Recent advances in AI have enabled agentic systems to act in operational environments with limited human oversight, raising the risk that they may violate or ignore human instructions and cause damage. The use of a digital twin as an intermediate environment can mitigate this risk by having actions examined before they reach the real system. While digital twin has been examined as environments for AI simulation more broadly, this study focuses on agentic AI and the use of DT as an intermediate environment for examining actions before real world execution. This paper develops a review informed conceptual framework that characterizes digital twin as testing grounds for agentic AI. By examining studies that integrate digital twin with agentic systems, it organizes the role of the digital twin into several stages, connected by pathways. Human role remains vital and takes different shapes around directing, checking, authorizing, and maintaining agentic systems.