Prefactor
Evaluate AI agents in real time: score quality, drift & risk, then act instantly. Catch failures live, not after.
Editor's Verdict
Key Takeaways
- Real-time agent evaluation
- Runtime enforcement
- Custom spans
- Agent lifecycle management
In-Depth Review: What is Prefactor?
Prefactor is a real-time agent evaluation platform that scores every production run for quality, drift, and risk the moment it happens. Unlike observability tools that only chart problems, Prefactor wires evaluations into action—pausing risky runs, enforcing policies, and enabling human-in-the-loop approval. With native SDKs for LangChain, Claude, Vercel AI, and more, it drops into your stack in minutes. Prefactor versions and promotes agents through dev, staging, and prod with eval-gated promotion, ensuring reliable, auditable AI operations.
Core Features
Real-time agent evaluation
Scores every agent run for quality, drift, and risk the moment it happens, enabling live intervention.
Runtime enforcement
Automatically holds, approves, or blocks agent actions at runtime based on evaluations and risk policies.
Custom spans
Enriches runs with context from any datasource (GitHub, Jira, databases) to ground evaluations in actual events.
Agent lifecycle management
Manages agents from dev to staging to production with versioning, eval-gated promotion, and instant rollback.
Integrations
Native SDKs for TypeScript, Python, LangChain, Claude, Vercel AI, OpenClaw, LiveKit, plus OpenTelemetry support.
PII detection
Detects and redacts sensitive data (17 categories) across all runs with real-time enforcement.
Pricing
Dev
- 25k spans/month included
- Every step recorded
- Scored and risk-checked live
- Hold, approve or block
- SDK install in minutes
Scaleup
- Everything in Dev
- 100% of activity, real time
- Dev, staging and prod
- Unlimited seats
- First 100k spans included, then $2.50 per 1k up to 4M/month
Enterprise
- Everything in Scaleup
- SSO and audit retention
- SLA and named engineer
- Pay by invoice or PO
- 4M+ spans/month
Pros and Cons
Pros
- Real-time evaluation and interventionAgents are not just monitored but also acted upon immediately, preventing costly mistakes.
- Easy integrationDrops into existing stacks quickly with SDKs for major frameworks and languages.
- Custom spans for grounded evaluationEnables context-rich evaluations by pulling data from any external source into the run.
- Versioning and promotionShip agents like software with version control, staging, and eval-gated rollouts.
- Built-in PII and risk detectionAutomatically detects sensitive data and high-risk actions with no extra configuration.
Cons
- Limited free tierFree plan caps at 25,000 spans per month, which may be restrictive for high-volume production agents.
- No self-hosting optionPrefactor is cloud-only; teams with strict on-premises requirements cannot self-host.
- Pricing can scale quicklyAfter the initial included spans, cost per span ($2.50 per 1k) can add up for large deployments.
- Requires SDK integrationAlthough minimal, agents need to be instrumented with the SDK to start collecting data.
- Limited native integrationsWhile core SDK works with any stack, native integrations are only for a handful of frameworks.
Use Cases & Recommended Professions
AI/ML Engineer→ View Toolkit
To evaluate and monitor agent performance in production, ensuring reliability and catching drift.
Head of AI→ View Toolkit
To gain oversight across multiple agents, enforce governance, and prove ROI of agent deployments.
Software Developer→ View Toolkit
To integrate monitoring and enforcement into agent workflows with minimal code changes.
AI Product Manager→ View Toolkit
To track quality metrics, costs, and user feedback for improving agent-based products.
Security Engineer→ View Toolkit
To enforce data protection policies, detect PII leaks, and ensure auditability of agent actions.
Founder/Startup→ View Toolkit
To build trustworthy AI agents from day one with built-in evaluation and safeguards.
Frequently Asked Questions
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ℹ️ Curation Disclosure: The overview and features of Prefactor were synthesized using AI and fact-checked by our curation team to ensure accuracy.
