Structured decision playground

Try Jev AI for structured decisions.

Give Jev a state, define the allowed answers, and get a typed result your software can use without parsing generated prose. Try the live tool, then see how structured decisions fit inside an agent that can actually take action.

Try Jev online Meet the OpenClaw agent

State

The context your software needs to evaluate

Question

One focused Choice, Noul, or Score request

Typed result

An answer with its probability distribution

Structured decision demo

Try a structured decision

Ask one focused question and receive a typed answer with probabilities your software can use.

Decision result

Run the example or enter your own state to see the selected answer and probability distribution.

This is an independent educational playground. Use non-sensitive test data and evaluate the decision pattern before applying it to a real workflow.

Why structured decisions matter

A typed decision is easier for software to use

Most language models are optimized to continue a conversation. Ask which team should handle a support request and they may return a helpful paragraph, caveats, and several possible routes. That is useful for a person, but an application still has to extract the answer, check that it is valid, and decide whether the model sounded certain enough.

Jev approaches the same problem as a bounded decision. You supply the current state, ask one focused question, and define the output shape in advance. Choice selects from allowed options, Noul measures a true-or-false proposition, and Score evaluates an ordered scale. The response includes probabilities, so the surrounding workflow can use an explicit confidence threshold instead of guessing from prose.

This pattern is especially useful at the narrow points where an agent must branch: choose a tool, classify a request, assess urgency, request approval, retry a failed step, or stop a loop. It does not replace a general-purpose model. It gives the model a smaller, testable job whose output can connect directly to code.

Three ways to structure an answer

Choose the output shape before inference so every answer maps cleanly to application logic.

Choice

Choice picks exactly one item from the options you provide. Use it for routing, tool selection, and bounded classifications.

choice: Billing

Noul

Noul answers a yes-or-no question with the probability of true. Use it for checks, filters, and approval gates.

P(true) = 0.90

Score

Score evaluates an ordered rubric and returns a weighted score plus the probability of every level.

score = 1.37 / 2

The idea behind the model

What is a System One model?

Jev is described as a System One model: a model focused on fast, repeated judgments rather than open-ended conversation. The name borrows the distinction between quick, intuitive decisions and slower deliberative reasoning. In software, that means narrowing a task to a state, one question, and an output type the application already understands.

This design is useful when the surrounding program needs a bounded answer, not an essay. A support system may need one destination queue. An approval flow may need a probability that a review is required. A monitoring agent may need an ordered severity score. The model still makes an uncertain prediction, but the uncertainty is returned in a form the program can inspect.

Structured output is not the same as guaranteed correctness. Probabilities need calibration against your own examples, and a confident result can still be wrong. Treat the model as one decision component inside a larger policy that includes validation, thresholds, logs, and human review for consequential actions.

What the Jev playground can—and cannot—show

This independent educational page demonstrates the structured-decision pattern in a live interface. It is useful for learning and prototyping, but a short browser test cannot establish production quality, safety, or reliability.

This page demonstratesThis page does not establish

Output shapeChoice, Noul, Score, and probabilitiesEquivalent behavior in every deployment

UncertaintyHow confidence can guide a workflowCalibration on your own data

IntegrationA browser and API interaction patternProduction reliability or service levels

EvaluationHands-on prompt and policy testingA benchmark, security review, or safety certification

From state to action

Define the state

Provide only the facts needed for this decision.

Constrain the answer

List the outcomes your software can actually handle.

Use the probabilities

Automate high-confidence results and review uncertain ones.

Decision models and chat models do different jobs

Decision modelChat model

Primary outputTyped decisionGenerated text

Best fitRouting and scoringWriting and explanation

Application stepBranch directlyParse and validate

UncertaintyProbability distributionUsually expressed in prose

What to verify before production

A typed response removes parsing work; it does not remove model risk. Test the complete decision policy with representative and adversarial inputs before allowing any result to trigger a real action.

Measure on your own cases

Build a labeled evaluation set that includes ordinary requests, edge cases, missing context, conflicting evidence, and options that are deliberately close together.

Set thresholds by consequence

A low-impact routing choice can tolerate more uncertainty than deleting data, changing credentials, spending money, or contacting a customer.

Treat input as untrusted

Constrained output does not prevent prompt injection or misleading instructions inside the state. Sanitize inputs and keep the allowed actions narrow.

Design a fallback

Send low-confidence, malformed, timed-out, or high-impact decisions to a person. Never turn an unavailable endpoint into automatic approval.

Operate your own endpoint

For production, protect the service with authentication, rate limits, timeouts, monitoring, versioned prompts, and request logs that exclude sensitive data.

A small integration pattern

Send the state, question, decision type, and allowed options to the local proxy. Branch only after checking that the request succeeded and that confidence meets the threshold chosen for that action. The example below matches this playground's API shape; it is an integration sketch, not a universal threshold recommendation.

const response = await fetch("/api/openjev/decide", {
  method: "POST",
  headers: { "Content-Type": "application/json" },
  body: JSON.stringify({
    state: ticketText,
    question: "Which team should handle this request?",
    type: "choice",
    options: ["Billing", "Technical Support", "Sales"],
  }),
});

const payload = await response.json();
if (!response.ok || !payload.success) return queueForReview();
if ((payload.result.confidence ?? 0) < 0.85) return queueForReview();
return routeTo(payload.result.decision);

Useful inside real workflows

Support routing

Send each request to the team equipped to solve it.

Agent tool selection

Choose the next allowed tool from a controlled set.

Risk checks

Route uncertain or high-impact actions to a person.

Loop control

Decide whether an agent should continue, retry, ask, or stop.

Questions about the playground

What does this playground demonstrate?

It demonstrates how a bounded state, one focused question, and a predefined answer type can produce a decision with probabilities that software can inspect.

Can I send private data to this playground?

No. Treat this tool as a public demonstration. Do not submit passwords, personal information, payment details, confidential business data, or regulated information.

Can I use this public endpoint in production?

Use the public endpoint for evaluation and prototypes. It has no service-level guarantee and may cold start or reach capacity. A production integration should deploy and protect its own endpoint.

From decision to action

From a decision model to an action-taking agent.

A Jev decision endpoint is one component of an agent workflow. OpenClaw is the agent you can talk to like a coworker: it can use a browser, work with files, run tools, connect to chat apps, and stay available in its own cloud instance. Use a structured decision when a workflow needs a reliable branch; use OpenClaw when you want the workflow to continue and complete real work.

Ask naturally

Describe the outcome you want in the same way you would brief a teammate.

Use real tools

Let the agent browse, work with files, and execute multi-step tasks.

Keep it running

Use a dedicated cloud instance for ongoing and scheduled workflows.

Try the OpenClaw agent Open my OpenClaw

No local installation required. Choose a hosted plan and start from your browser or chat app.