Prefactor

About Prefactor

Prefactor is the essential control plane for AI agents, designed to solve the critical governance gap that prevents autonomous agents from moving from proof-of-concept to secure, compliant production. It provides a single, unified layer of trust that gives every AI agent a first-class, auditable identity. For product, engineering, security, and compliance teams in regulated enterprises—such as banking, healthcare, and mining—managing multiple agent pilots, Prefactor is not a luxury; it's a necessity. It transforms the complex, fragmented challenge of agent authentication, authorization, and auditing into an elegant, scalable solution. By offering dynamic client registration, delegated access, and fine-grained role-based controls, Prefactor ensures you have complete visibility and policy-as-code management over every agent action. Built with SOC 2-ready security and interoperable OAuth/OIDC support, it is the foundational infrastructure you must have to deploy AI agents with confidence, maintain regulatory compliance, and prevent costly security incidents before they happen.

Features of Prefactor

Identity-First Agent Control

Every AI agent is assigned a unique, first-class identity within Prefactor, ensuring every single action is authenticated and every permission is explicitly scoped. This moves beyond insecure M2M tokens, applying proven human governance principles—like dynamic registration and delegated access—directly to your autonomous agents. It is the mandatory foundation for any secure agent deployment, eliminating anonymous and untracked operations.

Real-Time Agent Monitoring & Dashboard

Gain immediate, operational visibility across your entire agent infrastructure from a centralized dashboard. This feature is critical for answering the urgent question, "What is this agent doing right now?" You can track active agents, monitor accessed resources, and identify emerging failures in real-time, allowing you to prevent minor issues from cascading into major production incidents.

Compliance-Ready Audit Trails

Prefactor's audit logs are engineered for regulatory scrutiny, translating raw technical API events into clear, business-context narratives that stakeholders and auditors understand. This eliminates weeks of manual log analysis, enabling you to generate audit-ready reports in minutes. It is an indispensable tool for proving compliance and providing definitive answers about agent behavior.

Policy-as-Code & Emergency Controls

Manage and automate agent permissions directly within your CI/CD pipeline using policy-as-code, ensuring consistent, version-controlled governance. Most importantly, this includes emergency kill switches, providing human-delegated control to instantly revoke access or halt agent activity. This fail-safe mechanism is non-negotiable for maintaining ultimate authority in production environments.

Use Cases of Prefactor

Scaling AI Agents in Regulated Industries

For banks, healthcare providers, and mining companies, "moving fast and breaking things" is not an option. Prefactor is essential for these organizations to gain the necessary governance, audit trails, and security controls to get internal approval for AI agent deployments. It turns complex compliance requirements into a manageable framework, enabling safe scaling from pilot to production.

Unifying Visibility Across Multiple Agent Pilots

When engineering and product teams are running numerous, siloed agent experiments using frameworks like LangChain, CrewAI, or AutoGen, chaos and blindness ensue. Prefactor provides the mandatory single source of truth, giving all stakeholders shared, real-time visibility into every agent's activity across the organization, aligning teams and accelerating governance.

Implementing Secure MCP in Production

As the Model Context Protocol (MCP) becomes the default standard for agents to access tools and data, production teams are left flying blind. Prefactor delivers the essential control plane for MCP, providing the identity, authorization, and auditing layer that is absolutely required to use MCP securely and compliantly in enterprise environments.

Streamlining Compliance & Audit Reporting

When compliance or security teams demand an audit of AI agent activities, manual investigation of API logs is a slow, error-prone nightmare. Prefactor is necessary to automate this process, generating clear, business-aligned reports that definitively show what agents did, when, and why, satisfying regulatory inquiries in a fraction of the time and cost.

Frequently Asked Questions

What is an AI Agent Control Plane?

An AI Agent Control Plane is a dedicated governance layer that provides essential oversight for autonomous AI systems. It is the critical infrastructure that manages agent identity, enforces access policies, monitors activity in real-time, and maintains comprehensive audit trails. Without it, deploying agents at scale is insecure and unmanageable, especially in regulated environments.

How does Prefactor handle agent authentication?

Prefactor moves beyond basic, insecure API keys or M2M tokens. It gives each agent a unique, first-class identity with dynamic registration and supports industry-standard OAuth 2.0 and OpenID Connect (OIDC) protocols. This ensures every agent action is strongly authenticated and permissions can be delegated and scoped with fine-grained precision, which is mandatory for production security.

Can Prefactor work with existing AI agent frameworks?

Yes, integration is a core priority. Prefactor is designed to work seamlessly with popular frameworks like LangChain, CrewAI, and AutoGen, as well as custom-built agents. The platform provides the necessary interoperability to deploy a unified control layer across your entire agent ecosystem in hours, not months, which is vital for rapid yet governed adoption.

Why is real-time monitoring for AI agents a necessity?

Real-time monitoring is non-optional because AI agents operate autonomously and can trigger cascading failures or incur unexpected costs rapidly. Without immediate visibility into which agents are active and what resources they are accessing, teams cannot answer urgent operational questions, prevent incidents, or optimize performance, leading to unacceptable business risk and downtime.