This paper studies how large language models (LLMs) adapt their decisions when given experimental feedback. The study found that LLMs use a combination of their initial task context and feedback to make decisions. This is important for AI agent developers to know as it shows that LLMs can learn and adapt, and that their decisions are influenced by both prior knowledge and feedback.