Prefactor

Product Introduction

1. Definition: Prefactor is a real-time agent evaluation and enforcement platform, categorized as an AI agent observability and reliability layer. It functions as a closed-loop system that sits between an AI agent's execution and its actions in a production environment.
2. Core Value Proposition: Prefactor exists to close the critical gap between passive AI agent observability and active intervention. Its primary value is to evaluate AI agents in real time for quality, drift, and data risk, and then enforce policies at runtime to prevent failures, block sensitive data leaks, and enable human-in-the-loop workflows before an agent acts, not after.

Main Features

1. Real-Time Evaluation & Scoring: Prefactor runs user-defined evaluations on every agent run as it happens in production. This includes LLM-as-judge evaluations, technical checks (e.g., JSON schema validation), and qualitative metrics (e.g., tone, helpfulness). Scores are calculated per span and per run, providing granular visibility into performance and drift.
2. Runtime Enforcement & Human-in-the-Loop (HITL): This is the core innovation. Based on evaluation scores and risk classification (e.g., sensitive data detection across 17 categories), Prefactor can programmatically pause, block, or throttle agent actions via its SDK or API. High-risk actions, like issuing a refund or exporting data, can be held for human approval before execution, creating a enforceable safety mechanism.
3. Custom Spans for Context Enrichment: Beyond automatic instrumentation, Prefactor allows developers to create custom spans to pull context from any datasource (GitHub, Jira, internal databases, REST APIs) directly into an agent's trace. This context grounds subsequent evaluations in real, actionable data, improving eval accuracy and relevance.
4. Agent Lifecycle & Version Management: Prefactor treats agents like versioned software. It tracks immutable agent versions (by semver, commit, or tag) and manages promotion through dev, staging, and production environments. Promotion is gated by evaluation pass rates, enabling safe, data-driven deployments and instant rollbacks.
5. Full-Stack Observability & Tracing: The platform captures complete traces of every agent run, detailing each LLM call, tool invocation, and decision as structured spans. This provides real-time visibility into cost, latency (P95/P99), and data flow, offering foundational observability beyond simple logging.

Problems Solved

1. Pain Point: The "Observe ≠ Intervene" gap. Traditional AI observability and monitoring tools provide dashboards and alerts but lack the ability to stop a failing or risky agent action in real-time. By the time an engineer sees a problem, the agent has already acted (e.g., leaked PII).
2. Target Audience: Engineering and platform teams building and deploying production AI agents. This includes AI Engineers, ML Engineers, and DevOps/SRE teams at companies scaling agentic workflows. Personas include the Head of AI Platform needing governance, the Staff Engineer responsible for reliability, and the Product Manager requiring quality guarantees.
3. Use Cases: Essential for any customer-facing or high-stakes AI agent. Key scenarios include: Customer support agents handling refunds or sensitive data; Financial analysis agents generating reports or executing data exports; Claims processing agents making automated decisions; Marketing agents generating external content; ensuring compliance and data governance across all agentic workflows.

Unique Advantages

1. Differentiation: Unlike pure observability tools (e.g., LangSmith, Arize, Weights & Biases for LLMs) that stop at visualization and scoring, Prefactor adds the critical enforcement layer. It moves from "you have a problem" to "the problem has been contained." It is not just a monitoring dashboard but a runtime control plane.
2. Key Innovation: The closed-loop reliability trifecta: Observe → Evaluate → Act. Prefactor's SDK and API are designed to be an active participant in the agent's execution flow, enabling it to intercept and modify behavior based on live evaluations. This programmatic enforcement, combined with custom spans for ground-truth context, is a unique technical approach to agent reliability.

Frequently Asked Questions (FAQ)

1. How does Prefactor's real-time evaluation differ from offline LLM evals? Prefactor runs evaluations concurrently with production agent execution, scoring each step (span) as it happens. This allows for immediate intervention, whereas offline/batch evals only provide historical analysis after potential damage has occurred.
2. What frameworks and stacks does Prefactor integrate with? Prefactor offers native SDKs for TypeScript and Python, with out-of-the-box integrations for LangChain, Claude API, Vercel AI SDK, OpenClaw, and LiveKit Agents. It also supports generic instrumentation via OpenTelemetry and provides a core SDK for any other custom agent stack.
3. How does the human-in-the-loop enforcement work technically? When a pre-defined risk threshold is crossed, the Prefactor SDK pauses the agent's execution and raises an event. This event can be routed via API to a dashboard or external system where a human reviewer can approve, modify, or reject the pending action. The SDK then enforces this decision, resuming or terminating the run.
4. Is Prefactor suitable for small teams or early-stage AI projects? Yes, Prefactor is designed to scale. It offers a free tier for the first 25,000 spans per month, allowing small teams to instrument agents in minutes and establish evaluation and safety practices early, preventing technical debt as their agent fleet grows.
5. How does Prefactor handle data security and privacy? Prefactor is built for enterprise security with a least-privilege model. It provides sensitive-data detection, maintains a complete audit trail of all actions and decisions, supports SSO, and is undergoing SOC 2 Type II certification. Data is processed with scoped access aligned to agent identity.