# Overview

{% embed url="<https://youtu.be/l89A5rGmfGQ>" %}

{% content-ref url="build" %}
[build](https://docs.usepylon.com/pylon-docs/ai-agents/build)
{% endcontent-ref %}

{% content-ref url="test" %}
[test](https://docs.usepylon.com/pylon-docs/ai-agents/test)
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{% content-ref url="deploy" %}
[deploy](https://docs.usepylon.com/pylon-docs/ai-agents/deploy)
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{% content-ref url="monitor-and-iterate" %}
[monitor-and-iterate](https://docs.usepylon.com/pylon-docs/ai-agents/monitor-and-iterate)
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## How to think about AI Agents <a href="#expectation-setting" id="expectation-setting"></a>

You can think about AI Agents as an extension of your team, users who can be assigned to issues. They can be used for many things, including:

* Handling of questions based on knowledge base or documentation content
* Initial information collection from customer
* Gathering of context internally for your team
* End-to-end resolution of issue

You can use AI Agents to attempt to automate the pieces of your workflow you want to - **expect it to be an iterative process** to identify the best workflows to automate, and to build your skills in instructing the agent.


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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.usepylon.com/pylon-docs/ai-agents/overview.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.