A new arXiv paper argues that decision support needs to be rethought for agentic AI systems.
Traditional decision support studies how humans use models to make better choices. In agentic workflows, the roles can reverse: AI agents act on behalf of users while people, tools, and other systems become support mechanisms.
The paper focuses on two practical questions: when support is worth its cost, and how uncertainty should guide an agent before consequential actions.