Prefactor

Prefactor is a real-time agent evaluation platform that closes the gap between observing an AI agent's behavior and intervening when something goes wrong. It scores every agent run for quality, drift, and risk the moment it happens, then wires those evaluations directly into action, pausing a risky run for human approval, blocking a policy violation at runtime, or throttling a misbehaving agent. Unlike traditional observability tools that stop at dashboards and alerts, Prefactor enforces guardrails automatically or with human-in-the-loop approval, all through TypeScript and Python SDKs that integrate with frameworks like LangChain, leading LLM providers, Vercel AI, OpenClaw, and LiveKit. The platform captures every model call, tool use, and decision as spans and traces, attaching cost, latency, and data-risk metadata. Custom spans can pull context from external datasources like GitHub, Jira, or internal APIs to ground evaluations in real-world context. Prefactor supports LLM-as-judge, technical checks, and qualitative metrics as native evals, and can trigger actions like hold, approve, or block via SDK or API. It is designed for teams running production agents that need reliability beyond dashboards, catching failures live, not charting them after the fact.