failproof ai vs judgment labs

What Each Product Is

Judgment Labs is built around judges, written as a natural-language rubric or as Python code, that run on sampled production traces and return binary, categorical or numeric results with citations to the spans behind them. Those results feed monitoring, automations and a Production Issues view. The open-source judgeval SDK traces agents over OpenTelemetry, and an MCP server gives coding agents access to traces, judges, behaviors and issues.

Failproof AI does the same judging and keeps going once a failure is found. Evaluations run on every trace and session: code checks you author, test and version in the dashboard, LLM judges, and whatever eval suite you already use. Audits group failures into findings with a severity and a recommended fix. A finding becomes an issue you can assign, and generate policy turns it into a policy that the open-source failproofai CLI enforces at the agent's hook layer, before the next action runs.

Both have judges, so choose on the product around them. Does each customer need its own evaluations? Should an issue arrive with a fix someone owns? Should that fix be tested against real agent activity before it ships? Should a bad action be blocked, or only reported? And can you bring the evals you already run?

Features Compared

Judgment Labs does not publish a feature list, so its column is assembled from its public documentation, page by page, as of September 2026. Every page used is linked under Sources. Where its docs do not describe a capability, the cell says "not documented" instead of guessing.

1. Evaluations for Each Customer

2. The Judges Themselves

This is where the two are closest. Both score traces and sessions, point at the step behind a failure, and return pass or fail with reasoning. The judge is not where they differ; the product around it is, as the next three tables show.

3. Finding and Tracking Issues

4. Querying and Access

5. Fixing What the Judges Find

Where Judgment Labs Goes Further

• MCP coverage. About a hundred MCP tools reach traces, judges, behaviors, issues, automations and tests from a coding agent. JQL gives the Python and TypeScript SDKs a query language.
• Judge tooling. Judge calibration, offline tests pinned to a judge version, and an in-product agent that proposes rubric rewrites with citations. The homepage also lists AutoRubrics and Behavior Discovery.
• Evaluation research. Agent Judge, built for long-context trajectories, comes from a team that publishes evaluation research. The company raised $32M in 2026.

If you want a hosted judge with calibration tooling, from a team focused on evaluation, Judgment Labs fits well.

Where Failproof AI Goes Further

• Evaluations per customer. Each customer is its own organization, with its own evaluations, versions and rollback, isolated from the others. Judgment Labs scopes judges to a project and documents no per-customer rollout or rollback.
• Assignable issues. Findings carry a severity, the affected sessions and a recommendation, and become issues you assign, comment on and resolve. Judgment Labs documents issue status but not assignment.
• Fixes to the agent. From an issue, generate policy drafts a policy. A backtest replays it against calls your agents already made and counts the working calls it would have interrupted. You then deploy it in observe mode, and enforce it once it looks right. Judgment Labs' agent rewrites judge rubrics; to check a change to your agent, you re-run the agent yourself.
• Runtime blocking. Policies run at PreToolUse, before the tool executes, and return allow, instruct or deny. They work across twelve harnesses, including Claude Code, Codex, Cursor and Goose. Judgment Labs' automations evaluate each completed trace and send a notification; they do not block.
• Bring your own evals. Failproof AI runs the eval set you already have (DeepEval, Ragas, promptfoo or an in-house harness) in the cloud as it is, so you do not rewrite it to switch. Judgment Labs judges are written in its own rubric or Code Judge formats.

A custom policy for a failure your judges keep flagging takes a few lines of JavaScript:

// db-policies.js: block the failure the judges keep flagging, before it runs
import { customPolicies, allow, deny } from "failproofai";

customPolicies.add({
  name: "block-prod-drop",
  description: "Deny destructive SQL against the production database",
  match: { events: ["PreToolUse"] },
  fn: async ({ toolName, toolInput }) => {
    if (toolName !== "Bash") return allow();
    const command = String(toolInput?.command ?? "");
    if (/\b(DROP\s+TABLE|TRUNCATE)\b/i.test(command) && /\bprod(uction)?\b/i.test(command)) {
      return deny("Destructive SQL against production is blocked. Write a migration and open a PR instead.");
    }
    return allow();
  },
});

Enable it locally with failproofai policies --install --custom ./db-policies.js. Or publish it as a version from the policy editor, backtest it, and deploy it to machines in observe mode before you enforce it.

One Failure, Both Tools

Say a support agent, used by several of your customers, has an issue_refund tool, and it starts refunding orders outside policy. Here is how each product handles it.

1. Judgment Labs: judge, group, alert

   You write a judge ("refunds must match the refund policy") and run it on a sample of traces. Its behaviors filter by customer_id, Production Issues groups the evidence on a schedule, and an automation notifies the team or fires a webhook.

2. Failproof AI: your judge, per customer, find, fix

   Your existing refund-compliance judge plugs in as it is. The customer whose contract caps refunds gets a stricter threshold in its own organization. An audit groups the violations into a finding with the affected sessions and a recommended fix, and the issue goes to an engineer.

3. The next bad refund

   With Judgment Labs, the refund goes through and is flagged afterwards. With Failproof AI, generate policy drafts a PreToolUse rule on the refund tool. The backtest shows it would have denied the bad refunds without touching the valid ones, so it ships. The next bad refund is denied, or answered with instruct, which tells the agent to hand the case to a human.

Both take work. A judge is only as good as its rubric, and a policy has to be narrow enough to let valid refunds through. The difference is timing: Judgment Labs reports the bad refund after it happens, and Failproof AI can stop it before. Failproof AI also lets each customer hold the agent to its own standard.

Getting Started and Pricing

You buy the two differently. Failproof AI is self-serve: install the CLI, connect the free cloud tier, and upgrade from the pricing page when you need more volume or SSO. Judgment Labs sells through a demo and offers a forward-deployed engineering team for hands-on onboarding, which suits teams that want guided help from the start.

To try it on your own agents before talking to sales, start with Failproof AI's free tier. To begin with a guided engagement, Judgment Labs is set up for that.

Running Both

You can run both. If judgeval is already instrumented and your judges are tuned, keep them and add Failproof AI policies at the hook layer for the failures they keep flagging. If you are starting fresh, begin with the CLI: wire its hooks with failproofai policies --install, add the coding agent pack with failproofai policies add FailproofAI/policies, and see what fires over a week. That shows you which failures need a judge and which ones a policy already covers.