> For the complete documentation index, see [llms.txt](https://helpdocumentation.gitbook.io/user-documentation/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://helpdocumentation.gitbook.io/user-documentation/ai-dashboard/test-your-ai-agent.md).

# Test your AI Agent

### Step 1: Ensure Training Is Complete

* Make sure your AI agent has already been trained with your files, documents, or data before proceeding to the test.​

### Step 2: Access the Playground

* Go to the "playground" section in your platform where you can test AI agents.​

### Step 3: Select Your AI Agent

* In the playground, you'll see a list of your AI agents.​
* Choose the specific agent you wish to test (e.g., "Support Agent").​

### Step 4: Manage File Selection

* If the agent is trained with multiple files:
  * To get answers from all files, select "none" for file selection.​
  * To get answers from a single file, choose the specific file you want.​

### Step 5: Set Instructions and Defaults

* You can add instructions for the agent, such as its name, response style, or company details.​
* Define what the agent should reply if it does not find an answer in your files.​

### Step 6: Test Responses

* Type a query in the test area (e.g., "hi" or a question from your documentation).​
* Review the reply given by the agent, noting that you will also see the credits charged for each interaction.​

### Step 7: Monitor Credit Usage

* Each test query will show the amount of credits consumed; check pricing with support or sales teams if needed.

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


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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
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Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters:

```
GET https://helpdocumentation.gitbook.io/user-documentation/ai-dashboard/test-your-ai-agent.md?ask=<question>&goal=<user_goal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case.

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.