Understanding AI Bots in xBot

In the AI bot framework, there are three distinct types of AI bots designed to handle different aspects of interacting with messages. AI Bots are integral to xBot’s operation, functioning as the virtual agents that execute AI Flows and manage interactions with users. Each AI Bot is equipped to process queries, trigger workflows, and deliver responses across various communication channels. xBot’s AI Bots are structured into three key components: AI Pre, AI Agent, and AI Post, all of which work together to create a seamless user experience.

This guide will explain how AI Bots function, including the roles of AI Pre, AI Agent, and AI Post, how to set them up, and best practices for optimizing their performance.

What Are AI Bots?

The Structure of AI Bots

xBot’s AI Bots are divided into three main components, each serving a distinct role in the message handling process:

1. AI Pre

This component is responsible for preprocessing user queries before they are passed to the AI Agent. It includes tasks such as initial message parsing, language detection, and any other necessary data extraction to prepare the query for processing. This AI bot is responsible for initially screening incoming messages from customers. It checks for inappropriate content or negative sentiments. If the AI PRE agent detects negative content, it returns true, marking the message as NEGATIVE. In such cases, the message is flagged and forwarded to a human agent with a warning, and the AI PRE does not respond to these messages. AI pre will immediately respond to the user's answers in the configuration messages. Here's a basic example class in Java to represent this functionality:

public class AiPreAgent {
    public boolean checkMessageContent(String message) {
        // Implement logic to check for negative content
        boolean isNegative = false;
        // Example logic
        if (message.contains("negative keyword")) {
            isNegative = true;
        }
        return isNegative;
    }
}

User Interface (UI):

2. AI agent

The AI Agent is the core of the AI Bot, where the actual decision-making and task execution occur. It uses the logic defined in the AI Flow to interpret user queries, execute workflows, and generate responses. This AI bot handles responses to customer queries based on the knowledge it has been trained on. It automatically provides answers to customer questions using its AI capabilities. Below is a simple example of how this might be implemented in a Java class:

public class AiAgent {
    public String respondToCustomer(String query) {
        // Logic to generate response based on AI knowledge
        String response = "This is an automated response based on your query.";
        return response;
    }
}

User Interface (UI):

3. AI Post

After the AI Agent has processed the query, AI Post handles any final steps required before delivering the response to the user. This might include formatting the response, adding additional information, or logging the interaction for future reference. It logs or monitors the answers to ensure quality or for further analysis. This agent does not interact directly with customers but acts as a backend process to gather data on AI performance. An example Java class might look like this:

public class AiPostAgent {
    public void logResponse(String response) {
        // Logic to log or monitor responses
        System.out.println("Logging response: " + response);
    }
}

User Interface (UI):

Integration in Conversational Systems

In xBot, AI Bots function as part of a broader conversational group, a collective system where bots and AI Flows collaborate to manage the entire conversation lifecycle with users. This setup ensures that interactions are seamless, coherent, and context-aware, enabling more natural and effective communication. Within this structure, AI Bots are assigned to specific roles, performing checks, responding to messages, and handling post-processing tasks as conversations progress. This organized approach allows for efficient message handling, ensuring that each interaction is processed according to its content and the system's requirements. By utilizing these AI Bots, the system dynamically manages conversations, maintaining the quality of service and ensuring appropriate interactions throughout.

Group configuration

Setting Up AI Bots

Setting up AI Bots involves configuring each of the three components—AI Pre, AI Agent, and AI Post—along with integrating the bot into the appropriate conversational group.

Step 1: Creating an AI Bot

1. Access the AI Bots Section:

   • Navigate to the 'AI Bots' section in the xBot Admin Dashboard.

   • Click 'Create New Bot' to start setting up your bot.

2. Define Bot Parameters:

   • Bot Name: Choose a descriptive name for your AI Bot (e.g., "SupportBot").

   • Assign AI Flow: Select the AI Flow that this bot will use to process queries.

   • Assign AI Pre and AI Post: Configure any necessary preprocessing or post-processing tasks that this bot will handle.

3. Assign to a Conversational Group:

   • Add the AI Bot to the relevant conversational group to ensure it integrates smoothly with other bots and AI Flows.

Step 2: Configuring AI Pre, AI Agent, and AI Post

1. AI Pre Configuration:

   • Set Up Preprocessing Tasks: Configure tasks such as language detection, message tokenization, or data extraction that need to occur before the AI Agent processes the query.

2. AI Agent Configuration:

   • Decision Making and Execution: Define the core logic that the AI Agent will follow to interpret queries, execute tasks, and generate responses. This logic is typically defined within the AI Flow assigned to the bot.

3. AI Post Configuration:

   • Finalize Responses: Configure any post-processing tasks that prepare the response for delivery, such as formatting, appending additional information, or logging the interaction.

Step 3: Testing and Refining AI Bots

1. Run Test Interactions:

   • Use the testing tools in the Admin Dashboard to simulate interactions and ensure that each component of the AI Bot is functioning correctly.

2. Monitor Performance:

   • Track the performance of the AI Bot across its AI Pre, AI Agent, and AI Post components. Adjust configurations as necessary based on performance data.

Best Practices for AI Bots

1. Utilize AI Pre and AI Post Effectively

• Optimize AI Pre: Ensure that preprocessing tasks are efficient and contribute to a smoother AI Agent processing experience. Proper preprocessing can significantly enhance the bot's overall performance.

• Streamline AI Post: Use AI Post to add value to the user interaction, whether through additional data, enhanced response formatting, or efficient logging mechanisms.

2. Align Bots within Conversational Groups

• Consistency Across Bots: Ensure that all AI Bots within a conversational group are aligned in terms of logic and behavior, providing a consistent user experience.

• Context-Awareness: Leverage the conversational group to maintain context across multiple interactions, allowing AI Bots to deliver more relevant and coherent responses.

3. Regular Updates and Continuous Monitoring

• Refine AI Logic: Continuously improve the logic within AI Agents to handle new types of queries and scenarios.

• Monitor and Adapt: Regularly monitor AI Bot performance and make adjustments to AI Pre, AI Agent, and AI Post configurations based on user interactions and feedback.

Conclusion

AI Bots are the backbone of xBot’s automation capabilities, driving user interactions from start to finish. By understanding and effectively configuring the AI Pre, AI Agent, and AI Post components, you can ensure that your AI Bots operate efficiently and provide a seamless user experience. Integrating these bots into conversational groups further enhances their ability to manage complex, multi-step interactions.

For more detailed guidance on configuring AI Flows and Dispatch Rules, please refer to the AI Flow Setup Guide and the Dispatch Rules Guide.