Agent Quality Infrastructure

Your agents break silently.
AgentSieve catches it.

Cohort-based baseline testing for AI agent pipelines. Run the same workflow repeatedly, measure drift, know exactly when quality degrades.

$ agentsieve run --cohort onboarding-v3 --baseline 2026-05-01

✔ email_delivery ........... PASS (consistency: 98.2%)
✔ task_creation ............ PASS (consistency: 96.7%)
⚠ landing_page_quality ..... WARN (drift: -4.1% from baseline)
✘ mission_doc_completeness . FAIL (missing: 2/5 sections)

3 passed, 1 warning, 1 failed | baseline: 2026-05-01 | run: 2026-05-07
57%
of orgs have agents in production
32%
cite quality as top deployment barrier
60%
will adopt eval platforms by 2028
How it works

Baseline. Run. Compare. Repeat.

AgentSieve doesn't just observe individual runs. It compares every execution against a known-good baseline, surfacing drift the moment it appears.

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Cohort Runs

Execute the same agent pipeline across identical inputs. Build statistical confidence in your agent's behavior over hundreds of runs.

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Baseline Diffing

Establish a "known-good" baseline, then automatically flag when new runs deviate beyond your threshold. No manual review needed.

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Drift Detection

Catch gradual quality degradation before users notice. Model updates, prompt changes, and infrastructure shifts all surface as measurable drift.

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Verdict Engine

Automated pass/fail/warn verdicts on every dimension you care about. Define your quality bar once, enforce it continuously.

Ship agents with confidence, not hope.

The gap between "works in testing" and "works in production" is where trust dies. AgentSieve closes that gap with continuous, automated baseline measurement. Every run measured. Every regression caught. Every deployment backed by data.