Jev + Claude: Fast System 1 Triage, Deep System 2 Reasoning

Jev + Claude is a hybrid agent architecture that puts a fast triage model in front of a frontier reasoning model. Jev looks at each step in your agent loop first and answers routine decisions — approve, deny, score, route — in a single pass. Anything ambiguous, or anything that calls for written output instead of a structured answer, moves up to Claude. The result: most steps resolve fast and cheap, and the hard steps still reach a model built for depth.

Dual-System Architecture: System One Jev and Claude

Psychologist Daniel Kahneman split human reasoning into two modes: System 1, fast and intuitive, and System 2, slow and deliberate. Jev + Claude borrows that split for agents almost literally. System One Jev handles the fast pass — pattern-matching a request against a small set of possible answers — and Claude supplies System 2 depth once a step actually needs it.

That split shows up in how each model is built to answer. TypeSafe's docs describe the System One API as text-input only: you send a request to POST /v1/systemone on the jev-latest model and get back Choice, Score, or Noul answers (source: TypeSafe System One docs), and the LiteLLM pass-through examples show each answer carrying its own confidence value. Claude has no such ceiling — it can draft, summarize, and reason across long, open-ended context — which is exactly the work System One was never designed to do.

The speed and price gap between the two tiers is why the split is worth making. TypeSafe reports System One responses landing in 70–500ms end-to-end, against 3–329 seconds for frontier models on its demo workflows — 40–200x faster, with one demo workflow at 193.6x (source: TypeSafe blog). The same post prices System One input at $0.042 per million tokens with output free, and puts the resulting gap at up to 444.6x cheaper than typical large-model pricing. For the full picture of how System One's classification layer fits together, read the Deep Dive into Jev Architecture.

Agent Cost Optimizer

The panel below is an agent cost optimizer for this exact pairing, and every field in it is an editable assumption, not a fixed number. Agent steps per month, tokens per step, the share of steps you expect Jev to answer above your threshold, the threshold itself, Jev's per-token price, Claude's input and output price, output tokens per escalated step, and the latency you expect from each model — replace the defaults with your own workload and the totals recalculate immediately.

The confidence threshold slider sets the routing rule itself. On its own it does not change the monthly totals — the share of steps you expect Jev to answer above that threshold is a separate assumption, so move the two together. The routing check panel applies your threshold to one illustrative sample step with a fixed confidence value and shows which side of the line it lands on. Once a threshold looks right, download the resulting pipeline as a JSON config to review offline, or use Deploy Jev + Claude Pipeline to join the early access queue; running a pipeline against a live agent is not available yet.

How Routing Between Jev and Claude Works

Every Jev answer carries a confidence value alongside the Choice, Score, or Noul result itself. You pick a threshold — the same number you tuned in the simulator above — and everything else follows from a single comparison:

if (jevAnswer.confidence >= threshold) {
  return jevAnswer;
}
return callClaude(step, context);

Steps whose confidence clears the threshold execute on Jev's answer right away. Steps that fall short, or steps that were never a Choice, Score, or Noul question to begin with — a step that needs drafted text, a multi-part explanation, or a plan — escalate to Claude instead. That gives you two escalation triggers built into one rule: low confidence on a question Jev can answer, and any question Jev was never built to answer.

Anthropic Claude Integration

When a step escalates, Jev + Claude is designed to hand Claude the original state and surrounding context, not just the failed answer, so Claude reasons over the same information Jev saw rather than a stripped-down summary of it. Phase one of Jev Agent does not ship a server-side integration that calls Claude on your behalf — wiring the handoff into your own agent loop is on you today, and a managed Anthropic Claude integration is planned but not available yet.

Anthropic sets Claude's own pricing, rate limits, and model lineup independently of Jev, and those terms change on their own schedule. Check Anthropic's pricing page directly before you commit to a hybrid budget, rather than relying on any number restated secondhand.

Jev LLM Comparison: When Each Model Wins

The AI model Jev wins on repeatable, structured decisions: intake triage, refund eligibility, ticket routing, moderation calls — anything you can frame as a Choice, Score, or Noul question with a defined answer set. A general-purpose LLM wins once the task needs free-form writing, multi-step reasoning across long context, tool orchestration, or a judgment call that does not reduce to a fixed set of options.

Picking a lane per step, rather than per workflow, is what makes the hybrid pairing worth the extra wiring. Compare Jev vs Claude Benchmarks collects TypeSafe's published latency and cost figures for Jev against frontier LLMs in general — not Claude specifically — if you want harder numbers before you decide where to set your own threshold.

Once you trust a threshold, see where it fits in an agent loop: the Jev Agent Builder page steps through an illustrative loop that asks Jev a next-tool Choice and a task-done Noul on every turn, with sample code you can adapt and the fallback path to Claude left for you to wire.