One captures policy on a periodic cadence, the other evaluates every production run against it, so they are not alternatives.[1]
Credo AI covers the documentation side of AI risk: model testing, policy mapping, and risk registers on a review cycle. Prefactor evaluates each agent run in production between those reviews: quality, drift, and cost per run. One documents the programme, the other measures the live agents.
| Decision factor | Credo AI | Prefactor |
|---|---|---|
| Where it fits | Documenting AI policy | Knowing each agent did its job in production |
| Primary buyer | Risk, legal, and responsible-AI teams | Teams running agents in production |
| Cadence | Periodic reviews and audit cycles | Every run, as agents execute |
| What it covers | Model portfolio: bias, fairness, documentation | Agent outcomes: quality, drift, cost per run |
| How it attaches | You assess and document your models | Native SDK, core SDK, or OpenTelemetry ingest, no rebuild |
| Use them together? | Document policy with Credo AI | Evaluate the agents with Prefactor |
Best for risk, legal, and responsible-AI teams that need to document and demonstrate responsible AI across a model portfolio, ahead of a regulatory review.
Best for teams running agents in production who need to know each one is doing its job, and prove it.
| Capability | Credo AI | Prefactor |
|---|---|---|
| Documenting AI policy | ||
| Model risk documentation (bias, fairness) | ✓ | — |
| Compliance mapping (EU AI Act, NIST, ISO 42001) | ✓ | — |
| AI asset catalogue (models, apps, datasets) | ✓ | — |
| Evaluating agents in production | ||
| Quality score per run | — | ✓ |
| Cost attributed per agent and version | — | ✓ |
| Drift detection against a baseline | — | ✓ |
| Hold or escalate a risky action | — | ✓ |
| Across your stack | ||
| Agent evaluation on any framework | — | ✓ |
| One queryable record per agent | Portfolio-level catalogue | ✓ |
| Evidence for a specific decision | Periodic compliance reports | Per-run audit trail |
We sell the layer this section describes. Read it with that in mind.
Credo AI answers whether your AI programme is documented and defensible: which models exist, how they were tested, what policy they map to. It does not say whether an agent did its job between reviews.
A model can pass its bias tests and still ship an agent that drifts after a prompt change and starts returning wrong answers.
Prefactor keeps a quality score per run, tracks it per agent across versions, and flags when behaviour drifts.
Every run leaves a record you can hand to a customer or an auditor.
Prefactor reads them through a native SDK or any OpenTelemetry source, so the policy Credo AI captures and the runtime evidence Prefactor produces sit side by side.
Reviewed against public product and documentation pages on March 19, 2026. If a vendor has changed a feature, product name, or positioning since then, send a correction and we will update it. Numbered source links in the page body point to the ordered sources below.
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.
Prefactor helps teams observe, evaluate, and improve their AI agents in production, across every framework and provider.