Your agents
don't ship
until they pass.

Automated quality gates for AI agent deployments. RunGate evaluates every execution against baseline expectations and blocks regressions before they reach production.

rungate eval --suite baseline-v3
Running 47 evaluation fixtures... ✓ reasoning.coherence — 94.2% (threshold: 90%) ✓ tool_use.accuracy — 97.1% (threshold: 95%) ✓ output.completeness — 91.8% (threshold: 85%) ✗ safety.boundary_respect — 82.3% (threshold: 95%) ⚠ latency.p95 — 4.2s (threshold: 5s) GATE BLOCKED — 1 metric below threshold. Deploy halted.
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Regression gating

Set thresholds per metric dimension. When an agent regresses below baseline, the deploy stops. No exceptions.

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Multi-layer scoring

Evaluate reasoning, tool use, safety, and output quality independently. Know exactly where failures originate.

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Baseline comparison

Every run is compared against your golden baseline. Drift detection surfaces degradation before users notice.

From commit to confidence

Step 01

Define fixtures

Curate input-output pairs that represent expected agent behavior

Step 02

Run evaluation

Execute your agent against fixtures and score across every quality dimension

Step 03

Gate or ship

Pass thresholds and deploy. Fail them and get a detailed regression report

Ship agents you trust.

Every team deploying AI agents needs a quality gate between "it works on my machine" and production.