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

# Computer Agents

AI agents that operate websites for you, for systems that have no API.

Feathery **Computer Agents** operate websites the way a person would. An agent opens a browser, reads the page, logs in, fills out forms, submits them, and downloads files. Use an agent for systems that have no API, such as carrier portals, legacy admin consoles, and government filing sites.

{% hint style="info" %}
Computer Agents are available on the Business plan. [Reach out to sales](https://www.feathery.io/demo) to get access.
{% endhint %}

## Create a Computer Agent

Go to the **Computer Use** tab of your account and click `Create Computer Agent`. You need the Admin or Editor role.

1. Give your agent a name.
2. Describe what it should do. This is the agent's task.
3. Choose a schedule, or leave it on **Manual**.

## Write the Task

The task is the agent's complete set of instructions, written in plain English. The agent has no other knowledge of your workflow.

```
Go to https://portal.example.com and log in with PORTAL_USERNAME and PORTAL_PASSWORD.
Open the Statements tab, find the most recent monthly statement, and download it.
Report the statement date in your notes.
```

* Name any [credential](/platform/computer-agents/credentials-and-browser-policy.md) you want the agent to use by its key.
* Reference form fields with `{{field_id}}` syntax. See [Pass Form Data into the Task](/platform/computer-agents/trigger-a-run.md).
* Name the landmarks the agent should look for, such as "the Statements tab", rather than scripting each click.

## Agent Settings

* **Properties**: the agent's name, its ID, the task, and the file field that receives downloads.
* **Schedule**: how often the agent runs on its own.
* **Browser Policy**: the page each run opens to, and the domains the agent can reach.
* **Credentials**: logins the agent needs on the site.

## Next Steps

{% content-ref url="/pages/iNMOrX0UQMPneFhF3w75" %}
[Credentials & Browser Policy](/platform/computer-agents/credentials-and-browser-policy.md)
{% endcontent-ref %}

{% content-ref url="/pages/srFuOI4ZPyhwRib76Cbh" %}
[Trigger a Run](/platform/computer-agents/trigger-a-run.md)
{% endcontent-ref %}

{% content-ref url="/pages/hQJdm6KbpSIrZFED3H5Z" %}
[Review a Run](/platform/computer-agents/review-a-run.md)
{% endcontent-ref %}


---

# 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://docs.feathery.io/platform/computer-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.