Prefactor

Prefactor: Real-Time AI Agent Evaluation and Observability

In the rapidly evolving landscape of artificial intelligence, building an agent is only the first step. Ensuring that agents perform reliably, safely, and cost-effectively in production is a significant challenge for developers and enterprise teams. Prefactor is designed to solve these issues by providing a comprehensive platform for AI agent evaluation and agent observability.

Prefactor scores every agent run for quality, drift, and risk in production, then acts on those evaluations immediately. Unlike traditional tools that merely observe, Prefactor provides the "brake pedal" necessary to intervene when an agent deviates from its intended path. This ensures that a failing agent is caught live, rather than being analyzed hours later on a static dashboard.

What is Prefactor?

Prefactor is a real-time platform for AI agent evaluation and reliability. It addresses a critical gap in the market: the difference between simply observing an agent's actions and having the power to intervene. Most observability tools provide traces and alerts, but by the time a human reads the chart, the agent has already acted, potentially leaking data or incurring unnecessary costs.

Prefactor closes this loop by wiring evaluations and risk assessments directly into the agent's runtime. By scoring every run for quality, drift, and risk the moment it happens, Prefactor enables teams to enforce policies, pause risky actions for human approval, and maintain a high standard of reliability across their entire agent fleet.

"The gap: See everything. Stop nothing. That's the gap. Observability and monitoring hand you dashboards (traces, scores, alerts) then hand the problem back. By the time you've read the chart, the agent has already acted."

Key Features of Prefactor

1. The Reliability Trifecta: Observe, Evaluate, Act

Prefactor moves beyond standard agent monitoring by implementing a three-stage reliability loop:

• Observe: Capture every run as detailed traces and spans.
• Evaluate: Use sophisticated metrics, including LLM-as-a-judge, technical checks, and qualitative metrics, to score agents on the fly.
• Act: Automatically hold, approve, or block runs via the SDK or API. This ensures that risky agents are caught, not just charted.

2. Real-Time Scoring and Metrics

With Prefactor, every run is evaluated against custom-defined criteria. Users can monitor:

• LLM-as-a-judge scores: Automated qualitative assessments of agent output.
• Technical & Qualitative metrics: Ensuring compliance with JSON schemas and tone guidelines.
• Data-Risk & PII Detection: Identifying sensitive data leaks across 17 different categories.
• Performance metrics: Tracking P95 and P99 latency alongside cost attribution.

3. Agent Lifecycle Management

Prefactor allows teams to ship agents like traditional software. The platform supports a full development lifecycle:

• Development: Score agents against schemas and evaluations.
• Staging: Validate agents against production-like traffic.
• Production: Monitor live agents with the ability for instant rollback to previous versions if performance dips.

4. Custom Spans for Deep Context

Custom spans in Prefactor aren't just markers; they are enrichment tools. Developers can pull context from various data sources—such as GitHub PRs, Linear issues, Jira tickets, or internal databases—into the agent run. This ensures that every evaluation is grounded in real-world truth, providing a more accurate assessment of agent quality.

5. Runtime Enforcement and HITL

High-risk actions can be managed through Human-in-the-Loop (HITL) workflows. Prefactor can pause a run and require a person to approve, modify, or reject an action before it executes. This is particularly vital for agents performing sensitive tasks like financial analysis or data exports.

Use Cases for AI Agent Evaluation

Prefactor is designed for diverse industries and roles, solving problems like hidden failures, manual evaluation bottlenecks, and runaway costs.

Customer Support and Voice Agents

By implementing real-time agent evaluation, support teams can ensure their AI representatives maintain the correct tone, avoid PII leakage, and provide accurate answers. If an agent attempts an unauthorized refund, Prefactor can hold the action for manager approval.

Financial Services and Banking

In highly regulated industries, security and governance are paramount. Prefactor helps banking teams comply with frameworks like the EU AI Act, GDPR, and ISO 42001. It detects sensitive data in agent traffic and provides an auditable record of every decision made by the AI.

Internal Coding and Workflow Agents

Developers using agentic coding tools can use Prefactor to track the reliability of automated PR reviews or documentation updates. By integrating with tools like VS Code and GitHub Copilot, teams can ensure their internal agents are actually saving time rather than creating technical debt.

How to Use Prefactor

Getting started with Prefactor is streamlined to fit into existing developer workflows without requiring a platform migration. It integrates natively with popular frameworks like LangChain, Claude, Vercel AI, and LiveKit.

Step 1: Installation and Initialization

Use the Prefactor CLI to connect your workspace and discover existing agents across your runtimes. No complex migration is required.

$ prefactor init
# Scanning agent runtimes...
# Agents discovered successfully

Step 2: SDK Integration

Install the appropriate SDK for your project (TypeScript or Python). For a standard Node.js project, you can use the following command:

$ npm i @prefactor/sdk

Step 3: Instrument Your Agents

Wrap your agent logic using the Prefactor middleware. This allows the platform to start streaming spans and traces immediately.

const pf = prefactor({ agent: "claims-processor" });
// Every call now becomes a visible, evaluatable span.

Step 4: Define Evaluations

Set up your evaluations, such as LLM-as-a-judge or technical schema checks, within the Prefactor dashboard. These will run automatically on every step of the agent's process.

Step 5: Runtime Enforcement

Configure rules to block, throttle, or require approval for specific actions. Every decision is logged, providing a complete audit trail for security and governance.

FAQ

What is agent observability?

Agent observability involves capturing every agent run as structured trace data, including LLM calls, tool invocations, and decisions. This allows teams to see exactly what an agent did, why it did it, and what the associated cost was.

How does Prefactor differ from standard monitoring?

Standard monitoring stops at the dashboard. Prefactor provides a closed-loop system that wires evaluations directly into action, allowing for real-time intervention and enforcement through the SDK or API.

What are custom spans?

Custom spans allow you to pull ground-truth context from external data sources like GitHub or Jira into the agent run. This grounding makes evaluations much more accurate and allows for better tracking of quality over time.

How does human-in-the-loop (HITL) work?

Prefactor can detect high-risk actions and pause the agent's run. The action is then routed to a human for approval or modification. Once the person decides, the decision is enforced at runtime by the SDK.

Why do AI agents need scoped access?

Scoped access ensures that every action is tied to a specific agent, task, and user context. This enables the principle of least privilege, making the agent more secure and its actions fully auditable.

What frameworks does Prefactor support?

Prefactor offers native SDK integrations for LangChain, Claude, Vercel AI, OpenClaw, and LiveKit. It also supports any stack via its core TypeScript and Python SDKs or through OpenTelemetry spans.