Guardrails that intercept
Rails evaluate the turn at defined seams and can monitor, redact, block, escalate or abort. A blocked call never reaches the tool.
Governance
AI agent governance
AI agent governance
that actually holds
Instructions in a prompt are advice a model can ignore. AI agent governance means the platform intercepts the action, checks it against policy, and can refuse.
Policy belongs in the platform, not the prompt
Asking a model nicely to avoid a destructive action is not a control. It is a hope with good intentions. Real governance sits between the decision and the effect, where it can still say no.
Human approval on risk
A high-risk action pauses and waits for a person. The run resumes only on an explicit decision, and the decision is recorded with it.
Attributable identity
Every request carries an authenticated identity, so an action traces back to the person it was taken for rather than to a shared service account.
Evidence after the fact
Structured traces of calls and results, so a review answers what happened instead of reconstructing it from logs.
The questions a security review will ask
AI agent governance is easier to talk about than to evidence. These are the questions that decide whether an agent ships, and each one has a concrete answer here.
- Who is the agent acting as? An authenticated identity on every request, never an anonymous shared account.
- Whose credentials does it use? Encrypted per user, with OAuth handled and revocation available at any time.
- What is it allowed to do? Tool access is granted explicitly, and guardrails can refuse a call the model still tries to make.
- What needs a human first? Risky actions escalate and park until somebody approves them.
- Where does the data live? Your database, your VPC, if that is what your policy requires.
- What can you show an auditor? A structured record of the calls made and the results returned.
Governance you can change without a redeploy
Policy is configuration, not a fork of the codebase. Rails ship switched off, so an agent with no policy behaves exactly as one written before the feature existed, and turning a rail on is a config change rather than a rewrite.
Start permissive
Run in monitor mode first and watch what the agent tries. Real traffic tells you which rails you actually need.
Tighten where it matters
Escalate the handful of actions with real consequences, and leave the rest alone. Gating everything trains people to click approve.
Keep the evidence
Every decision, refusal and approval lands in the record, which is what turns AI agent governance into something you can demonstrate.
Governance and capability pull against each other, and pretending otherwise helps nobody. The useful question is not how to restrain an agent completely, but which actions deserve a human and which do not. Read the enterprise controls for how this is deployed.
Bring your security team
The questions above are the ones they will ask. We are happy to answer them together.
Frequently asked questions
What is AI agent governance?
It is the set of controls that decide what an autonomous agent may do, enforce those limits at the moment of action, and leave evidence afterwards. It covers identity, credential scope, tool permissions, human approval on risky actions and traceability.
Why are prompt instructions not enough?
A prompt is advice the model can ignore, and a model under pressure sometimes does. Governance has to sit in the platform, between the decision and the effect, so a refused action genuinely cannot reach the tool.
Does governance slow the agent down?
Most rails are checks on a call that was going to be made anyway. The visible cost is human approval, which applies only to the actions you choose to gate.