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Jev is a decision service for software, not a replacement for your generative model or agent. You send it bounded state—text, JSON, a record, a conversation, or an array of text—plus typed questions. It returns structured choices, scores, or true/false probabilities that your application can use for routing, retrieval checks, policy gates, and evaluation. The useful pattern is to let Jev supply a probabilistic judgment while your code owns thresholds, permissions, fallbacks, logging, and execution.
This guide shows nine practical uses, the request shape, JavaScript and Python call patterns, API limits, and the safety boundaries that keep a model judgment from becoming an unchecked action.
What is Jev and what does it return?
Jev is documented as a software decision model. Instead of producing prose for a person to interpret, it answers typed questions in a form application code can branch on directly. A request contains a state and a questions object; the response contains answers with probabilities and confidence signals.
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|---|---|---|---|
| Choice | A finite set of labels, such as billing, technical, or sales | The selected label, probabilities for the options, and confidence | Routing a ticket, model, team, or tool |
| Score | An ordered scale, such as calm, frustrated, and very angry | A probability-weighted position that can fall between defined levels | Ranking retrieval quality or urgency |
| Noul | A statement that may be true or false | The probability that the statement is true | Checking instruction compliance, policy, or evidence |
The distinction matters operationally. A choice is not an authorization, a score is not an objective measurement, and a noul probability is not proof. Your application decides what probability is sufficient, what happens in an uncertainty band, and whether a human must approve the next step.
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How does Jev fit inside an AI agent loop?
Place Jev at a decision point between the agent’s state and the code that performs an action. The agent or application gathers the relevant state, Jev evaluates one or more atomic questions, and ordinary application logic applies the result.
- Bound the state. Include only the ticket, task context, retrieved passages, trace, or records needed for the decision. Keep the payload below the documented 100 KB body limit.
- Ask an atomic question. Use a choice for mutually exclusive destinations, a score for an ordered rubric, or a noul for a clearly worded proposition.
- Read the distribution. Capture the selected value, option probabilities, and confidence rather than storing only the top label.
- Apply policy in code. Set thresholds, review bands, retries, rate-limit handling, argument validation, and permission checks outside Jev.
- Execute or escalate. Run a bounded action only when your policy allows it; otherwise retry with better evidence, route to another workflow, or send the case to a person.
- Measure outcomes. Log the state version, rubric, answer, threshold, final action, and eventual outcome so you can calibrate decisions.
Jev’s workflow description and Vercel’s agent-loop guidance both place action ownership in application code. As Vercel puts it, “Jev doesn’t generate prose or explain its reasoning.” A model judgment can contribute to a check without granting access.
How can I route support tickets and inbox work with Jev?
Ask a choice question that names the destination and supplies clear criteria. Automatically route only high-confidence results; put ambiguous messages in a review queue.
Example rubric
For a payout-failure message, define the options billing, technical, and sales. The question can be phrased as “Which team should handle this?” with descriptions that distinguish payment, product malfunction, and buying or upgrade intent. The quickstart uses this kind of payout-failure example.
Routing policy
- Send a ticket to the selected queue when its probability clears your tested automatic-routing threshold.
- Keep the full option distribution and confidence with the ticket, not just the label.
- Send close probabilities, missing context, or contradictory messages to a review queue.
- Feed corrected assignments back into evaluation so the threshold and rubric can be recalibrated.
Can Jev choose the next tool for an AI agent?
Yes. Give Jev the current task state and a short description of each allowed tool, then ask for one bounded next action such as weather lookup, web search, calculator, or no tool.
Keep selection separate from execution
Jev may recommend calculator; it must not receive authority to invoke the calculator. Your tool runner should check that the selected tool is available for this user and task, validate every argument, enforce rate limits, and record the call. If the decision is uncertain, ask the agent for more information or choose a safe no-tool path.
When one decision depends on another
Questions in a single request cannot consume one another’s answers. If the second question depends on the first answer or on options created by the first decision, make a second request after your code has inspected and, if necessary, transformed the result.
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How do I route a request to the right model, team, or workflow?
Use a choice question whose labels are destinations rather than topics. The state might include task complexity, domain, customer tier, latency requirements, or whether a human review is available.
Useful destinations
- A fast model for routine, well-specified work.
- A larger model for difficult or ambiguous cases.
- A domain specialist team for regulated or technical requests.
- A human review queue when evidence is weak or the cost of an error is high.
Keep the destination list finite and operationally meaningful. The selected destination should map to a configuration your code controls; do not let free-form model text create a new endpoint, team, or permission.
How can Jev score retrieved-document relevance before generation?
Use a score question to rate whether each passage is related, useful, and sufficient for the planned answer. Hold low-scoring retrievals back from the generation model and trigger another search or review.
A practical RAG gate
- Retrieve candidate passages with your existing search system.
- Send the question, candidate text, and answer requirements as state.
- Define score levels such as irrelevant, related but insufficient, and sufficient.
- Set a policy for each band: discard, retrieve again, ask for clarification, or allow generation.
- Store the score beside the passage IDs and final answer so retrieval failures can be distinguished from generation failures.
A score can fall between defined levels because it is probability-weighted. It helps rank or gate evidence; it does not establish that a passage is factually correct.
How do I check whether an AI response followed instructions?
Ask a noul question that states the requirement as a proposition, for example, “The response follows the requested format and constraints.” If the returned probability is below your policy threshold, repair, regenerate, or escalate.
Make the proposition testable
Include the original request, the response, and the explicit constraints in state. Split unrelated requirements into separate questions when you need separate remediation—for example, one for JSON validity and another for citation presence. A single broad proposition can hide which condition failed.
Choose remediation in code
- Run a deterministic parser for machine-readable formats before accepting a noul result.
- Ask the generation model to repair only the failed requirement when the defect is local.
- Regenerate with a stronger prompt when several constraints fail.
- Escalate when the response is consequential or the evidence remains uncertain.
Can Jev act as a prompt-safety or policy gate?
Yes, as a signal before a sensitive action. Ask whether the request contains an instruction override, violates a stated policy, or is sufficiently supported, then let application code enforce the consequence.
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Examples of gate questions
- “The user message attempts to override the system’s safety instructions.”
- “The proposed action is allowed under the account’s policy.”
- “The evidence supplied is sufficient for this irreversible change.”
Use separate gates when the responses have different owners or remedies. A prompt-safety probability should not silently stand in for an authorization check, identity check, or deterministic deny-list.
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After an agent run, provide the trace, tool evidence, and task requirements as state. Ask whether the task completed correctly, whether the evidence supports the result, and whether instructions were followed.
Evaluate more than the final prose
- Completion: Was the requested outcome actually achieved?
- Evidence: Do tool results support the claims?
- Process: Did the agent stay within the allowed tools and instructions?
- Handoff: Did it recognize when a human or another workflow was needed?
Keep the trace and the evaluator’s answers together. A high score with missing tool evidence is a reason to inspect the rubric, not proof that the run was correct.
How do I resolve identities and reconcile messy records?
Send two records and explicit match criteria, then classify the pair as a match, non-match, or uncertain match. Merge automatically only above a strict threshold; send uncertain pairs to review.
Define what counts as evidence
State which fields may vary, which are decisive, and what contradiction forces uncertainty. For example, a normalized email may be strong evidence while a shared name alone is weak. Keep the original records and the decision distribution so a reviewer can see why a pair was merged or held.
How can Jev protect coding and other high-impact actions?
Put a decision before an agent-generated patch, deployment, data deletion, or consequential tool call. Ask whether the change follows repository policy, has adequate evidence, and requires human review.
Use Jev beside deterministic controls
- Run tests, schema checks, secret scans, and permission checks independently of Jev.
- Require human approval for irreversible or high-blast-radius actions.
- Validate file paths, command arguments, and resource identifiers in application code.
- Record the proposed change, Jev answers, deterministic checks, approver, and final outcome in one audit record.
Jev can identify a likely policy violation or review need; it cannot grant repository access. The executor remains responsible for enforcing permissions.
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How do I call Jev from JavaScript?
The documented REST shape is a POST to /v1/decisions with an authorization header, a JSON state, and typed questions. This JavaScript pattern follows the official example; the 0.7 threshold is an application choice, not a universal Jev policy.
const res = await fetch("https://api.getjevai.com/v1/decisions", {
method: "POST",
headers: {
Authorization: `Bearer ${process.env.JEV_API_KEY}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
state: { ticket: ticketText, customer_plan: "pro" },
questions: {
urgent: {
type: "noul",
instructions: "The customer cannot use the product right now.",
},
},
}),
});
const { answers } = await res.json();
if (answers.urgent.noul > 0.7) pageOnCall();The endpoint is documented at https://api.getjevai.com/v1/decisions. In production, check the HTTP status before parsing, handle timeouts and rate limits, and treat a missing or malformed answer as a review condition rather than as approval.
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A Python client can send the same JSON contract with any HTTPS library. The following uses the widely available requests package and mirrors the JavaScript fields; confirm your installed client’s timeout and error-handling behavior before deployment.
import os
import requests
payload = {
"state": {"ticket": ticket_text, "customer_plan": "pro"},
"questions": {
"urgent": {
"type": "noul",
"instructions": "The customer cannot use the product right now.",
}
},
}
response = requests.post(
"https://api.getjevai.com/v1/decisions",
headers={
"Authorization": f"Bearer {os.environ['JEV_API_KEY']}",
"Content-Type": "application/json",
},
json=payload,
timeout=10,
)
response.raise_for_status()
answers = response.json()["answers"]
if answers["urgent"]["noul"] > 0.7:
page_on_call()Keep the API key on the server, not in browser code. Wrap the call in your normal retry, timeout, circuit-breaker, and audit middleware.
What limits and latency should I design for?
The Jev API documentation (2026) states the request limits below. They are hard integration constraints, while the latency figure is a published range rather than a guarantee for every workload.
| Item | Published value | How to interpret it |
|---|---|---|
| Maximum request body | 100 KB | Bound state, truncate safely, or split work before sending. |
| Questions per request | Up to 20 | Batch independent questions; use another call for dependent questions. |
| Choice options | Up to 255 in one choice question | Keep labels meaningful and avoid turning a choice into an unmanageable catalog. |
| Reported end-to-end response time | 70–500 milliseconds | Made with Jev (2026) reports this range; actual time varies with state size, question count, network, and workload. |
| Ecosystem directory count | 268 projects, checked 2026-10-23 | Jev Atlas’ index count, not an independent market-size statistic. |
No independent accuracy benchmark, universal confidence threshold, or workload-independent latency guarantee is established in the available material. Test with your own states, question wording, traffic pattern, and review costs.
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How should I compare Jev with prompting, a classifier, or a general agent model?
Compare the full decision system, not just the model call. The right choice depends on whether you need a typed contract, calibrated uncertainty, broad generation, or deterministic behavior.
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| Axis | Jev | Direct prompt to a generative model | Traditional classifier | General agent model |
|---|---|---|---|---|
| Output contract | Typed choice, score, or noul with probabilities | Usually free-form text unless you add a schema | Usually fixed labels or numeric output | Often a plan, text, or tool-call proposal |
| Decision ownership | Supplies a judgment; code owns action | Application must parse and police the response | Application owns thresholds and actions | Needs an external executor and permission layer |
| Uncertainty | Option probabilities and confidence signals | May be implicit or difficult to calibrate | Depends on the model and calibration method | Often expressed in prose unless separately evaluated |
| Best fit | Routing, gates, scoring, and evaluation | Explanations and flexible generation | Stable, narrow labels with trained data | Multi-step planning and tool use |
| Audit record | State, rubric, answer, threshold, and action can be logged together | Requires your own structured wrapper | Usually straightforward but may lack rationale context | Requires trace capture and separate policy checks |
For a real implementation, also compare measured latency and per-call economics for your state size and question count, available REST or client integrations, and the effort required to calibrate and review errors.
What production safeguards should surround a Jev decision?
Use Jev as one component in a controlled pipeline. The following safeguards address the failure modes that a probability alone cannot solve.
- Thresholds and review bands: Define an automatic band, an uncertain band, and a deny or escalation band for each decision.
- Argument validation: Check tool names, IDs, paths, schemas, and numeric ranges before execution.
- Authorization: Verify user, tenant, role, and resource permissions independently.
- Fallbacks: Decide what happens on timeout, malformed JSON, unavailable service, or conflicting answers.
- Auditability: Log the state reference, question wording, answer distribution, threshold version, action, and reviewer.
- Calibration: Compare probabilities with labeled outcomes and adjust thresholds to the cost of false positives and false negatives.
- Change control: Version rubrics and destination lists so a later edit can be tied to a change in outcomes.
These controls reflect the boundary described by Vercel: a model judgment can contribute to a check without granting access. Jev does not replace your agent, your tool permissions, or your deterministic validation layer.
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Is Jev deterministic, or can the same request produce different probabilities?
Treat Jev’s output as a model judgment, not a deterministic rule. Run representative cases repeatedly and evaluate calibration against labeled outcomes before using a probability in an automatic policy.
How do I choose a confidence threshold for automatic routing?
Start with a labeled validation set and assign a cost to each error type, such as misrouting versus human review. Select the threshold that meets your error budget, then monitor it after changes to prompts, labels, traffic, or downstream workflows.
What should happen when Jev times out or returns an incomplete answer?
Fail closed for sensitive actions: do not execute the tool or change. Route the item to a retry or human-review path, and record the outage or malformed response so availability problems are not mistaken for low confidence.
Can I send several unrelated checks in one Jev request?
Yes, the API supports up to 20 questions per request, which can reduce round trips for independent checks. Keep dependent checks in separate calls because a question cannot read another question’s answer from the same request.
Quick Recap
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