Agent Quality Infrastructure

Your agents work.
Prove they work every time.

Cohort-based quality baselines for autonomous AI agents. Not single-call evals. Full-pipeline execution measurement, tracked over time, with drift detection built in.

spectrumqa run --cohort baseline-005
Running cohort baseline-005 (12 executions)
✓ onboarding.research passed 1.2s drift: 0.00
✓ onboarding.naming passed 0.8s drift: 0.01
✓ task.creation passed 0.4s drift: 0.00
● landing_page.quality warning 2.1s drift: 0.12
✓ email.delivery passed 0.3s drift: 0.00
✗ cycle.completion failed -- drift: 0.45
Baseline score: 83/100 (prev: 91/100) ▼ regression detected
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Drift Detection

Track quality drift across cohorts over time. Know the moment your agent pipeline degrades, not after users complain.

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Full-Pipeline Measurement

Not just LLM output quality. Measure the entire execution: tool usage, state transitions, output completeness, timing.

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Cohort Baselines

Run identical workloads across agent versions. Compare today's cohort against yesterday's. Regressions surface instantly.

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Repeatable Harness

Define execution scenarios once, run them on every deploy. Automated quality gates that block regressions from shipping.

The eval gap is not in prompts. It's in pipelines.

Every tool in the market measures whether your LLM said the right thing. Nobody measures whether your agent did the right thing, end to end, consistently. That's the measurement that matters when agents run autonomously.