What defines an AI agent compared to a standard LLM?

An AI agent is characterized by its ability to not only understand and generate language similar to a standard large language model (LLM) but also to take independent actions based on its understanding of the environment or context. While a standard LLM primarily responds to prompts and generates text based on the patterns learned from its training data, an AI agent incorporates mechanisms to act autonomously, make decisions, and adapt its responses based on real-time interactions and varying situations.

This distinction is crucial as it positions AI agents as more versatile systems capable of operating in dynamic environments, where they can execute tasks, make informed decisions, and learn from experiences beyond mere language generation. The ability of an AI agent to combine LLM capabilities with independent actions enables it to function effectively in applications such as robotics, personal assistants, and automated systems, where context-awareness and proactivity are essential.

The other options highlight characteristics that do not capture the essence of an AI agent's function or capabilities. For example, strictly following user prompts does not allow for adaptability or independent action. Limitations to training data only imply a lack of dynamic interactions, while requiring constant user input undermines the agent's ability to operate independently.