DelegationBench: Measuring When AI Agents Should Ask Before Acting
AI agents that send emails, edit files, and make purchases must decide when to act on their own and when to check with the user first. This decision is usually evaluated by showing a model a proposed action, asking whether it should proceed, and scoring agreement with human labels. We introduce DelegationBench to test whether such scores can be trusted. It has 156 scenarios with four possible responses (act, ask for permission, ask for missing information, refuse), and most scenarios come in matched pairs that change a single feature: whether the action was requested, what is at stake, whether it can be undone, or who will see it. Across ten models from five families, agreement scores mis...
| Authors | Shiva Pochampally |
|---|---|
| Primary category | cs.AI |
| Published | 4 October 2026 |
| Last updated at source | 4 October 2026 |
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