1. Data source
  2. Decision schema
  3. Model inference
  4. Structured result
2 Decision schema 3 typed questions
  • verdictchoicepassretryfailinconclusive
  • qualityscorepoorfairgoodexcellent
  • needs_human_reviewnoulprobability 0–1

3 Model inference typesafe/jev-1.13

Waiting for a run.

4 Structured result

Run the demo to see the JSON answers, the highlighted pick and the final action.

Call Jev yourselfSame data and schema, straight to Flatkey. Set FLATKEY_API_KEY and run.
curl -sS https://router.flatkey.ai/api/alpha/decisions \
  -H "Authorization: Bearer $FLATKEY_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "model": "typesafe/jev-1.13",
  "state": "Goal: update staging deploy and verify health check returns 200. Agent steps: (1) ran git pull on staging host, (2) restarted the service, (3) claimed \"deploy complete and healthy\". Missing: no curl or health-check command was run; last log line is only the restart. Required verification step was skipped. Retries so far: 0.",
  "questions": {
    "verdict": {
      "type": "choice",
      "instructions": "Judge the agent run against the stated goal and required checks.",
      "criteria": {
        "pass": "The agent completed the goal and met the required verification steps with supporting evidence",
        "retry": "The run is incomplete or skipped a required check, but another attempt with the same setup is warranted",
        "fail": "The agent clearly missed the goal, violated policy, or produced incorrect or unsafe output",
        "inconclusive": "The transcript lacks enough evidence to judge pass or fail"
      }
    },
    "quality": {
      "type": "score",
      "instructions": "Overall run quality ignoring self-claims of success.",
      "criteria": [
        "poor",
        "fair",
        "good",
        "excellent"
      ]
    },
    "needs_human_review": {
      "type": "noul",
      "instructions": "A human or chat-model judge should review this run before the score is trusted."
    }
  }
}'

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About this use case

Use Jev as an agent-run judge: incoming state, Jev decision, routed outcome

Who / when

Audience

  • teams building agent eval and CI quality gates
  • platform engineers replacing LLM-as-judge with a typed scorer
  • builders who need stable pass / retry / fail signals at volume

Triggers

  • an LLM judge writes long reasoning and drifts run-to-run
  • you need a cheap pass / retry / fail / inconclusive call after every agent run
  • eval latency or cost is dominated by chat-model judges
  • you want auto-record only when confidence is high

The workflow

  1. 01
    Run transcriptGoal, steps, claims and evidenceInput
  2. 02
    Verdictpass / retry / fail / inconclusiveJev
  3. 03
    Qualitypoor / fair / good / excellentJev
  4. 04
    Confidence gateRecord, second-judge or escalateYour code

Question shape

{
  "verdict": {
    "type": "choice",
    "instructions": "Judge the agent run against the stated goal and required checks.",
    "criteria": {
      "pass": "The agent completed the goal and met the required verification steps with supporting evidence",
      "retry": "The run is incomplete or skipped a required check, but another attempt with the same setup is warranted",
      "fail": "The agent clearly missed the goal, violated policy, or produced incorrect or unsafe output",
      "inconclusive": "The transcript lacks enough evidence to judge pass or fail"
    }
  },
  "quality": {
    "type": "score",
    "instructions": "Overall run quality ignoring self-claims of success.",
    "criteria": [
      "poor",
      "fair",
      "good",
      "excellent"
    ]
  },
  "needs_human_review": {
    "type": "noul",
    "instructions": "A human or chat-model judge should review this run before the score is trusted."
  }
}

Gate policy

≥ 0.90

Auto-record the verdict and quality score in the eval log.

0.65–0.90

Send to a second judge or a human before recording.

< 0.65

Escalate to a human or chat-model judge; do not auto-record.

Failure modes

Pre-launch checks before you trust auto-routing:

  • Transcripts that only contain the agent's self-claim of success with no tool output;
  • Eval rubrics that change between runs (keep criteria fixed for stable scores);
  • Prompt injection in tool stdout, logs or retrieved documents inside the run;
  • Using a chat model to rewrite the judge's long reasoning after a typed score;
Keep exact work in code: rubrics, pass thresholds, retry budgets and which runs get auto-recorded should be deterministic. Use Jev for the semantic judge call; code enforces the gate bands.

FAQ

Why use Jev instead of another LLM as judge?

Most LLM evals ask a chat model to read a run, write reasoning, then emit a score—and the score drifts. LangChain tested Jev as the judge instead: about 0.44s per call with dramatically lower scoring variance. A typed Choice / Score / Noul layer behaves more like a measurement instrument than another chatbot.

How should I gate the judge output?

Auto-record when confidence is ≥ 0.90. Between 0.65 and 0.90, send the run to a second judge or a human. Below 0.65, escalate to a human or chat-model judge and do not auto-record.

Can I bring my own API key?

Yes. Try the free demo above without signing in. To run it live on your own text, sign in with Google and connect a Flatkey or OpenRouter key (a quick test call runs when you save it). Manage or delete it anytime under Dashboard → BYOK. Keys are stored encrypted on the server, used only for runs you trigger, and never sent back to the browser. Need a Flatkey key? Get one for Jev 1.13 on Flatkey.

Is this the official TypeSafe Jev product page?

No. jev-llm.com is an independent research and practice site. Verify pricing, models and terms at the original TypeSafe sources.

Sources

Seen on X
Independent-site note: jev-llm.com is an independent research and practice site. It is not the TypeSafe AI official site. Verify models, pricing and terms at the original source.