Agent Evaluation Infrastructure

Know when your agents break before your users do

Cohort-based quality baselines for AI agent platforms. Run standardized scenarios repeatedly, measure consistency, catch regressions before they ship.

$ qualitygate run --cohort onboarding-005 --repeat 5

PASS onboarding.welcome_email ............ 5/5 consistent
PASS onboarding.company_naming ........... 5/5 consistent
FAIL onboarding.landing_page_quality ..... 3/5 consistent
PASS onboarding.task_creation ............ 5/5 consistent
FAIL execution.tool_call_accuracy ........ 2/5 consistent

Baseline score: 78% (prev: 84%) | Regression detected in 2 areas
How it works

Three layers of quality measurement

01

Define cohorts

Create synthetic identities that run through your agent platform like real users. Same scenario, every time.

02

Run baselines

Execute cohorts on a schedule. Capture every output, tool call, decision, and timing. Build a statistical baseline.

03

Catch regressions

When consistency drops or outputs drift from baseline, you know immediately. Before users file tickets.

Why this matters

The numbers that keep agent teams up at night

60%
Single-run success
25%
Multi-run consistency
57%
Orgs with agents in prod
#1
Quality as deployment barrier

Your agents are non-deterministic.
Your quality standards shouldn't be.

QualityGate turns agent testing from "run it and hope" into measurable, repeatable, automated quality assurance.