Decision science in the agentic era: Research frontiers on agentic AI and human–agent collaboration

Abstract Decision science is entering a new era in which decisions are no longer made solely by humans, but increasingly by autonomous AI (Artificial Intelligence) agents and human–agent collectives. Prior research often treated AI as a tool for prediction or support. Agentic systems now decide, act, learn, and coordinate over time, changing the unit of analysis for decision science. In this perspective, we argue that this shift requires new conceptual foundations and a sharper research agenda. We outline a design‐oriented framework that decomposes human–agent systems into atomic structures, decision architectures, and field‐to‐model mappings, enabling comparison, experimentation, and cumulative knowledge building. Building on this framework, we introduce the mission of the new “Agentic AI and Human–Agent Collaboration in Business” department. The department emphasizes two central topics: agents as decision‐makers in single‐ and multi‐agent systems, and human–agent collaboration. We also highlight preferred methods, including lab, field, and simulated experiments, as well as agent‐driven research that uses AI agents for exploration, hypothesis generation, and knowledge discovery. The goal is to guide rigorous research on how agentic systems reshape decision‐making in business and society.