For most of computing history, the machine waited for you. You decided what it would do and when. If the output was wrong, the cause was findable. Responsibility and observability were the same thing.
That world is gone. Agents now receive a goal and make hundreds of decisions you never witnessed. The middle of your job disappeared, but it did not become somebody else's responsibility. It is still yours professionally, legally, and reputationally. It is simply less visible.
The trail matters more than the summary
A fluent final answer is the part most optimised to look good. It tells you little about whether the reasoning underneath was sound.
When you cannot watch the work, require the trail. Sample decisions, not just conclusions. Ask what the skill almost did. Make it declare where it guessed. Look for what is missing. Keep irreversible actions behind a human gate.
Autonomy should be earned against evidence. A new skill gets narrow scope and reversible actions. A skill that produces verifiable work across many runs earns wider scope. If it drifts, the scope contracts again.
When you cannot watch the work, the only thing you can rely on is the character of the thing doing it. Which is the whole argument of this series.
This is part of the Parenting Your AI series, a practitioner's guide to building AI skills that are safe, effective, and worth trusting. Written from inside enterprise AI systems by someone who has spent years diagnosing what goes wrong when AI meets real work at scale.
Read the full series at KnowledgeManagement.ie