Prefactor

About Prefactor

Prefactor is the essential control plane for AI agents, designed to solve the critical governance gap that emerges when moving autonomous agents from proof-of-concept to production. It provides a centralized platform for managing identity, access, and auditability across all AI agents within an organization. Built specifically for product and engineering teams in regulated enterprises, Prefactor addresses the core challenges of security, compliance, and operational visibility that typically block agent deployment at scale. Its main value proposition is transforming complex, ad-hoc agent authentication and monitoring into a single, elegant layer of trust. By giving every AI agent a first-class, auditable identity and enabling policy-as-code management, Prefactor aligns security, product, engineering, and compliance teams around one source of truth. This allows companies to govern faster with shared visibility and control, ensuring agents can operate safely within environments where "move fast and break things" is not an option, such as banking, healthcare, and mining.

Features of Prefactor

Identity-First Agent Control

Prefactor assigns a unique, first-class identity to every AI agent, similar to human user identities in traditional IAM systems. This foundational feature ensures every agent action is authenticated and every permission is explicitly scoped. It supports dynamic client registration, delegated access, and fine-grained role and attribute-based controls, bringing proven governance principles to the world of autonomous AI.

Real-Time Agent Monitoring & Dashboard

Gain complete operational visibility across your entire agent infrastructure from a centralized dashboard. This feature allows teams to monitor all agents in one place, seeing which are active or idle, what resources they are accessing, and where failures or issues are emerging in real-time. This proactive visibility helps prevent incidents before they cascade and answers the critical question, "What is this agent doing right now?"

Compliance-Ready Audit Trails

Prefactor generates detailed audit logs that translate technical agent actions into clear business context. Instead of cryptic API calls, compliance officers and stakeholders receive understandable records of agent activity. This feature enables the generation of audit-ready reports in minutes, designed to withstand rigorous regulatory scrutiny in industries like finance and healthcare.

Policy-as-Code & Automated Governance

Manage and enforce agent access control through declarative policy-as-code. This allows security and platform teams to define, version, and automate permission policies directly within their CI/CD pipelines. It ensures consistent governance, reduces human error, and enables scalable management of permissions as the number of agents grows, integrating security directly into the development lifecycle.

Use Cases of Prefactor

Scaling AI Agents in Regulated Industries

For enterprises in banking, healthcare, or mining, deploying AI agents requires rigorous compliance proof. Prefactor provides the necessary audit trails, identity controls, and real-time monitoring to meet regulatory standards. It enables these companies to move from isolated agent pilots to approved, production-scale deployments by answering critical compliance questions with clarity and evidence.

Unifying Visibility Across Multiple Agent Frameworks

Teams using a mix of AI frameworks like LangChain, CrewAI, and AutoGen often struggle with fragmented oversight. Prefactor integrates with these popular frameworks, offering a single control plane to monitor, secure, and audit all agents regardless of their underlying technology. This eliminates visibility silos and simplifies governance for platform engineering teams.

Implementing Emergency Control and Kill Switches

In production environments, the ability to immediately intervene on agent activity is non-negotiable. Prefactor provides operational controls, including emergency kill switches, allowing human operators to instantly deactivate or restrict agents that are behaving unexpectedly or causing issues, ensuring human-delegated control is always maintained.

Optimizing Agent Operational Costs

As agent usage scales, compute costs can spiral without visibility. Prefactor's cost tracking and optimization features allow teams to monitor agent compute costs across different providers, identify expensive or inefficient interaction patterns, and make data-driven decisions to optimize spending and resource allocation.

Frequently Asked Questions

What is an AI Agent Control Plane?

An AI Agent Control Plane is a centralized management layer that provides governance, security, and operational oversight for autonomous AI agents. It handles core functions like identity and access management (IAM), real-time monitoring, audit logging, and policy enforcement. Prefactor acts as this control plane, ensuring agents are secure, compliant, and observable as they scale from demos to production.

How does Prefactor handle authentication for agents?

Prefactor provides identity-first authentication for agents, moving beyond simple API keys or M2M tokens. It offers interoperable OAuth 2.0 and OpenID Connect (OIDC) support, giving each agent a unique, auditable identity. This allows for fine-grained, delegated access controls and integrates with existing enterprise identity providers, creating a robust and standards-based security layer.

Is Prefactor built for the Model Context Protocol (MCP)?

Yes, Prefactor is designed with MCP (Model Context Protocol) as a primary use case. The product recognizes MCP as the emerging standard for how agents access tools and data. Prefactor provides the missing production-grade security and visibility layer for MCP, ensuring teams are not "flying blind" when deploying MCP-based agents in regulated environments.

Can Prefactor be integrated into existing CI/CD pipelines?

Absolutely. A core feature of Prefactor is its policy-as-code approach, which is designed for automation. Security and platform teams can define agent access policies as code and seamlessly integrate permission automation and governance checks into their existing CI/CD pipelines, enabling DevSecOps practices for AI agent deployments.