Researchers have published a survey on agentic video understanding, which involves AI systems that actively decide what information to process from video data. The survey formalizes an agent loop for video understanding, addressing the question of why agents are important for this task. It organizes the literature through a taxonomy that links various video understanding challenges to design considerations, including context bottlenecks, evidence memory, and multimodal ambiguity. The survey also reviews state space paradigms, learning paradigms, supervision signals, benchmarks, and evaluation protocols. It identifies open research directions towards developing agentic-native temporal modeling and video-native agents, which could improve the performance and efficiency of AI systems that process video data.