With the rapid development of large language models (LLMs), their integration into autonomous systems has become essential. This integration significantly increases the flexibility and adaptability of the system. In this paper, we propose a categorization of LLM integration into three levels: open-loop, closed-loop, and autonomous systems fully driven by robotic LLMs. They are analyzed through existing literature, real experiments with the humanoid robot TIAGo, and simulations with models such as ChatGPT-4 and Vicuna 13b-v1.5-16k. We demonstrate the potential of LLMs to enhance the flexibility and adaptability of autonomous systems, particularly in dynamic environments where conventional finite state machines may prove inadequate. Closed-loop systems effectively handle unexpected situations with human-like problem solving. Integrating LLMs with autonomous systems enables new real-world applications by enhancing their ability to adapt, reason, and respond intelligently in dynamic environments.