Trust your AI Agents.

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.

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.

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.

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.

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.

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.

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.

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’s actually using.

Seventeen categories in total, including standard PII (names, contact, location, behavioural), financial records, credentials, confidential business data, and the GDPR Article 9 special categories — health, biometric, genetic, racial or ethnic origin, religious belief, political opinion, sex life or orientation, and trade union membership.

The first is the design — the permissions and data access an engineer declared when they built the agent. The second is the reality — what the agent has actually invoked in production. Most teams only see the first. Prefactor shows you both, side by side, so you can see where an agent has drifted from what it was designed to do.

Risk is declared in the schema, not sniffed from payloads at runtime. Each span type in your agent has a data risk definition: which categories of data flow through its inputs and outputs, what classification level they’re at, and what actions it’s allowed to take. That makes the risk profile auditable, version-controlled, and reviewable by a human before it ever runs.

A security audit asks whether your infrastructure is hardened. An Agent Audit asks something different: whether the agent itself is doing what you think it’s doing. It’s the layer between the model and the systems it touches — the part most security tooling doesn’t cover yet.