Guardrails
The constraints and checks that keep an AI system within safe and intended behavior, spanning input filtering, output validation, and scoped permissions.
In more detail
Guardrails are the mechanisms that bound what an AI system can do and say. They span the whole pipeline: filtering or framing inputs, constraining the model with clear instructions, validating outputs before they are used, limiting which tools a step can call, and requiring approval for consequential actions. No single guardrail is sufficient; they work as layers.
The purpose is to make the failure modes tolerable. A model will occasionally produce something wrong or be manipulated; guardrails ensure that when it does, the consequence is caught by validation, blocked by scope, or reviewed by a human rather than executed silently. Designing for graceful failure is more realistic than designing for a model that never errs.
Where this shows up at Ceven
Ceven's guardrails are structural: scoped tool access per step, human-approval gates before consequential writes, and a full audit trail so nothing an agent does is invisible. Rather than trusting a model to always behave, the platform limits what a step can reach and puts a person in front of the actions that could do harm.