Automated cohort-based quality regression detection for AI agents. Not another eval dashboard. A harness that runs continuously, compares across populations, and flags degradation before users notice.
Not individual evals. Population-level quality measurement. Run the same scenarios across agent variants and time windows. Detect drift that single-run tests never see.
Runs on a schedule without human intervention. Executes baseline scenarios, captures structured results, compares against historical cohorts. You get alerted on regression, not noise.
Consistency, completeness, correctness, latency. Every dimension tracked independently so you know exactly what degraded and when the regression started.
Works with any agent stack. LangChain, CrewAI, custom builds, or pure API calls. If it takes an input and produces an output, ScoreFrame can evaluate it.
of orgs have agents in production
cite quality as top deployment barrier
tools do automated cohort regression
Every eval tool tells you how one run went. ScoreFrame tells you whether your agents are getting better or worse. That's the difference between observability and quality assurance.