A typed answer tells code what Jev selected; confidence helps decide whether the application should act automatically. TypeSafe describes this as confidence-gated routing. The value is useful only when paired with a policy that names the action, its cost of error, and a fallback.
Separate the judgment from the action
Imagine a ticket router choosing billing, technical, or sales. The model supplies a Choice result. Application code can send high-confidence routine tickets to a queue and place uncertain tickets in a human review queue. A low-confidence result should not silently disappear or be forced into an arbitrary destination. Always record enough context for a reviewer to understand the original request.
Choose thresholds from evidence
There is no universal “safe” threshold. Collect representative examples, label the correct action, and look at errors at several candidate thresholds. Measure both mistakes among automatically handled cases and the share sent for review. Choose a threshold that matches the cost of a wrong action and your team’s review capacity. Recheck it when the model, question, input population, or business rule changes.
Handle failure as a separate path
Timeouts, malformed responses, missing state, and authorization failures are not low-confidence answers. Handle them explicitly in code. For a consequential action, a failed model call should go to a known safe path, such as review or a deterministic rule. Do not let model output bypass access checks, payment safeguards, or audit logging.
A small rollout
First, show suggestions to reviewers without changing production behavior. Next, compare suggestions with human decisions and inspect disagreements. Then automate only a narrow, well-performing group. Keep a way to reverse an incorrect action and review a sample of automated decisions after launch.
The agent-memory gate offers one author-reported evaluation to inspect: 100 synthetic cases at a stated cutoff, with save, skip, and defer outcomes. Its cutoff is evidence about that test set only. The Jev-kit guard shows a code-first path that sends ambiguous tool calls to the model rather than asking it to decide every call.
The official confidence guide explains confidence and probabilities in more detail. For the request shape, start with Choice, Score, and Noul; for what Jev should not be asked to do, read What is Jev?.