reynforce

What's the difference between AI guardrails and agent governance?

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Guardrails judge a single output — is this sentence safe to send? Governance answers the questions around every output: who let this agent do this, what it relied on, who owns that, and what it has said so far.

Both words are used loosely, and vendors use them for whatever they sell. The distinction that holds up is about what each one can see.

What each can see

guardrailsgovernance
unitone model outputan agent over time
checkscontent: toxicity, leaks, off-topic, formatauthority: what it may do, on what information, approved by whom
knows about your companyvery littlewho owns what, and what is currently true
leaves behinda pass or a blocka record somebody can be shown
typical ownerthe engineer who built the agentthe people accountable for what it commits to

Access control is a third thing, often sold as governance: which tools and data an agent may reach. An agent offering an unapproved discount passes it — allowed tool, valid access, unauthorised offer.

Where commitments fall

Between all three. Happy to confirm SSO will be live by March passes a wording check and an access check. What catches it is knowing that a date is a promise, that nothing is in place to keep this one, and that somebody must answer for it. That is governance applied to what an agent commits your company to.

What good governance looks like for a sales agent

What this does not do

Reynforce is not a guardrail on wording and does not control access. It is the part of governance about commitments.

/what-is-a-gtm-agent-guardrail.md defines the commitment layer. /compare.md sets it beside the tools people already have.