Prefactor

What is AI agent management?

AI agent management is the discipline of tracking, assessing, and governing AI agents across their full lifecycle — from pilot to production. It gives enterprises visibility into what agents are doing, whether they're performing, and the governance evidence needed to scale with confidence.

What is AI agent governance?

AI agent governance is the set of policies, controls, and review workflows that determine how agents are deployed, what identities and permissions they receive, how their behavior is monitored, and what happens when risk thresholds are crossed.

Is Prefactor only for enterprises?

Prefactor is built for enterprises moving AI agents from pilot to production — organisations where governance, visibility, and accountability are required to scale. That said, these challenges aren't unique to large organisations. Prefactor works for teams of all sizes, from startups shipping their first production agents to government agencies.

How do you enforce governance on AI agents at runtime?

Runtime governance means applying policies directly at the agent execution layer - blocking risky actions, detecting PII in outputs, and routing high-risk operations for human approval. Unlike static rules, runtime enforcement adapts as agents operate.

What is the difference between AI security and agent governance?

AI security focuses on threats such as prompt injection, data leakage, model misuse, and compromised tooling. Agent governance focuses on whether agents are approved, operating within scope, producing acceptable outcomes, and following the right human or policy controls in production.

Why do AI agents need identity and scoped access?

AI agents need their own identity and scoped access so each action can be tied to a specific agent, task, and user context. That enables least privilege, traceability, token revocation, and safer delegation than static shared credentials.

How does Prefactor govern AI agents in production?

Prefactor assesses outcome quality, cost efficiency, and scope adherence across AI agents, then can block actions, route them for approval, or record them for audit when policy thresholds are crossed.

How does Prefactor define agent risk?

Risk is broken into two halves. The action profile is what your agent is permitted to do — create, read, update, or delete data, trigger financial transactions, send external communications. The data profile is the categories of sensitive data flowing through it, classified from public through to secret. Together they tell you how much damage an agent could do, and how much of that surface area it is actually using.