agent judge vs failproof ai evaluations

What Each One Is

Judgment Labs' judges docs define a judge as "a reusable evaluation asset. It defines what to evaluate, how to evaluate it, and the shape of its output." Agent Judge "follows a natural-language rubric and reasons over the supplied trace or example"; Code Judge "runs Python logic that you implement". Judges run continuously on live traces, with a sampling percentage and span triggers, on demand, or in offline tests, at trace or session scope. They return binary, categorical or numeric results, and each result carries citations with the id of the span behind the claim.

Failproof AI runs evaluations in the cloud on every trace and session and writes the results beside the trace. Code checks are deterministic Python, one expression with no imports and no network, which the assistant drafts from a plain-English description, which you test against real sessions before enabling, and which are kept as immutable versions you roll back by enabling an earlier one. LLM judges run in the cloud too, and so does the eval set you already have (DeepEval, Ragas, promptfoo or your own). A result is a score, a metric or an assertion, with reasoning, a summary and labels.

So the judge is not where these two differ. Both write rubrics and code checks, both score traces and sessions, and both explain each verdict. The difference is what the product does around the judge: which sessions it sees, whose evaluations they are, and what a failing score turns into. On Judgment Labs, a failing verdict becomes a monitored behavior, an issue record and an alert. On Failproof AI, it becomes a finding with a recommended fix, an issue someone owns, and a policy that stops the same failure before the agent repeats it.

Features Compared

Judgment Labs publishes no feature list, so its column is assembled from its public docs as of September 2026; every page used is linked under Sources.

The first four rows are the judge, and there the two are at par. Every row after them is the product around the judge, and that is where the choice gets made.

The Whole Loop, Not Just the Judge

Judgment Labs turns judge results into monitored behaviors, automations and alerts, and its Production Issues view groups related evidence into issue records, with a diagnosis, priority, cited traces and a status, on a schedule or on demand. Its Judgment Agent proposes rewrites of judge rubrics, with citations. The loop ends at a better judge and an alert.

Failproof AI's loop keeps going. Audits review failing sessions for a stated goal, grounded in agent contracts, and group them into findings with a severity, affected sessions, evidence queries and a recommended fix. A finding becomes an issue you assign and resolve. From the issue, generate policy drafts a policy; a backtest replays the draft against calls your agents already made and counts the working calls it would interrupt; and once it ships, a deny at the agent's PreToolUse hook stops the next bad refund before it runs. Alerts go to email, Slack, a webhook or the dashboard along the way. Turn judge findings into runtime policies walks the path end to end.

Where Judgment Labs Stands Out

• Judge tooling. Judge calibration against human labels, offline tests pinned to a judge version, and an in-product agent that proposes rubric rewrites with citations.
• Long-context research. Judgment Labs has written about Agent Judge's design for long-context trajectories.
• MCP coverage. About a hundred MCP tools reach traces, judges, behaviors and issues from a coding agent.

Getting to a First Score

On Judgment Labs, writing a rubric and attaching it to monitoring is configuration once you have access, and access goes through a demo, since no self-serve plan is published.

Failproof AI starts self-serve, with 100 evaluations a month on the free cloud tier. The assistant drafts a code check from a plain-English description, you test it against real sessions and enable it in one sitting, and LLM judges run in the cloud beside it. If you already have evals, such as a DeepEval or Ragas suite in CI or a promptfoo config, they come in as they are rather than being rewritten in a new format, so moving in does not start from zero. GET /v1/sessions/{id}/export returns "the exact bytes an evaluator receives", so you can run the same suite offline over real production sessions before you switch it on.

Versions and Tenants

Judgment Labs keeps judge versions immutable once saved and promotes a version to production, with judge calibration and offline tests pinned to a version; its docs do not describe a rollback. Failproof AI's code checks are immutable versions too, deployed and rolled back in the dashboard by disabling one version and enabling another, so a bad change is one switch away from undone.

Tenancy is part of the product, not the judge. In Failproof AI, evaluations belong to an organization, and organizations isolate sessions, evaluations, audits, issues, alerts, queries, dashboards, users and keys, so each customer you serve can be its own organization with its own evaluations. Judgment Labs scopes judges to a project and records customer_id as a built-in, filterable trace field; its docs describe no per-customer rubric or rollout, so separate projects or attribute-based span triggers are the workaround.

Sampled or Every Session

Judgment Labs' monitoring docs say its measures describe "traces the judge evaluated, not necessarily every trace sent to the project", and continuous judges run with a sampling percentage and span triggers. Sampling keeps judge cost proportional; it is a weak way to catch a behavior that happens four times a week.

Failproof AI evaluates every trace and session that an evaluation's condition admits. The condition is one Python expression, session.agent_id == "support-agent" and session.environment == "production", so "only this agent" or "only production" is one line, and anything that does not qualify is skipped rather than scored.

The Judge Model and Its Cost

Judgment Labs' judges docs do not say which model Agent Judge runs on, whether you can bring your own model or key, or how judge runs are billed, and its pricing is not published. If your security review asks where transcripts go for judging, or finance asks what doubling traffic does to the bill, put both on the demo agenda.

Failproof AI's evaluation bill is published: 100 evaluations a month on Free, 2,000 on Team ($99/month) and 20,000 on Scale ($599/month), then $0.05 per run on Team and $0.04 on Scale, with each session-and-evaluation pair counted once.

A worked example: 10,000 finished sessions a month with two evaluations each is 20,000 billable evaluations. On Team that is $99 + (18,000 × $0.05) = $999; on Scale it fits inside the 20,000 for $599. An evaluation's condition is the lever on volume: score only the sessions the question applies to, such as production sessions of the support agent.

The Refund Example in Failproof AI

Here is the refund example in Failproof AI. First, a code check: the share of tool results that came back ok. It is one expression, with no imports and no network, and the assistant can draft it from that sentence.

EvalResult(
    score=Score(
        len([e for e in session.events_of_type("tool_result") if e.payload.get("status") == "ok"])
        / max(1, session.count("tool_result"))
    ),
    metrics={"tool_calls": Metric(session.count("tool_use"), unit="calls")},
    reasoning="Share of tool results that came back ok.",
)

Then the refund rule itself, which is a judgment rather than a count, so it is an LLM judge, and it runs in the cloud like the code check. The rubric is the same kind you would write for Agent Judge: one question, a clear pass condition, and a reply format that can be parsed.

You are grading one customer-support agent session.
PASS only if every refund the agent issued was no larger than the order total,
and every refund over $200 was handed to a human instead of issued.
Reply with JSON only: {"pass": true or false, "reason": "one sentence citing the event"}

1. Test it on real sessions

   Run the judge against real sessions before enabling it (a few refunds you know were fine, a few you know were not) and read its reasoning, not just its verdict.

2. Deploy it as a version

   Enable it as an immutable version. If the next rubric change starts failing good refunds, disable that version and enable the earlier one.

3. Read the scores

   Results appear under Observe → evaluations, beside each session, and re-evaluate reruns one. From the terminal:

   fp evals --since 24h --aggregate
   fp evals --since 7d --score refund_policy:0..0.5

4. Close the loop

   When the judge keeps failing sessions, an audit groups them into one finding with a recommended fix, generate policy drafts a PreToolUse rule on the refund tool, and the backtest shows it would have denied the bad refunds and none of the good ones before you enforce it.

If a refund-policy check already exists in your suite, such as a DeepEval metric, a Ragas score or your own harness, bring it in as it is instead of writing the rubric again. Its results land in the same place and feed the same loop.