Your agents regress. Know before users do.

Continuous quality baselines for AI agents. Run cohort evaluations automatically. Catch regressions the moment they happen, not the moment someone complains.

Open Dashboard → API Docs
$ scoreguard run --cohort onboarding-v3
 
Running 24 fixtures across 3 agent configs...
 
PASS cohort/onboarding task_creation (98.2%)
PASS cohort/onboarding email_quality (94.7%)
FAIL cohort/onboarding landing_page (71.3% < 85%)
PASS cohort/onboarding tool_selection (99.1%)
 
Baseline drift: -4.2% on landing_page since May 01
Regression alert sent to #quality-alerts
How it works

Three layers. Zero blind spots.

1

Cohort Harness

Define fixture sets per agent workflow. Run identical evaluations across multiple configurations simultaneously. Compare outputs side-by-side.

2

Baseline Tracking

Establish quality baselines automatically. Track drift over time. Know exactly when and where quality degraded, down to the specific evaluation layer.

3

Regression Gates

Set thresholds per metric. Block deploys that drop below baseline. Alert on drift before it compounds into user-facing failures.

The difference

One-shot evals miss the trend.

Traditional Evals

Run manually before deploys
Single snapshot in time
No baseline comparison
Regressions found by users

ScoreGuard

Runs continuously, autonomously
Cohort-based trend tracking
Automatic baseline drift detection
Regressions caught at source

Quality is a trend line, not a checkbox.

ScoreGuard watches the line so you can ship with confidence.