Jev AI decisions for every agent.

Jev AI brings structured decisions to your agents with an OmniaKey-powered playground, editable workflow presets, and MCP and Skills integration guides.

Open live playground Read integration docs

Connect Jev AI

jev-ai.site · jev-ai.online

Add the Jev MCP server

{
  "mcpServers": {
    "autojev": {
      "type": "http",
      "url": "https://autojev.ai/mcp",
      "headers": {
        "Authorization": "Bearer ${AUTOJEV_API_KEY}"
      }
    }
  }
}

Agent setup

Point your MCP client at the hosted AutoJev endpoint shown here.

Create an access key

Jev model guide

The Jev AI model decides. Generative models create.

Jev is TypeSafe AI's System One model for typed decisions inside software. Give the Jev model shared state and bounded questions, then receive choices, probabilities and scores instead of prose.

Read the complete Jev guide

TypeSafe Jev guide Jev AI for agents Jev model guide Jev API guide Jev ecosystem Jev Skills

DimensionJevGenerative LLM

OutputTyped choices, probabilities, and scoresGenerated text, code, and tool arguments

Best useRouting, classification, scoring, and gatesPlanning, explanation, coding, and creation

UncertaintyCalibrated probabilities are part of the responseConfidence handling depends on the workflow

Jev MCP

Jev MCP turns a decision model into tools an agent can discover.

Jev AI pairs Jev with validated inputs, editable workflow presets and structured answers through OmniaKey. The agent keeps its existing model, tools, permissions, and approval rules.

Explore Jev MCP

Decisions your agent should not make from instinct alone.

Jev AI makes Jev decisions available through editable workflow presets and a native API playground.

autojev_route_model

Smart model routing

Choose from the models you allow using task stakes, quality, cost, latency, context, and tool-use needs. Escalate when no candidate is safe enough.

Input: task + candidates
Signals: quality / cost / latency
Output: model + escalate

autojev_guard_tool_call

Tool-call protection

Inspect the proposed action, side effects, safeguards, reversibility, and your policy before an agent changes external state.

allow / confirm / review / deny

Research Guard

Judge whether the available evidence is sufficient, contradictory, stale, or ready to cite.

Completion Reviewer

Decide whether a task is truly done, needs one more check, or should return to implementation.

Custom decision contracts

Define your own choice, yes-or-unknown, and scored questions through the generic Decisions API.

One Jev decision layer, four ways to use it.

Jev AI brings together a live decision playground, native request examples and integration guides for agent workflows.

Jev MCP server

https://autojev.ai/mcp

Expose six typed Jev tools to Codex, Claude Code, Cursor, and any Streamable HTTP MCP client.

Configure MCP

OmniaKey Jev API

https://api.omniakey.com/v1/alpha/search

Send native Jev state and questions through OmniaKey for model routing, tool protection, research, task routing, completion, or custom decisions.

View API docs

Jev Skills for AI agents

https://autojev.ai/skills/autojev-*/SKILL.md

Install portable Jev Skills that teach an agent when to call each decision tool, what state to send, and how to apply the typed result without expanding authority.

Install Skills

Online Jev playground

/playground

Edit preset inputs, call Jev through OmniaKey, and inspect the model’s actual choices, probabilities, scores and token usage.

Start debugging

Live Jev API debugger

Inspect the decision before your agent acts.

Run Jev through OmniaKey with a workflow preset or your own native decision contract. Edit the JSON input and inspect the model’s actual answers, probabilities, and token usage. Your OmniaKey API key stays in memory and is never saved.

Choose a workflow preset and provide the task context. The preset defines bounded Jev questions, evaluates your input through OmniaKey, and returns the model’s original answers. Open the native request preview to inspect what will be sent.

OmniaKey API key

Used only for this request. The page does not persist the key.

Request endpoint

https://api.omniakey.com/v1/alpha/search

All presets use OmniaKey’s native Jev endpoint with the jev-latest model.

Workflow preset input

{
  "tool": "issue_customer_refund",
  "action": "Refund USD 680 after a disputed duplicate charge",
  "arguments_summary": [
    "order_id=ord_7429",
    "amount_usd=680"
  ],
  "side_effects": [
    "Moves funds",
    "Changes the order payment state"
  ],
  "safeguards": [
    "Customer identity and duplicate charge verified"
  ],
  "policy": [
    "Refunds above USD 500 require human approval"
  ],
  "reversibility": "partially_reversible"
}

View the native request sent to OmniaKey

{
  "model": "jev-latest",
  "state": {
    "tool": "issue_customer_refund",
    "action": "Refund USD 680 after a disputed duplicate charge",
    "arguments_summary": [
      "order_id=ord_7429",
      "amount_usd=680"
    ],
    "side_effects": [
      "Moves funds",
      "Changes the order payment state"
    ],
    "safeguards": [
      "Customer identity and duplicate charge verified"
    ],
    "policy": [
      "Refunds above USD 500 require human approval"
    ],
    "reversibility": "partially_reversible"
  },
  "questions": {
    "action": {
      "type": "choice",
      "instructions": "Evaluate the proposed tool call using policy, side effects, safeguards, authorization, and reversibility. Treat policy thresholds and missing approvals as constraints. This evaluation does not grant permission to execute anything.",
      "criteria": {
        "allow": "The action is authorized, within policy, and supported by adequate safeguards.",
        "confirm": "Ask the user to confirm intent or missing details before proceeding.",
        "review": "A policy approval or human review is required before this action can proceed.",
        "deny": "The action violates policy, lacks required authority, or must not proceed."
      }
    },
    "risk": {
      "type": "score",
      "instructions": "Rate the financial, operational, and policy risk of the proposed action.",
      "criteria": [
        "Low: routine and within policy.",
        "Moderate: limited, reversible effects.",
        "High: material effects or approval requirements.",
        "Critical: unsafe, unauthorized, or irreversible effects."
      ]
    }
  }
}

Create an access key

Structured response

answers + probabilities + usage

The typed Jev response will appear here.

Run the request to inspect the original Jev answers, confidence, probability distributions, resolved model version, and token usage returned through OmniaKey.

Decision boundaries

Use Jev where the workflow needs an answer, not another paragraph.

Each Jev AI preset turns compact workflow state into a bounded decision your agent can inspect and apply within its existing permissions.

Explore all use cases

Model routing

Choose the model that fits the task

Compare only the models you allow against quality, context, tool use, cost, latency and task stakes.

Workflow question: Which approved model can handle 100k context and tool use without wasting cost?
Example decision: reasoning-model

Read the use case Try this preset

Tool-call guardrails

Review an action before it changes external state

Evaluate side effects, safeguards, policy thresholds and reversibility before an agent calls a consequential tool.

Workflow question: Can the agent issue this high-value refund without human confirmation?
Example decision: review · 0.91

Read the use case Try this preset

Task routing

Select the next workflow path

Route ambiguous work to quick execution, deeper review, decomposition or a blocked state using explicit evidence.

Workflow question: Should the agent proceed, review deeply, split the task or stop?
Example decision: review_deep

Read the use case Try this preset

Research verification

Check whether the evidence supports the claim

Judge evidence coverage, contradictions, source quality and stakes before an agent cites or acts on research.

Workflow question: Is the current evidence sufficient to support this policy claim?
Example decision: insufficient

Read the use case Try this preset

Completion review

Keep unfinished work from being reported as done

Compare the objective, completed work, verification and known gaps before closing the task.

Workflow question: Is the refund task complete if the charge is identified but no refund was issued?
Example decision: verify_more

Read the use case Try this preset

The value is not another model endpoint.

Jev supplies structured judgment. Jev AI lets you test those decisions and inspect the original model answers before using them in your agent.

Decision engine

Jev evaluates

A System One model selects among explicit options and returns probabilities and scores.

Jev AI

Operational decision layer

Contract

Typed inputs and bounded outputs for each decision.

Connect

MCP tools, REST presets, and portable skills.

Trigger

Teach agents which boundary needs a second judgment.

Action policy

Map an answer to proceed, confirm, review, or stop.

Execution layer

Your agent acts

The agent keeps its model, tools, permissions, and human approval boundaries.

Agent integration

Connect once. Add judgment at every decision boundary.

MCP makes the tools discoverable. Skills teach when to call them. Typed results give the workflow a predictable next action.

Connect the MCP server

Add one remote endpoint and keep the AutoJev access key in an environment variable.

https://autojev.ai/mcp

Install the right skills

Give the agent explicit triggers for routing, tool protection, research, and completion checks.

.agents/skills/autojev-*

Evaluate with Jev

Send compact task state and bounded questions, then receive choices, probabilities, and scores.

choice + noul + score

Apply your policy

Let the workflow proceed, request confirmation, route for review, or stop without expanding authority.

allow / confirm / review / deny

Make the first guarded decision.

The agent discovers six typed tools over MCP. Add portable skills when you want it to call those tools at specific workflow boundaries.

Create an OmniaKey API key from your account settings.
Point your MCP client at the hosted AutoJev endpoint shown here.
Install all six AutoJev Skills in one step.

Create an access key Read the integration guide

Remote MCP configuration

Codex / config.toml

[mcp_servers.autojev]
url = "https://autojev.ai/mcp"
bearer_token_env_var = "AUTOJEV_API_KEY"

Streamable HTTP MCP

Bearer API key

Typed Jev output

Jev AI questions, answered.

Understand the Jev model, Jev MCP and Jev Skills before adding typed decisions to an agent workflow.

Give your agent a decision boundary.

Create an access key, connect the MCP server, and place typed judgment before high-impact actions.

Create an access key

Jev AI decisions for every agent.

The Jev AI model decides. Generative models create.

Jev MCP turns a decision model into tools an agent can discover.

Decisions your agent should not make from instinct alone.

Smart model routing

Tool-call protection

Research Guard

Completion Reviewer

Custom decision contracts

One Jev decision layer, four ways to use it.

Jev MCP server

OmniaKey Jev API

Jev Skills for AI agents

Online Jev playground

Inspect the decision before your agent acts.

Use Jev where the workflow needs an answer, not another paragraph.

Choose the model that fits the task

Review an action before it changes external state

Select the next workflow path

Check whether the evidence supports the claim

Keep unfinished work from being reported as done

The value is not another model endpoint.

Jev evaluates

Operational decision layer

Your agent acts

Connect once. Add judgment at every decision boundary.

Connect the MCP server

Install the right skills

Evaluate with Jev

Apply your policy

Make the first guarded decision.

Jev AI questions, answered.

What is Jev?

Is the Jev model a chatbot or generative LLM?

What are Jev Skills?

Why not call the Jev endpoint directly?

Why offer both MCP and skills?

Can Jev AI execute actions on its own?

What context will be sent to Jev?

Is Jev AI available now?

Give your agent a decision boundary.