Designing for Doubt: The Case for Informed Abstention in Autonomous Agents
As large language models gain tool access and are deployed as autonomous agents capable of editing records, executing transactions, and modifying infrastructure, we still evaluate them based on the sole metric of task completion. We argue that this evaluation focus constitutes a systematic design failure. Benchmark scoring, product metrics, and default deployment configurations all reward agents for proceeding even when they lack the inputs, evidence, or authorization required to do so safely. We call this phenomenon compliance bias. This paper makes three contributions. First, we show how compliance bias is embedded in the evaluation regimes that currently shape agent development: promin...
| Authors | Victor Ojewale, Suresh Venkatasubramanian |
|---|---|
| Primary category | cs.AI |
| Published | 1 June 2026 |
| Last updated at source | 3 August 2026 |
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