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4 articles from Crelis on Policy Enforcement. Most recent: “An AI Agent Security Policy: What Belongs In One”.
A practical policy separates agent proposals from permission, names approval states, tests material changes, and keeps evidence for disputed actions.
Agentic AI controls are the limits, checks, approvals, and records that decide what an agent may do before it changes money, data, access, or records.
An LLM judge can return a different verdict on the same facts tomorrow; a deterministic policy engine returns the same decision each time and can name the rule that made it — that difference decides w
Every governance pilot dies on the same objection: we cannot risk it blocking a live workflow. Shadow mode answers it by deciding nothing at all.