A policy document can't tell you whether an agent actually followed it at 2am on a Saturday.
Maps to EU AI Act, NIST AI RMF, ISO 42001, and SOC 2: evidence generated continuously, not compiled at reporting time.
Every agent action lands in a queryable, tamper-evident record, separated from human activity. Runs are checked against the rules you set, a breach can be held or blocked, and the evidence maps to your frameworks.
Most organizations already have the AI principles and risk policies. What's missing is proof an agent actually stayed inside them, and evidence to show a regulator or auditor.
Defines the rules. No way to check an agent actually followed them.
Scores, enforces, and logs every run against those rules, continuously.
When audit, risk, or a regulator asks what an AI system actually accessed or changed, most teams have governance documents but limited runtime visibility into what really happened.
Agents connected to customer data, internal tooling, and communication platforms can expose names, emails, SSNs, or credentials in an output without a safeguard catching it.
AI deployments cut across engineering, security, legal, and business teams, so accountability for what an agent can access and do gets harder to pin down as adoption spreads.
Documentation tools like Credo AI produce model cards, bias reports, and compliance artefacts, and are genuinely good at that job. Prefactor does the operational half: continuous risk assessment, inline blocking, and approval routing on every agent run. They're complementary, not competing: one documents that governance exists, the other ensures it executes. See Prefactor vs. Credo AI → for the honest breakdown.
Rather than one opaque number, every run is scored on three dimensions that combine into a single classification your team reviews.
Scored against the run's own record: inputs, outputs, and whether the task was actually completed correctly, not a generic fleet-wide average.
Token, API, and compute cost measured against the value of the task, so an expensive run for a trivial task is visible, not averaged away.
Whether the agent operated within its approved permissions and boundaries, the same signal runtime policies and approval routing act on.
Model Context Protocol connects agents to file systems, databases, and APIs. Ungoverned, that's tool poisoning and data exfiltration waiting to happen, and the same governance layer covers it.
An agent that can read a system can repeat what it read somewhere else.
How it gets caught →No kill switchWhen an agent misbehaves, nothing can stop it short of stopping everything.
How it gets caught →Hidden failuresAgents fail quietly and the first signal is a complaint, not an alert.
How it gets caught →Book a demo and we'll walk through what composite risk scoring and audit evidence look like against your own agents.
Prefactor helps teams observe, evaluate, and improve their AI agents in production, across every framework and provider.