Difference Between an LLM and an AI Agent

In AI, Large Language Models (LLMs) generate responses based on input but are limited to single tasks. In contrast, AI agents operate more dynamically, combining specialized roles to make complex decisions and adapt to varied challenges. This difference enables agents to manage tasks, delegate responsibilities, and collaborate effectively with other agents and humans.

Agents can be composed to perform complex tasks, with each agent fulfilling a specific role.

For example, in a development team, different roles like project manager, product manager, backend engineer, frontend engineer, DevOps specialist, and scrum master work together to achieve shared goals. Similarly, in an AI ecosystem, agents can be organized to manage and direct other agents to complete tasks. For instance, the MORagents system has a basic implementation where a delegator agent assigns your query to specific task agents (such as live news, MOR rewards, Tweet generator, etc.).

In contrast, a pure Large Language Model (LLM) is more limited in function. It simply generates text in response to an input query, similar to how a worker uses a tool to complete a task. An agent, however, is capable of using an LLM to make decisions—such as choosing whether to use the LLM again, switching to a different tool, or taking an entirely new approach to accomplish a task.

The primary advantage of agents is that they enable a computer, rather than a person, to make complex decisions autonomously, collaborating with other agents and even humans as needed.

Another view on Agents is given by Lumerin Protocol below