Jev: stop paying your smartest AI to answer yes or no

The useful idea here is not replacing Claude or Codex with Jev. It is adding a specialized judgment layer inside the agent workflow you already have.

Jev MCP exposes 12 typed judgment tools. They can:

• Verify claims against supplied evidence
• Screen content before it enters context
• Classify, rank, and find the best candidate
• Choose between bounded alternatives
• Audit extracted values
• Review work before an agent declares it complete

Jev returns probability distributions and, for many tools, a confidence score. Your code then applies the policy: proceed automatically, request review, or escalate.

The project reports typical judgments in roughly 150 to 500 milliseconds for a fraction of a cent. Those are project claims, so test them on your own workload.

The boundary matters. Jev is built for questions with enumerated answers: yes or no checks, routing, ranking, screening, and picking among known options. Keep strategy, writing, planning, and open-ended problem solving with the frontier model.

A practical first experiment: look through recent Claude or Codex sessions and mark the repetitive decision points. Pick one read-only checkpoint, measure accuracy, tune its thresholds, and send low-confidence results to review or a person.

The catch: typed output guarantees the interface, not the truth. Calibrate against your own data before letting it trigger anything consequential.