the tool should know when to stop.
reliable systems need an ending.
an automated process that can begin work but cannot pause, fail safely, or return control is not complete. it is only optimistic.
every tool operates outside its strongest conditions eventually.
the data changes. a request is unusual. a dependency fails. confidence falls while the system continues producing output that looks normal.
define the stopping conditions before deployment.
which signals indicate that the answer is no longer trustworthy? what volume can the system handle before quality changes? which actions are too costly to repeat automatically? who receives the alert?
a stop should preserve evidence.
record what the tool saw, what it attempted, and why it stopped. recovery becomes slower when a failure destroys the information needed to understand it.
design for partial success too. if nine steps complete and the tenth fails, the system should know whether to reverse, wait, or ask for help. blindly restarting may duplicate payments, messages, or decisions.
human review must be reachable and informed. sending a confused customer to an agent who cannot see the automated history adds another failure.
teams often resist stop conditions because they reduce the automation rate. that metric is incomplete. a lower automation rate with safer boundaries may create more trust, less rework, and better outcomes.
test the stop itself. alerts that nobody receives, queues that nobody owns, and recovery instructions that have never been practiced are not controls.
the handoff must work on the worst day, not only during a demonstration.
the strongest tool is not the one that acts without interruption.
it is the one that recognizes when the situation has exceeded its authority and returns control without creating more damage.
