Runtime Policy Enforcement for AI Agents
Runtime policy enforcement applies governance when an AI agent is about to act, rather than relying only on prompts, documentation or design-time restrictions.
Why runtime enforcement matters
An agent can produce a perfectly formatted proposal that is still outside its authority. Runtime governance therefore needs an independent policy decision based on the current identity, requested capability, target and system state.
This is different from asking the model to remember a rule. The policy decision should remain enforceable even when the model changes.
Deterministic policy decisions
For consequential actions, a deterministic policy engine gives operators a bounded result that can be reproduced and inspected. Sentinel's governed path uses policy evaluation to produce ALLOW, REVIEW or DENY decisions.
Policy can consider action type, target, tenant configuration, capability scope, risk controls and other structured attributes without making the agent itself the final authority.
Runtime guardrails versus execution authority
Runtime guardrails may filter content, inspect behavior or constrain tool use. Execution authority is narrower and stronger: the downstream side effect must not occur unless the required governance decision is valid.
This distinction matters because advisory guardrails can be bypassed if the executor does not actually enforce their result.
Fail-closed enforcement
A production policy boundary should define what happens when policy state, signatures or required infrastructure are unavailable. Sentinel's execution path is designed to fail closed rather than silently granting execution.
This keeps absence of a valid governance decision from becoming implicit permission.