Quality measurement for autonomous AI

Your agents ship daily.
Who's grading them?

RunHarness runs repeatable quality baselines against your AI agent outputs. Catch regressions before your users do. Measure consistency, completeness, and correctness across every execution.

runharness run --cohort onboarding-v4

                $ Running cohort: onboarding-v4 (12 scenarios)
                  Agent: claude-opus-4 | Provider: anthropic-direct
                  ─────────────────────────────────────────────
                  PASS email_generation ........... 94.2%
                  PASS task_creation .............. 97.8%
                  PASS landing_page_quality ....... 91.5%
                  FAIL mission_doc_completeness ... 68.3%
                  ─────────────────────────────────────────────
                  Summary: 11 pass / 1 regression | baseline drift: -2.1%
            

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Repeatable Cohorts

Define test scenarios once. Run them against any model, any prompt version, any agent configuration. Same input, measured output.

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Regression Detection

Catch quality drift before it hits production. Every run compares against your established baseline. Regressions surface instantly.

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Multi-Dimension Scoring

Consistency. Completeness. Correctness. Measure what matters for your agent's domain, not just pass/fail binary checks.

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CI/CD Integration

Run baselines on every deploy. Block regressions in the pipeline. Quality gates that actually understand AI agent output.

Software has unit tests.
AI agents deserve baselines.

The $112B testing market was built for deterministic code. AI agents are probabilistic. RunHarness bridges the gap with structured, repeatable quality measurement built for the agentic era.