Span-based observability
Records every LLM call, tool invocation, message turn, and custom business step as a span so agent activity can be reviewed in context.
Prefactor
Prefactor is an AI agent observability, evaluation, and enforcement platform for teams running agents in production. It records agent steps as spans, evaluates them in real time, and can hold, approve, or block runs based on policy.
What it is
Prefactor is an AI agent observability, evaluation, and enforcement platform. It records each agent step as a span, evaluates runs in real time, and can act on policy by holding, approving, or blocking a run while it is still executing.
The product is built for teams running agents in production and across development stages. It supports TypeScript and Python SDKs, native integrations for several agent frameworks, and a workflow that moves agents from dev to staging to prod with evaluation gates and rollback support.
Pricing is usage-based on spans, with a free Dev tier and paid plans for production usage. The company also positions the platform for regulated environments with immutable audit logs, data residency options, encryption, and runtime policy enforcement.
Core capabilities
Real-time evaluation
Runs deterministic scoring, risk checks, and PII checks on live activity, with results attached to each step as it happens.
Runtime enforcement
Can hold, approve, block, throttle, or require human approval when a run crosses a policy threshold, through the SDK or API.
SDKs and native framework support
Supports TypeScript and Python SDKs, plus native integrations for LangChain, Claude, Vercel AI, OpenClaw, and LiveKit.
Lifecycle and version control
Tracks agents through dev, staging, and prod, with versioning, eval-gated promotion, and instant rollback.
Audit-ready security controls
Provides immutable audit logging, encryption in transit and at rest, and environment-scoped keys for security and compliance workflows.
Where it fits
Production monitoring with live intervention
Use Prefactor to watch production agent runs as they happen, score each step, and intervene before a risky action is executed.
Instrumenting agents during setup
Use the SDKs to instrument a new or existing agent without a full migration, then inspect traces, costs, and risk on every run.
Versioning and staged promotion
Use eval gates and rollback to compare versions, promote only validated agents, and keep dev, staging, and prod separated.
Adding business context to evaluations
Use custom spans to attach context from systems like GitHub, Linear, Jira, databases, or internal APIs so evaluations are grounded in source data.
Compliance-oriented deployments
Use the security and audit features when your team needs tamper-evident logs, data residency options, and runtime policy controls for regulated deployments.
Pros and Cons
Pros
- Observability, evaluation, and enforcement are combined in one platform instead of split across separate tools.
- Spans are the billing and measurement unit, and scoring or interventions do not create additional spans.
- Unlimited seats are included on every plan, which simplifies team access and review workflows.
- The platform supports real-time intervention, not just post-run dashboards and alerts.
- Security and compliance features are visible in the product itself, including audit logs, encryption, and data residency options.
Cons
- The source does not provide a complete public list of every integration or platform supported beyond the examples shown.
- Some compliance details are described at a high level, so buyers may still need a security review for environment-specific requirements.
FAQ
How does Prefactor fit into an existing agent stack?
Prefactor records each agent step as a span, then runs scoring, risk checks, and runtime enforcement on that activity. The source pages describe TypeScript and Python SDKs, plus native support for LangChain, Claude, Vercel AI, OpenClaw, and LiveKit.
Can multiple people use Prefactor on the same account?
The pricing page says seats are unlimited on every plan, and the security page describes role-based access control for policy management, audit log access, and infrastructure configuration. That suggests teams can add multiple reviewers and engineers without seat-based limits, while still controlling access by role.
Is there a free tier or trial?
Prefactor offers monthly pricing for teams that do not know their volume yet, and annual pricing for committed usage. The pricing page also states that Dev starts free with 25,000 spans a month, while other plans are paid and usage-based.
How long does setup take?
The source pages do not describe a self-serve setup wizard in detail, but they do say the CLI installs in minutes and discovers agents across runtimes. The product is designed to attach via SDKs and the CLI rather than through a migration-heavy process.
How does pricing work for evaluations and enforcement?
The pricing page says checks and interventions do not create extra spans, and scoring, risk analysis, PII checks, and interventions are included in the span price. Annual plans are committed volume plans, while monthly billing is for teams that want flexibility.
Quick Facts
- Category
- AI agent observability and evaluation
- Platform
- Web platform with TypeScript and Python SDKs
- Primary users
- Teams building and operating AI agents
- Deployment stages
- Dev, staging, and prod
- Pricing model
- Usage-based on spans
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