> ## Documentation Index
> Fetch the complete documentation index at: https://docs.chatmode.com.br/llms.txt
> Use this file to discover all available pages before exploring further.

# Agents

Agents are the core AI assistants you create and configure in Chatmode. They handle conversations with your users, provide information, and perform actions based on their configuration.

## Creating an Agent

To create a new agent:

1. Navigate to the **Agents** section from the sidebar
2. Click the **Create Agent** button in the top-right corner
3. Fill in the required information:
   * **Name**: A descriptive name for your agent
   * **System Prompt**: Define the agent's personality, knowledge, and behavior (see Prompt Engineering below)
   * **Features**: Select optional features to enhance agent capabilities (see Features below)
   * **Training Data**: Select pre-existing [Training Data](/en/features/trainings) to ground the agent's knowledge
4. Expand **Advanced Settings** and configure:
   * **API Key**: Choose the API key (e.g., OpenAI,) that provides the model and will be used for requests
   * **Model**: Select the specific AI model (e.g., GPT-4o) to power your agent. Available models depend on the selected API Key provider
   * **Temperature**: Control the creativity/randomness level (0.0-1.0)
   * **Max Tokens**: Set the maximum length of a single response from the agent
5. Click **Create**

<Note>
  The **System Prompt** is crucial for your agent's performance. Take time to
  craft a prompt that clearly defines the agent's role, tone, and constraints.
</Note>

## Agent Configuration Options

### Prompt Engineering

The **System Prompt** is the foundation of your agent's behavior. It should include:

* **Role Definition**: What the agent is supposed to be (e.g., customer support specialist)
* **Personality**: How the agent should communicate (e.g., professional, friendly)
* **Knowledge Scope**: What information the agent should provide or avoid
* **Workflow Instructions**: How the agent should handle different scenarios

Example prompt structure:

```
You are a customer support agent for TechGadgets, an electronics retailer.

Your personality is:
- Professional but friendly
- Helpful and patient
- Knowledgeable about tech products

Your goal is to help customers with:
- Product information and recommendations
- Order status inquiries
- Technical support for products
- Return and exchange policies

Always introduce yourself as "TechGadgets Support" at the beginning of conversations.
```

### Model Selection

Chatmode supports multiple AI models with different capabilities:

| Model   | Best For                              | Considerations                         |
| ------- | ------------------------------------- | -------------------------------------- |
| GPT-4o  | Complex reasoning, detailed responses | Higher cost, slightly slower           |
| GPT-4.1 | General-purpose, faster responses     | Cost-effective, less complex reasoning |

### API Key Selection

Ensure you have added the necessary [API Keys](/en/features/api-keys) for the AI providers (like OpenAI) you intend to use. The selected API key determines which models are available.

### Temperature Setting

The temperature parameter controls response randomness:

* **0.0-0.3**: Highly deterministic, consistent responses (good for factual information)
* **0.4-0.7**: Balanced creativity and consistency (recommended for most uses)
* **0.8-1.0**: Maximum creativity and variability (good for creative content)

### Max Tokens

This setting limits the number of tokens (roughly words or parts of words) the agent can use in a single response. Adjust this based on the desired length and complexity of answers.

### Features

Enable specific features to extend your agent's capabilities:

| Feature      | Description                                                                |
| ------------ | -------------------------------------------------------------------------- |
| Lead Capture | Automatically collect user information (like name, email) during the chat. |
| Auto Assign  | Automatically assign conversations to available team members.              |
| Auto Resolve | Automatically mark conversations as resolved after a period of inactivity. |

*Note: Available features may change.*

### Training Data Selection

You can link one or more completed [Training Data](/en/features/trainings) sources to your agent. This provides the agent with specific knowledge from your documents or websites, improving its accuracy and relevance for your specific domain.

## Managing Agents

### Agent List View

The main Agents page displays all your agents with key information:

* Agent name and status (active/inactive)
* Associated environments
* Creation date
* Usage statistics

### Editing Agents

To modify an existing agent:

1. Select the agent from the list
2. Click **Edit** in the agent details page
3. Make your changes to any configuration options
4. Save your changes

<Note>
  Agents currently linked to active [Environments](/en/features/environments)
  cannot be deleted directly. You must first remove the agent from all
  environments.
</Note>

### Agent Analytics

Monitor your agent's performance with built-in analytics:

* Conversation volume over time
* Average response time
* User feedback metrics
* Common topics and questions

## Best Practices

1. **Start Simple**: Begin with a focused use case and expand as you learn
2. **Test Thoroughly**: Try different scenarios to ensure your agent handles them correctly
3. **Iterate Based on Feedback**: Regularly review conversations and refine your agent
4. **Use Training Data**: Enhance your agent with domain-specific knowledge
5. **Monitor and Maintain**: Regularly check agent performance and update as needed

## Next Steps

After creating your agent, you'll need to:

1. [Set up an environment](/en/features/environments) to deploy your agent
2. [Add training data](/en/features/trainings) to improve agent knowledge
3. [Monitor conversations](/en/features/conversations) to ensure quality