Recent approaches combine LLMs with causal discovery by asking models to infer pairwise directions and propose graph structures; this paper examines that paradigm in the context of agentic systems. It's relevant for builders working on reasoning agents and knowledge graphs, where distinguishing correlation from causation directly affects downstream decisions.