Many AI workflows treat human-in-the-loop as a final approval step.

That is too late.

If AI has already modified critical code, triggered external actions, polluted project state, or opened a PR, a final approve button can only catch part of the problem. The dangerous side effects may already have happened.

Humans should stand at risk boundaries, not only at the end of the workflow.

What is a risk boundary?

Risk boundaries are concrete.

In software projects, they often include:

money, payment, trading, or payout;

permissions, security, privacy, or compliance;

database schema or persisted formats;

production data or production configuration;

release, deployment, or migration;

irreversible external actions;

product claims that cannot be verified.

In professional tools, risk boundaries may also include:

writing uncertain visual recognition into project truth;

turning a source-specific repair into a general rule;

applying irreversible changes to a user project;

presenting an unverified demo as…