ianalloway1

How do you calibrate the error-finding model against human labels? My worry with judge models is that they share the agent's blind spots, since both were trained on similar data. Do you see a measurable gap between what the model flags and what a human reviewer flags on the same traces?

Curious how you handle actions with real-world cost versus plain read/write scopes. In my own agent experiments the useful controls were per-action spend caps, a rolling daily budget, and a human approval step above a threshold. Does Alter model 'this tool call spends money' as its own kind of permission, or is that left to the app?