When combined with advanced techniques like sentence and word embeddings, hierarchical clustering can uncover higher-order semantics—providing insight into the underlying “thoughts” or “ideas” within the data. This capability is crucial not only for understanding textual data but also for applications such as AI agent alignment, where interpreting the deeper meanings and intentions behind clusters can guide more accurate and aligned AI behavior.