LLM Agents: What They Are, Tools, and Examples
We can define an "agent" as a person who acts on behalf of another person or group. However, the definition of agent with respect to Large Language Models (LLMs) is a hotly debated topic with no one definition yet reigning.
We like to refer to an LLM agent as an autonomous or semi-autonomous system that can act on your behalf. The core concept is the use of tools to enable the LLM to interact with its environment through tool use.
Agents can be used to handle complex, multi-step tasks that may require planning, data retrieval, or other dynamic paths that are not necessarily fully or well defined before starting the task.
This goes beyond what an LLM normally does on its own — which is to generate text responses to user queries based on its pre-training — and steps up its autonomy in planning, executing tasks, using tools, and retrieving external data.
What makes LLM agents useful is they can function within workflows that integrate multiple systems and services without having to fully define every step of the process beforehand.