judgment labs vs future agi

A Judge Company and a Platform Company

Judgment Labs is narrow on purpose. The Apache-2.0 judgeval SDK traces runs; Agent Judge grades a trace against a natural-language rubric; Code Judge runs your Python; Agent Behavior Monitoring runs judges on production traces and alerts on detection rates. Its research bets, Behavior Discovery and AutoRubrics, are about finding what to judge. Everything is aimed at one question: what did the agent get wrong?

Future AGI answers that question and several neighbouring ones. The platform, Apache-2.0 at its core since Q2 2026, has six parts: Simulate (multi-turn text and voice conversations), Evaluate (built-in evaluators, which its README puts at "50+ metrics", plus its own Turing Large, Turing Small and Turing Flash judge models), Monitor (traceAI, on OpenTelemetry, and Error Feed, which groups failures and suggests root causes), Protect (real-time guardrails), the Agent Command Center gateway for 100+ model providers, and Optimize (prompt optimization with methods such as ProTeGi, PromptWizard and GEPA).

So the comparison is not like-for-like. On judging, both are serious. On everything around judging, Future AGI has a product and Judgment Labs does not try to. Whether that is an advantage depends on whether you want those products from the same vendor.

Side by Side

Future AGI has more green in that table, and you should expect that from a platform compared with a specialist. The rows that decide real evaluations are fewer: how each one judges, whether you need a runtime control, and what it costs you to adopt a gateway.

Judges: A Rubric You Write or a Metric They Trained

Judgment Labs gives you two judge types and expects you to define what good looks like. An Agent Judge reads the trace against your rubric; the docs suggest it for decisions that need interpretation, and a Code Judge for exact conditions like a timeout or an empty result. Output is binary, categorical or numeric, with reasoning, run online, offline or on demand. AutoRubrics is described as "automatically constructing and refining evaluation rubrics from verifiable signals", which is the company working on the slowest part of the job for you.

Future AGI gives you a catalogue. Built-in evaluators cover the common failure classes out of the box, and its README claims "50+ metrics under one evaluate() call"; its own Turing judge models come in large, small and flash sizes, so you can trade accuracy for latency and cost without choosing a third-party model. Where Judgment Labs asks "what is your rubric?", Future AGI asks "which of these do you want switched on?"

• Judgment Labs is stronger on long, bespoke trajectories where no catalogue metric fits, and on discovering failure modes in unlabeled production data. It also has judgeval SDKs in Python, JavaScript, Go and Java.
• Future AGI is stronger on time to first score, breadth of ready metrics, and running judges on models it trained for the job. It can also test before launch, with simulated multi-turn and voice conversations.

Before Launch: Simulation or Offline Tests

The two products also disagree about when evaluation starts. Future AGI can test an agent that has never met a user. Its Simulate module runs multi-turn text and voice conversations against your agent, it generates synthetic datasets, and Optimize rewrites prompts with published methods such as ProTeGi, PromptWizard and GEPA. The free tier includes a million text-simulation tokens and 60 voice-simulation minutes a month, with pay-as-you-go above that.

Judgment Labs is at its strongest once production traces exist. Its pre-release loop is offline tests: judges run against datasets built from real production cases, so a fix is checked on the failures that actually happened before it ships. That is a better test of a fix and a worse test of a launch, because on launch day there are no production cases yet.

If you are pre-launch, or building voice, Future AGI covers ground Judgment Labs does not publish. If you have months of production traffic and a backlog of failures nobody has categorised, Judgment Labs' discovery and rubric work is aimed squarely at you.

One Forbidden Tool Call, Both Products

Future AGI's own example makes the difference plain: a summarization agent should be "structurally unable to call a payments tool, not merely unlikely to". Suppose yours is not, and one day it calls issue_refund because a document it was summarizing asked it to.

1. Judgment Labs: detect it after

   Whether the summarization agent called a payments tool is an exact condition, so it is a Code Judge: a few lines of Python that look for the tool call in the trace and return true or false. As a binary judge it can run as a monitored behavior. Monitoring scores the trace after it completes and alerts in Slack or through a webhook when the detection rate moves. The refund has already been issued; the alert tells you it happened and how often.

2. Future AGI: stop it at the gateway

   Route the agent's model traffic through the Agent Command Center, an OpenAI-compatible gateway. Its Tool Permissions scanner enforces which tools an agent may invoke at request time, and MCP Security inspects MCP traffic between the agent and its tool servers. Protect returns block, warn, mask or log. The call that would have issued the refund is refused at the gateway, before the tool runs.

3. The part to check

   Future AGI's guardrails run at the gateway, "the single network hop between your app and the model providers", or wherever your code calls the Protect SDK. Its docs describe tool-call enforcement for traffic routed through the gateway. Actions that never pass through it, such as a step your own code runs on its own, need another control.

For this failure, Future AGI is the better tool, and it is not close: one of the two can prevent the refund. The trade is that you put a gateway in the request path, and the next section is about what that means.

What Breadth Costs

A gateway is a dependency on every model call. It adds a hop, and it needs to be available whenever your agent is. Licensing deserves a look too: the repository carries a LICENSE-EE file next to the Apache-2.0 core, and code under its ee/ directories needs an Enterprise licence key or a Future AGI Cloud plan in production. Many teams want exactly that control point. Others, particularly ones with a gateway already, would rather keep evaluation out of the request path entirely, which is Judgment Labs' design: it never touches a live call, so a slow judge can never slow your agent.

Self-hosting shows the same trade. Future AGI self-hosts today with Docker Compose, which its docs call "the only supported path today"; the requirements page lists PostgreSQL, ClickHouse, Redis, Temporal, MinIO and a code executor that needs privileged containers, and puts Helm charts and Kubernetes manifests "on the roadmap". Telemetry is on by default, per the README, and off with FUTURE_AGI_TELEMETRY_DISABLED=1. That is a serious stack, and its issue tracker has the open items you would expect of one, among them a ClickHouse config crash-loop (#1540) and a CDN dependency that breaks offline installs (#2339). Judgment Labs has no equivalent cost because it has no self-hosted option yet: its docs list self-hosting as coming soon.

Future AGI names no customers; its case studies are anonymized ("Fortune 50 Retailer", "Code Agent Startup"), and it reports "10K+ self-hosted deployments" by its own count. Judgment Labs shows testimonials from Monaco, Human Behavior, Contrario, E3 Group and Vigil Labs and publishes no case studies. Neither gives a reference buyer much to go on, so ask both for a customer call.

Pricing and Deployment

Future AGI's pricing is detailed enough to model before you talk to anyone. One caution: its pricing page and its enterprise page do not agree on every SLA figure and which add-on gates what, so treat the pricing page as a starting point and get the tier you need confirmed in writing. Judgment Labs publishes no plans; the only call to action on its site is "Book demo".