Know when your agents break before your users do

Automated cohort-based quality grading for AI agent pipelines. Continuous baseline measurement. Catch regressions the moment they happen.

$ gradepulse run --cohort baseline-v3
───────────────────────────────────────
PASS onboarding_flow .......... 0.94 (baseline: 0.92)
PASS task_execution ........... 0.88 (baseline: 0.87)
WARN email_composition ........ 0.71 (baseline: 0.82) drift: -13%
FAIL code_generation .......... 0.43 (baseline: 0.79) drift: -46%
───────────────────────────────────────
2 passed | 1 warning | 1 failed | drift threshold: 15%
cohort

Cohort-Based Baselines

Run identical inputs through your agent pipeline at regular intervals. Establish quality baselines automatically, detect drift the moment it starts.

grade

Multi-Dimension Scoring

Grade outputs on completeness, correctness, tone, and task adherence. Not just "did it work" but "how well did it work across every axis."

alert

Regression Alerts

Set drift thresholds per dimension. Get notified before degraded output reaches production users. CI/CD for quality, not just deployment.

trace

End-to-End Runs

Evaluate complete agent executions, not isolated prompts. The full pipeline from input to final output, measured as your users experience it.

How it works

1

Define your cohort

Set up baseline inputs that represent your critical agent workflows. Real scenarios, real complexity.

2

Run continuously

GradePulse executes your cohort on schedule. Every run produces quality scores across multiple dimensions.

3

Catch drift early

When scores drop below your thresholds, you know immediately. Fix regressions before users notice them.

Your agents are only as good as your last measurement

Stop guessing whether your AI agents are still performing. Start measuring continuously.