Governance records stop at deployment; Prefactor takes over once an agent runs, evaluating each outcome against the policy for quality, cost, and approved scope.
An AI governance platform documents the rules an AI system should follow: policy catalogues, model cards, risk registers. Prefactor measures whether each live run met those rules, at acceptable quality and cost. Write the policy with one, then use the other to show your agents held to it.
| Decision factor | AI governance platforms | Prefactor |
|---|---|---|
| When it works | Before deployment, on paper | During production, on live runs |
| What it produces | Policies, model cards, risk registers | A quality score, drift, and cost per agent |
| Primary buyer | The compliance or risk team | The team that owns the agent in production |
| What it measures | Whether a policy is documented | Whether the agent met the policy on each run |
| How it attaches | Forms, catalogues, and records | Native SDK, core SDK, or OpenTelemetry ingest, no rebuild |
| Use them together? | Document with a governance platform | Measure with Prefactor |
Best for compliance and risk teams defining policy and assembling the evidence a regulator expects.
Best for the team running agents in production that has to show a documented policy was met on live runs.
| Capability | AI governance platforms | Prefactor |
|---|---|---|
| Documenting the policy | ||
| Policy catalogues and versioning | ✓ | — |
| Model cards and risk registers | ✓ | — |
| Maps to standards (NIST, EU AI Act) | ✓ | For evidence |
| Measuring against the policy at runtime | ||
| Quality score per run | — | ✓ |
| Quality score tracked per agent and version | — | ✓ |
| Drift detection after a model or prompt change | — | ✓ |
| Cost attributed per agent and version | — | ✓ |
| Acting and recording | ||
| Hold or escalate a risky action before it reaches a user | — | ✓ |
| One queryable record per agent | Documents only | ✓ |
| Scores agents across frameworks from one place | — | ✓ |
| Audit trail tied to live runs | Partial | ✓ |
We sell the layer this section describes. Read it with that in mind.
A policy platform answers what an agent is supposed to do, all before the agent runs. None of that measures what it actually does once live: whether each run met the policy, at acceptable quality and cost.
A documented policy that nobody measures against is a claim, not a result. Prefactor checks each run against the outcome the policy asks for.
The quality score is tracked per agent across versions, and behaviour drift after a change is flagged.
Every run feeds a record tied to live behaviour that you can hand to a reviewer.
It reads the traces your agents already emit, through a native SDK or any OpenTelemetry source, so it measures the policy the documentation defines.
Book a demo and we will evaluate a live agent on a fleet like yours: quality per run, drift after a change, and cost per agent, tied to the policy you already defined.
Prefactor helps teams observe, evaluate, and improve their AI agents in production, across every framework and provider.