Originally published on robatdasorvi.com
Putting a human in the loop doesn't make your AI system safer — it makes it slower, more expensive, and for most task categories at scale, less accurate.
That sentence will make some of you close this tab. That's fine. But if you're building an AI agent pipeline and you've defaulted to human-in-the-loop because it feels like the responsible call, you're spending real money to introduce real errors while believing you're reducing them. The economics of AI agent deployment have shifted faster than most teams have adjusted. The prevailing logic hasn't kept up.
The standard position in enterprise AI circles goes like this: autonomous agents are useful, but humans must review consequential outputs. Every responsible AI framework says some version of it. Every cautious CTO defaults to it. The reasoning sounds airtight — AI makes mistakes, humans catch them. The system is only as risky as the humans allow it to be.
The problem is this reasoning treats human review as free and infinitely reliable. It is neither.
