Prefactor

About Prefactor

Prefactor is the definitive control plane for AI agents, engineered to solve the critical governance and security challenges that arise when moving autonomous agents from proof-of-concept demonstrations to regulated, production-scale deployments. It provides a centralized platform for managing agent identity, access control, and observability. The product is designed for product, engineering, security, and compliance teams within SaaS companies and regulated enterprises—such as those in financial services, healthcare, and mining—who are running multiple AI agent pilots and require enterprise-grade security, auditability, and operational control. Its core value proposition is transforming the complex, fragmented challenge of agent authentication and governance into a single, elegant layer of trust. By providing every AI agent with a first-class, auditable identity and enabling fine-grained, policy-driven access management, Prefactor allows organizations to scale their agent deployments with confidence, maintain full visibility over every agent action, and generate compliance-ready audit trails that translate technical events into clear business context.

Features of Prefactor

Identity-First Agent Governance

Prefactor assigns a unique, first-class identity to every AI agent within an organization's ecosystem. This foundational feature enables robust authentication for every agent action, moving beyond simplistic machine-to-machine tokens. It supports dynamic client registration and provides fine-grained controls over agent permissions through roles and attributes. This identity-centric approach ensures that every interaction is authenticated and every access request is properly scoped, applying proven human identity governance principles to the autonomous agent landscape.

Real-Time Visibility & Monitoring

The platform offers a comprehensive dashboard for complete operational visibility across an entire agent infrastructure. Users can monitor all agents in one place, tracking which agents are active, idle, or encountering issues in real-time. This feature provides insights into what resources agents are accessing and where failures are emerging, enabling proactive incident management before problems cascade. It delivers the shared visibility that engineering, security, and product teams require to manage agents effectively at scale.

Compliance-Ready Audit Trails

Prefactor generates detailed audit logs that are designed for regulatory scrutiny. Unlike logs that merely record cryptic API calls, Prefactor's system translates agent actions into clear business context and stakeholder-understandable language. This allows compliance and security teams to quickly answer critical questions about agent activity and generate audit-ready reports in minutes, not weeks. The feature is built to meet the stringent requirements of SOC 2 and other frameworks common in regulated industries.

Policy-as-Code & Automated Permissions

Access control for agents is managed through declarative, policy-as-code configurations. This allows security and platform teams to define, version-control, and automate permission policies directly within their CI/CD pipelines. The system supports delegated access models and human-delegated control, ensuring that permissions can be granted, reviewed, and revoked systematically. This automation integrates with OAuth/OIDC standards for interoperability and ensures consistent, scalable governance as the number of agents grows.

Use Cases of Prefactor

Scaling AI Agents in Regulated Enterprises

Organizations in heavily regulated sectors like banking, insurance, and healthcare use Prefactor to gain the necessary governance controls to deploy AI agents from pilot to production. It provides the audit trails, identity management, and real-time monitoring required to satisfy internal compliance and security teams, turning agent governance from a deployment blocker into a managed process. This enables these enterprises to leverage agentic automation while maintaining their rigorous security postures.

Centralizing Governance for Multi-Framework Agent Fleets

Companies utilizing multiple AI agent frameworks—such as LangChain, CrewAI, and AutoGen—alongside custom-built agents deploy Prefactor as a unified control plane. It offers a single source of truth for identity and access management across all agents, regardless of their underlying technology. This eliminates the need to rebuild security and observability layers for each framework, significantly reducing development time and operational complexity.

Managing Model Context Protocol (MCP) in Production

For teams adopting the Model Context Protocol (MCP) as a standard for agents to access tools and data, Prefactor provides essential production-grade controls. It addresses the "flying blind" problem by adding identity, audit, and visibility layers on top of MCP connections. This ensures that the powerful data access enabled by MCP is governed, monitored, and compliant with enterprise security policies from day one.

Cost Optimization and Operational Efficiency

Engineering and FinOps teams leverage Prefactor's monitoring and tracking capabilities to gain insights into agent compute costs across different providers. By identifying active versus idle agents and analyzing access patterns, teams can pinpoint expensive operational behaviors and optimize spending. The dashboard provides the data needed to right-size agent deployments and improve overall infrastructure efficiency.

Frequently Asked Questions

What is an AI Agent Control Plane?

An AI Agent Control Plane is a centralized management platform that provides the foundational infrastructure for operating autonomous AI agents at scale. It handles the non-differentiating but critical "plumbing" required for production, including agent identity and authentication, granular access control, real-time monitoring, audit logging, and policy enforcement. Prefactor's control plane abstracts this complexity, allowing development teams to focus on building agent logic rather than security and governance tooling.

How does Prefactor handle authentication for AI agents?

Prefactor implements an identity-first authentication model. Each agent is provisioned with a unique, auditable identity—similar to a human user in an IAM system. It supports industry-standard protocols like OAuth 2.0 and OpenID Connect (OIDC) for interoperability. Authentication is not based on static keys but on dynamic, scoped credentials. This allows for fine-grained permission controls, delegated access models, and the ability to audit every authenticated action an agent takes back to its specific identity.

Is Prefactor compatible with existing AI agent frameworks?

Yes, Prefactor is designed for broad compatibility. It works seamlessly with popular agent development frameworks including LangChain, CrewAI, and AutoGen, as well as with custom-built agentic systems. The platform provides integration libraries and APIs that allow these frameworks to connect to the Prefactor control plane for identity, policy checks, and logging, typically enabling deployment in hours rather than months.

How does Prefactor support compliance requirements like SOC 2?

Prefactor is built with compliance as a core design principle. It generates detailed, immutable audit trails that record every agent action in business-accessible language, which is essential for audits. The system enforces separation of duties and least-privilege access through its policy-as-code engine. Furthermore, Prefactor's own infrastructure is developed to be SOC 2 Type II ready, providing enterprises with a compliant foundation upon which to build their agent deployments, significantly reducing the compliance burden on internal teams.