set. Every prompt or model change runs against it first, with the score attached to the change.

  • Test sets built from real reviewed work
  • Score every change before it ships
  • Block a release that regresses
Memory

It remembers your preferences, not just your prompt.

Corrections your reviewers make stick. The tenth run of a kind of work starts from what your team decided on the first nine.

  • Scoped per workspace and per project
  • Readable and editable — you can see what it thinks it knows
  • Deletable, one entry at a time
Audit trail

Every run, replayable.

Inputs, outputs, tools called, cost, duration, approver, timestamps. Months later, you can show someone exactly what happened.

  • Full run history, filterable
  • Retention you set, not the one we picked

The comparison you're already making.

Not between cken and a competitor. Between cken and three things you already have.

CapabilityA chat windowA workflow builderScripts your team wrotecken
Who does the workA person, promptingSteps you drewCode, until it breaksAn agent, from a queue
Who's accountableWhoever pasted itNobodyThe author, unfindableA named approver
When it's wrongYou notice, maybeIt completes anywayIt retries foreverRejected before it ships
What it costUnknownPer task, not per model callUnknownPer run, before and after
What you show an auditorA Slack threadA run logApplication logsThe run, replayable
What happens at 10,000It doesn'tPer-task pricing bitesSomeone becomes on-callSame thing, queued

Workflow builders are good tools. If your task is deterministic, use one — it's cheaper and it'll be right every time. The distinction here is the reviewer and the record, not capability.

A run is one task an agent picks up and finishes.

Retries of the same task don't count twice. A run that fails before producing anything isn't billed. That's the whole meter.

We don't bill for runs that didn't finish.

If a run fails before it produces output, it costs you nothing — no credit to claim, no ticket to open.

Answered before you ask.

What counts as a run?

One task an agent picks up and finishes, success or failure. Retries of the same task are still one run. A run that fails before producing output isn't billed at all.

Can it run without a human approving?

Yes — you can auto-approve a class of work once its eval scores earn it, and keep review on everything else. The default is review on, because that's the one setting people regret turning off.

What happens when the AI gets it wrong?

A reviewer rejects or edits it, and the result never leaves cken. The edit is kept as an example, so the same mistake gets caught by the eval set the next time someone changes a prompt.

How is this different from a workflow automation tool?

Those tools run the steps you drew. cken runs work you didn't fully specify, under a budget, and puts a person in front of the result. If your task is deterministic, use the workflow tool — it's cheaper and it'll be right every time.

A queue, a budget, and a reviewer for every AI run.

No card. Failed runs are never billed.

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