Prefactor

What is Prefactor?

Prefactor is a real-time evaluation layer for AI agents that bridges the gap between passing evals in testing and failing in production. It scores every agent run the moment it happens—measuring quality, drift, and risk—then wires those evaluations directly into action. Instead of just charting failures after the fact, Prefactor catches them live and enforces policies at runtime, so a risky agent is paused, approved, or blocked before it acts.

Who it's for

• Engineering teams shipping agents to customers who need to catch quality regressions and drift as they happen, not after deployment.
• Platform and reliability engineers who want to move beyond passive dashboards and enforce guardrails automatically or with human-in-the-loop approval.
• Agent builders using TypeScript or Python who need a drop-in evaluation layer that works natively with LangChain, Claude, Vercel AI, OpenClaw, and LiveKit.

Key features

Real-time scoring and enforcement

Every agent run is evaluated the moment it completes—quality, drift, and risk scores are computed live. If a run triggers a high-risk action, like leaking PII or issuing a refund, Prefactor can hold it for human approval or block it entirely, enforced at runtime through the SDK or API.

Custom evals and LLM-as-judge

You define the evals that matter: LLM-as-judge, technical checks, and qualitative metrics. Human review feeds straight back into scoring, so your evaluation criteria improve over time without manual retuning.

Full trace visibility with cost and risk data

Every model call, tool use, and decision is captured as a trace with cost and data-risk attached. You see exactly what happened in each run, streaming in live, so debugging and auditing are straightforward.

One-command install and SDK integrations

The CLI connects your workspace and discovers agents across runtimes in minutes. The TypeScript and Python SDKs integrate natively with major frameworks, turning every call into a span without a rip-and-replace migration.

What stands out

"A risky agent is caught, not just charted."

Most evaluation tools stop at dashboards and alerts, handing the problem back to humans. Prefactor closes the loop by wiring scores directly into action—pausing a run for approval, enforcing a policy, or blocking a harmful action before it executes. That shift from passive observation to active intervention is what makes it reliable for production agents at scale.

Worth checking out if…

You're building AI agents that need to operate reliably in production, especially if you've seen evals pass in testing only to fail with real users. Prefactor is a strong fit if you want to catch quality regressions and drift live, enforce guardrails automatically, and keep a human in the loop for high-risk actions—all without rebuilding your stack.