Large Language Models-based AI Agents (LLM Agents) are capable of replicating human-like intelligent behaviors such as reasoning, planning, decision-making and executing actions across various environments. Recent studies have demonstrated the effectiveness of applying LLM Agents to building energy systems, enhancing automation, streamlining information processing, and supporting decision-making processes while reducing the need for manual intervention and domain-specific expertise. However, the fundamental challenge of how physical building systems are properly textualized so that LLMs can process them remains largely unaddressed. This paper analyzes how various LLM applications in the building and energy domains represent and textualize their physical system targets, based on a preliminary review of recent literature. The study reveals that most current applications rely on custom, simple, unstructured text-based representations. In contrast, a number of existing works have adopted ontology-based representations, which introduce formal semantic graphs that can help integrate heterogeneous information. Building on this observation, the paper highlights ontology-based approaches as a promising direction for enhancing LLM-building interactions