> For the complete documentation index, see [llms.txt](https://docs.n8n.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.n8n.io/build/integrate-ai/understand-ai-components/what-agents-do.md).

# What agents do

One way to think of an agent[^1] is as a [chain](/build/integrate-ai/understand-ai-components/what-chains-do.md) that knows how to make decisions. Where a chain follows a predetermined sequence of calls to different AI components, an agent uses a language model to determine which actions to take.

Agents are the part of AI that act as decision-makers. They can interact with other agents and tools[^2]. When you send a query to an agent, it tries to choose the best tools to use to answer. Agents adapt to your specific queries, as well as the prompts that configure their behavior.

## Agents in n8n <a href="#agents-in-n8n" id="agents-in-n8n"></a>

n8n provides one Agent node, which can act as different types of agent depending on the settings you choose. Refer to the [Agent node documentation](/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.agent.md) for details on the available agent types.

When you execute a workflow containing an agent, the agent runs multiple times. For example, it may do an initial setup, followed by a run to call a tool, then another run to evaluate the tool response and respond to the user.

[^1]: AI agents are artificial intelligence systems capable of responding to requests, making decisions, and performing real-world tasks for users. They use large language models (LLMs) to interpret user input and make decisions about how to best process requests using the information and resources they have available.

[^2]: In an AI context, a tool is an add-on resource that the AI can refer to for specific information or functionality when responding to a request. The AI model can use a tool to interact with external systems or complete specific, focused tasks.