Catch agent regressions before your users do

Automated quality baselines for AI agents. Run cohort evaluations, track consistency across versions, gate deployments on real pass/fail criteria.

$ gradeloop run --cohort baseline-v2.4
Running 47 fixtures against 3 agent versions...

PASS reasoning_accuracy 94.2% (+1.1%)
PASS tool_selection 98.7% (+0.3%)
FAIL output_consistency 71.4% (-8.6%)
WARN latency_p95 2.4s (+0.9s)

1 threshold breached | deploy blocked
47
Fixture scenarios
<3s
Full run time
CI/CD
Native gates

Built for teams shipping agents daily

Not another eval platform. A harness that runs on every commit.

cohort

Cohort-based baselines

Compare agent versions against stable fixture sets. Track quality drift over time, not just point-in-time scores.

gate

Threshold deployment gates

Set pass/fail criteria per metric. Block merges when output consistency, reasoning accuracy, or latency regresses beyond tolerance.

layers

Layered metric scoring

Separate metrics for reasoning, tool use, and output quality. Know exactly which layer broke instead of debugging a single composite score.

fixture

Deterministic fixtures

Mock-the-model test patterns for decision logic. Schema validation catches structural regressions without requiring exact output matches.

Quality is the bottleneck. Automate it away.

Every team shipping AI agents needs a quality baseline. The ones who catch regressions on commit, win. The ones who find out from users, don't.