> For the complete documentation index, see [llms.txt](https://argos-ci.com/docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://argos-ci.com/docs/agents/agents.md).

# Overview

Argos is built to work with AI agents. Whether your assistant reviews visual changes in a pull request, inspects builds from your terminal, or answers questions about Argos, these are the features it can use.

<table data-view="cards"><thead><tr><th></th><th></th><th data-hidden data-card-target data-type="content-ref"></th></tr></thead><tbody><tr><td><strong>MCP server</strong></td><td>Connect AI assistants like Claude, Cursor, or Copilot to Argos: list builds, inspect diffs, review changes, and manage projects through the Model Context Protocol.</td><td><a href="/pages/0axIlbxcGWsTpLc1S4qq">/pages/0axIlbxcGWsTpLc1S4qq</a></td></tr><tr><td><strong>CLI</strong></td><td>Upload screenshots, inspect builds, and submit reviews from scripts and local workflows — the tool agents use when they work in your terminal.</td><td><a href="/pages/ce4ff41dcaf17a29ff520ad83b29a2afab15bfa4">/pages/ce4ff41dcaf17a29ff520ad83b29a2afab15bfa4</a></td></tr><tr><td><strong>Agent skills</strong></td><td>Installable skills that teach your assistant the Argos CLI and a complete pull-request visual review workflow.</td><td><a href="/pages/TATuQgbXKE4CEGUazUvD">/pages/TATuQgbXKE4CEGUazUvD</a></td></tr><tr><td><strong>AI-ready docs</strong></td><td>This documentation is optimized for LLMs: Markdown for every page, <code>llms.txt</code> indexes, and its own docs MCP server.</td><td><a href="/pages/juJ8a2yOgrosLCxNrOWJ">/pages/juJ8a2yOgrosLCxNrOWJ</a></td></tr></tbody></table>

### Where to start

* To review builds with an AI agent in a pull request, follow the [Review builds with AI agents](/docs/learn/review-workflow/review-builds-with-ai-agents.md) guide — it combines the CLI and agent skills.
* To let an agent track down what makes a screenshot unstable, follow [Fix flaky tests with AI agents](/docs/learn/reliability-and-flakiness/fix-flaky-tests-with-ai-agents.md).
* To give a general-purpose assistant access to your Argos account, connect it to the [MCP server](/docs/agents/mcp-server.md).
* To let an agent answer questions about Argos itself, point it at the [AI-ready docs](/docs/agents/ai-ready-docs.md).


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# 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 dynamically by asking a question.

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

```
GET https://argos-ci.com/docs/agents/agents.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

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.