# AI Agents

CARTO AI Agents provide a powerful conversational interface that allows anyone, regardless of technical expertise, to ask questions in natural language and receive instant, actionable insights. This marks a fundamental shift beyond dashboards to a dynamic, intuitive way of exploring your geospatial data.

<figure><img src="https://3029946802-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FybPdpmLltPkzGFvz7m8A%2Fuploads%2Fgit-blob-cd4e59c1cff03c337eb1147bb11fab37907a3c5f%2Faiagent_location.gif?alt=media" alt=""><figcaption></figcaption></figure>

With CARTO, you can create and publish powerful geospatial AI Agents tailored to your specific needs. By combining your custom prompt instructions with CARTO's built-in tools and your own MCP tools, you can build trustworthy solutions that make complex geospatial analysis accessible to any user within your organization.

## What is an AI Agent?

An AI Agent is a sophisticated system powered by a large language model (LLM) that goes beyond simple chat. It autonomously interacts with your data and a robust set of tools to solve complex geospatial tasks, reasoning through problems to deliver actionable, data-driven insights.

Each agent you create is built from three core components:

* **Logic (Use Case & Instructions)**: The agent's "brain" — defining its purpose, behavior, and expertise through clear, structured instructions.
* **Tools & Capabilities**: The agent's "hands" — accessing a powerful suite of built-in geospatial tools and extending skills with your custom MCP tools.
* **Model (LLM)**: The agent's "engine" — powering reasoning, language understanding, and decision-making capabilities.

<figure><img src="https://3029946802-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FybPdpmLltPkzGFvz7m8A%2Fuploads%2Fgit-blob-aa7244f3d277e41fde785034804566c92732e01b%2FScreenshot%202025-10-04%20at%2010.14.45.png?alt=media" alt=""><figcaption></figcaption></figure>

## Getting started with AI Agents

This documentation walks you through the complete journey of building and deploying AI Agents:

#### Learn the basics

* [**Creating your Agent**](https://docs.carto.com/carto-user-manual/ai-agents/creating-your-agent) - Set up your agent and understand the interface
* [**Agent Config Assistant**](https://docs.carto.com/carto-user-manual/ai-agents/agent-config-assistant) - Configure your Agent through conversation: create, iterate, and refine
* [**Understanding Agent behavior**](https://docs.carto.com/carto-user-manual/ai-agents/understanding-agent-behavior) - Learn how agents interpret queries and make decisions
* [**Choosing the right model**](https://docs.carto.com/carto-user-manual/ai-agents/choosing-the-right-model) - Select the optimal LLM for your use case

#### Build and configure

* [**Defining your Agent logic**](https://docs.carto.com/carto-user-manual/ai-agents/defining-your-agent-logic) - Write effective Use Cases and Instructions
* [**Working with tools**](https://docs.carto.com/carto-user-manual/ai-agents/working-with-tools) - Integrate Core Tools and custom MCP Tools
* [**Configuring capabilities**](https://docs.carto.com/carto-user-manual/ai-agents/configuring-capabilities) - Enable advanced features like Query Sources

#### Deploy and share

* [**Iterate and refine**](https://docs.carto.com/carto-user-manual/ai-agents/iterate-and-refine-your-agent) - Test and improve your agent based on feedback
* [**Sharing your Agent**](https://docs.carto.com/carto-user-manual/ai-agents/sharing-your-agent) - Publish and distribute to your organization

#### Reference

* [**Tools Reference**](https://docs.carto.com/carto-user-manual/ai-agents/ai-tools-reference) - Comprehensive documentation of all available Core Tools


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# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.carto.com/carto-user-manual/ai-agents.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
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.