Policy platforms write the rules. Prefactor proves your agents follow them.
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.
AI governance platforms or Prefactor, in one table
| 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 |
What each one is for
- Policy catalogues: define and version the rules an AI system is meant to follow.
- Model cards and risk registers: record intended use, known limits, and assessed risk before deployment.
- Compliance evidence: assemble the records a regulator or an internal reviewer expects.
- Frameworks: map policies to standards such as the NIST AI RMF or the EU AI Act.
- Stakeholder review: route a policy or a risk assessment for sign-off.
Best for compliance and risk teams defining policy and assembling the evidence a regulator expects.
- Scores each run against the outcome the policy asks for: quality, cost, and whether the agent stayed in scope.
- A quality score per agent tracked across versions, so a regression shows up as a trend.
- Drift detection when behaviour shifts after a model update or a prompt edit, before a user hits it.
- Holds or escalates a risky action for review before it reaches a user, not after.
- One record across frameworks: agents scored from the same place, with an audit trail tied to live runs.
Best for the team running agents in production that has to show a documented policy was met on live runs.
Side by side, documenting against measuring
| 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 | ✓ |
Where policy documentation stops: whether the agent did its job
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.
Does Prefactor replace an AI governance platform?
Is a quality score the same as a compliance record?
Does Prefactor generate compliance documentation?
How does Prefactor attach without a rebuild?
Who owns Prefactor, the compliance team or the agent team?
Measure agents against the policy you documented
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.
Unified performance platform for agents, authentication, and risk management