Intelligent turn detection, or endpointing, is crucial in enhancing the user experience of AI voice agents by effectively managing turn-taking in conversations, moving beyond traditional silence-based methods to more sophisticated semantic approaches. The article discusses the challenges of latency and turn detection in voice agents, highlighting the importance of accurately detecting the end of a user's speech to facilitate natural interactions. It explores three main endpointing methods: manual, silence detection, and semantic endpointing, with the latter being the most advanced, utilizing language models to predict semantic completeness and sentence boundaries. AssemblyAI's Universal-Streaming model exemplifies semantic endpointing by integrating both semantic content analysis and audio context, which offers robust performance across diverse conditions. The comparison with other models like LiveKit and Pipecat showcases the advantages of a hybrid approach, emphasizing the need for adaptable systems that can handle various acoustic scenarios and speaker variations. As the field of conversational AI evolves, the integration of multimodal signals promises to further refine turn detection, making voice interfaces more responsive and human-like.