jev evals in failproof ai

Jev Evals and Policies Are Live in Failproof

Jev evals and Jev policies are live for Failproof beta users. Email nikita@befailproof.ai if you need access or help getting started.

Where a Jev Result Fits in the Failproof Evidence Loop

Failproof’s value is not replacing a Jev API call. It is preserving the chain from session evidence to a measurable result and then to recurring operational work. A playground can answer one question; a reliability system must show which sessions failed, how the criterion changed, whether the pattern recurs and what happened after intervention.

Start with a Small Portfolio of Independent Evals

One broad “session quality” score hides which system component needs repair. Independent evals can point to routing, prompting, tool descriptions, retrieval, recovery logic or final communication. The trajectory guide defines the evidence boundary, while tool-call evaluation separates intent, authorization, execution and outcome.

Store an Evaluation Result as a Versioned Measurement

Do not store only a Boolean. A Noul of 0.51 and 0.99 may both pass a 0.5 rule while representing different evidence. The Jev eval guide covers primitive semantics; calibration explains how distributions become validated operating points.

Design the First Beta Evaluator Narrowly

Good first criteria include whether the requested task was completed, whether a cited claim is supported by supplied evidence, or whether a tool matched the user’s stated intent. Counts, schemas, permissions and arithmetic remain deterministic checks. The question-writing guide gives rewrite patterns.

1. Select one recurring failure with clear trace evidence and operational ownership.
2. Write one atomic criterion and define positive, negative and unscorable examples.
3. Project only the messages, tool arguments/results and outcome fields needed.
4. Label a representative set without seeing Jev’s result.
5. Measure errors, calibration, latency, cost and missing-evidence coverage.
6. Run in observation mode before using the result for alerts or policy work.

Turn Individual Results into Recurring Failure Evidence

A failed eval is evidence, not yet a diagnosis. Ten task-completion failures may share one missing tool capability, or they may be unrelated. Grouping should retain links to original sessions and the evaluator version so a team can distinguish a recurring mechanism from coincidental labels. The drift guide covers production slices and delayed outcomes.

Direct Jev Calls and Failproof Solve Different Layers

A direct harness remains useful for local experiments and model comparison. A reliability platform becomes valuable when results must stay connected to production traces, repeated failures and remediation. The evaluation-platform comparison shows how direct API runners, LangSmith, Vercel tooling and Failproof differ by layer rather than treating them as interchangeable model hosts.

An Eval Result Is Not Automatically a Policy

An evaluation normally measures completed behavior. A policy runs at a lifecycle point where it can alter an action. Moving a question earlier changes available evidence, latency constraints and the cost of errors. Backtest the pre-action version on recorded evidence, define outage and review behavior, then observe before enforcing.

The eval-versus-policy guide explains that promotion path. Jev policies are live for Failproof beta users. Email nikita@befailproof.ai if you need access or help getting started.