Ship AI Agents You Can Trust with AI Evals
The quality gate for AI agents, built into your Agent Development Lifecycle (Agent DLC). Score every response and gate every release, inside the CI/CD pipeline.
Score what matters to you, from faithfulness and safety to task completion, using LLM-as-judge, deterministic, and custom methods.
Eval suites run as a native pipeline step alongside build, test, and deploy, and gate the release.
Score before you ship, catch failures in production, and feed them back into your tests with every release.
Manual release sign-off becomes an automated pass-rate gate.
Two modes, one continuous loop
Score before you ship. Monitor after. Improve every release.
Offline and online evaluation share the same metrics and datasets, so there's no drift between what you test and what you measure.
Run agents against golden datasets before they ship. Score every response, compare prompt and model variants, and block the deploy if scores drop below threshold.
Score real user traffic with the same metrics, then promote real failures back into your datasets so quality compounds with every cycle.
The scoring logic you test with pre-deploy is the exact logic that runs against production traffic. No gap, one consistent quality bar.
A guided onboarding flow, built-in templates for common patterns, and AI-synthesized datasets from a single description.
Evaluate agents at scale, inside your Agent DLC
Four building blocks: Target, Dataset, Metrics, and Threshold compose into Evals. Evals bundle into Suites. Suites gate your pipeline.
Eval suites run as a Harness step in your Agent DLC, alongside Build, Test, and Deploy. Regressions fail the build and surface in the Tests tab.
Faithfulness, relevance, tone, and task completion, scored with LLM-as-judge, deterministic checks, and semantic similarity, in parallel.
Evaluate multi-step behavior at the step, tool-call, and final-outcome level: not just whether the agent answered, but whether it reasoned.
Prompt-injection detection and policy-compliance scoring built into every suite.
Diff any two runs with per-item deltas to catch regressions during PR or CI before they reach production.
Score sampled live traffic to detect drift, and promote any production trace into a dataset case in one click.
Version prompts, agents, MCP tools, and skills in a central registry. Roll back or promote any version without a code deploy.
Built on an Apache 2.0 evaluation SDK that runs in any CI system, notebook, or test suite, so you're never locked in.
Built for every role
AI quality for your whole team
Score every run across 50+ metrics and get reproducible quality signals instead of eyeballing outputs.
Compare prompt variants and model versions side by side, then drill from a suite score to the exact failing item in one click.
Synthesize entire datasets from a single description so you start scoring in minutes, not days.
.webp)
Make evals a release gate: every deployment is scored, and the build fails on a regression like any unit test.
See item-level pass/fail in the Tests tab right next to your existing checks.
What took one to two weeks of manual review now takes minutes.
.webp)
Manage prompts, agents, MCP tools, and skills in a central, versioned registry.
Roll back or promote any version without a code deploy.
Inherit RBAC, OPA policy governance, audit trails, and secrets management from the Harness platform.
.webp)
Ship AI features without trading speed for trust: regressions are blocked before they reach customers.
Track score trends across every run, with cost per run so LLM spend finally has an owner.
Close the loop from production back to your test suite so quality compounds every release.
.webp)
The trust gap
Tools built for traditional software are blind to AI.
Traditional software crashes with a stack trace. An agent returns a confident, plausible, completely wrong answer, and no alert ever fires.
Run the same prompt twice, get different results. You can't write assertEqual for a summarization agent.
DORA metrics don't measure agent faithfulness. Your test suites pass while quality silently degrades.
FAQs
Frequently Asked Questions
What is an AI eval?
An AI eval scores the output of an AI agent or LLM application against a defined quality bar. Unlike a traditional unit test that returns a binary pass or fail, an eval grades responses along a spectrum (faithfulness to source, relevance, tone, task completion, safety, and more) using a mix of LLM-as-judge scoring, deterministic code checks, and semantic similarity. Harness AI Evals turns those scores into a quality gate that can block a release when results drop below threshold.
What's the difference between offline and online evaluation?
Offline evaluation runs before you ship. You score your agent against golden datasets (curated test cases pairing inputs with expected outputs or context) inside your CI/CD pipeline, and gate the deploy if scores regress. Online evaluation runs in production. It scores sampled live traffic against the same metrics to detect drift and silent degradation, and lets you promote any real failure back into your datasets. Both modes share the same metrics and datasets, so there's no gap between what you test and what you measure.
What is a golden dataset?
A golden dataset is a curated set of test cases: the inputs your agent will receive, paired with the expected outputs or the context it should use to respond. It's your ground truth: the known-good answers the agent should produce or stay faithful to. Datasets are versioned, grouped by domain, and grow over time as you promote real production failures into them. You can also synthesize an entire dataset from a single natural-language description instead of hand-writing hundreds of cases.
What is LLM-as-judge scoring?
LLM-as-judge uses a language model to grade a response against a rubric: for example, judging whether an answer is grounded in the retrieved context (faithfulness) or whether it actually completed the task. It's how you score quality that can't be checked with an exact-match assertion. Harness AI Evals combines LLM-as-judge with deterministic checks and semantic similarity, and lets you define custom rubrics in natural language or write your own scoring logic in Python.
How does AI Evals gate a CI/CD pipeline?
AI Evals runs as a native Harness pipeline step alongside Build, Test, and Deploy, not a script you wire into CI yourself. You bundle metrics, datasets, and thresholds into an eval suite and attach it to your release pipeline. When a quality threshold regresses, the build fails exactly the way a unit-test failure does, and item-level results appear in the Tests tab. Datasets, metrics, and thresholds live as config alongside your pipeline, with the same RBAC, OPA governance, and audit trails you already use.
How does AI Evals fit into Harness Agent DLC?
AI Evals is the Test stage of Harness Agent DLC: the development lifecycle for building, testing, storing, deploying, operating, and governing AI agents on one platform. The same eval suites that gate your CI/CD pipeline feed the quality signal Agent DLC uses across build, deploy, and operate, so you're never reconciling separate tools to answer whether an agent is safe to ship.
What is trajectory evaluation?
Trajectory evaluation scores multi-step agent behavior, not just the final answer. It looks at step reasoning, tool usage (did the agent call the right tools with the right arguments?), and the final outcome. This matters for tool-calling agents and orchestration pipelines, where an agent can reach a plausible answer through a flawed path, or fail silently mid-trajectory.
Is AI Evals locked into Harness?
No. AI Evals is built on an open-source evaluation SDK released under Apache 2.0, so the scoring logic runs in any CI system, any notebook, or any test suite. The Harness platform adds the native pipeline gate, the AI asset registry, online evaluation, and enterprise governance on top.
Join the beta
Help shape enterprise-grade AI evaluation
We're building AI Evals with design partners running agents in production. Get early access and a direct line into what we build next.