> For the complete documentation index, see [llms.txt](https://gitbook.mor.org/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://gitbook.mor.org/meet-morpheus/mysuperagent/difference-between-an-llm-and-an-ai-agent.md). # 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 {% embed url="" %} --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://gitbook.mor.org/meet-morpheus/mysuperagent/difference-between-an-llm-and-an-ai-agent.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.