Spent the last few months running multiple agents for job hunting and editing workflows. The failure mode that kept hitting me wasn’t bad outputs. It was agents making decisions I never saw and wouldn’t have seen without digging into the data behind them. By the time I noticed, the action had already happened. Caught one bad one before it went out. Didn’t catch all of them. Ash and Professor Oak would be disappointed. So I built an interrupt layer. Before any consequential action executes, the agent signals a control plane, a gate fires, and I decide. Approve, deny, or edit. Every decision gets logged. That part works. But now I’m sitting on something more interesting. A personal dataset of labeled decision points. Every approve/deny/edit is a signal. The agent proposed X, I said no and changed it to Y. I’m building a hyper-personalized training set inside my own control plane. The direction I’m heading is using that decision history to build a recommendation model. The more agents I run, the more critical the decision layer becomes, especially as stakes go up. I can’t remove the human from the loop. But I want a smarter decision matrix so I’m only reviewing low-confidence outputs, not everything. The research paper that dropped yesterday on AI-based decision making and fatigue reinforces why the data behind decisions matters more than the decisions themselves at scale. Curious how others are structuring this. Are you capturing decisions at the action level, output level, or earlier in the chain? And what measurable outcomes are you actually tracking? submitted by /u/dc_719
Originally posted by u/dc_719 on r/ArtificialInteligence