Configuring Dispatch Rules in xBot
Dispatch Rules in xBot are essential for managing how user queries are routed within the system. These rules determine whether a query should be handled by a human Agent or an AI Agent (AI Bot), based on predefined criteria such as keywords, user profiles, or specific query types. xBot’s flexible architecture allows for seamless switching between human Agents and AI Agents, ensuring that each query is handled by the most appropriate entity, thereby maximizing productivity and efficiency.
This guide will walk you through the key concepts, setup process, and best practices for configuring Dispatch Rules in xBot.
Understanding Dispatch Rules
1. What Are Dispatch Rules?
Dispatch Rules define the logic that xBot uses to determine where a user query should be directed. These rules ensure that each query is handled by either a human Agent or an AI Agent (AI Bot), optimizing the efficiency and accuracy of your system's responses.
Key Concepts:
Agent: A human operator who can handle complex queries, provide personalized support, and make judgment calls that require human intervention.
AI Agent (AI Bot): An automated virtual agent that processes and responds to user queries based on predefined AI Flows. AI Agents excel at handling routine, high-volume queries, allowing human Agents to focus on more complex tasks.
AI Agent Allocation Rules: Specific rules that determine how queries are assigned to AI Agents. These rules can be customized to meet the unique needs of your organization, enabling dynamic allocation based on various factors.
Setting Up Dispatch Rules
2. Creating and Configuring Dispatch Rules
Creating Dispatch Rules in xBot involves defining the criteria that determine how queries are routed to either an Agent or an AI Agent. Here’s how to set them up:
Step 1: Accessing the Dispatch Rules Section
Navigate to Dispatch Rules:
In the xBot Admin Dashboard, go to the 'Dispatch Rules' section.
Click 'Create New Rule' to begin setting up your dispatch logic.
Step 2: Defining Routing Criteria
Set Up Conditions:
Keyword Matching: Define specific keywords or phrases that, when detected in a query, trigger routing to an AI Agent or escalate it to a human Agent.
User Profile: Use user-specific data, such as account type or location, to determine whether a query should be handled by a human Agent or an AI Agent.
Message Type: Route queries based on the type of message (e.g., routine inquiries can go to AI Agents, while complex issues are directed to human Agents).
AI Agent Allocation Rules:
Simple Configuration: Use built-in options to define basic routing logic, such as matching keywords to predefined responses by AI Agents.
Advanced Configuration: Implement custom routing logic using Java Beans or Groovy Scripts for more complex scenarios, allowing for dynamic allocation between human Agents and AI Agents.
Step 3: Implementing Custom Logic with Java Beans and Groovy Scripts
Java Beans:
Usage: Use Java Beans to implement complex business logic in your dispatch rules. This allows for highly customized query handling, such as determining when to involve a human Agent versus an AI Agent based on the complexity of the query.
Example: Configures the operational hours of the AI agent throughout the week.
During AI agent's hours: When a message arrives, if linked to a group and this rule, the AI agent is assigned to handle and respond to the conversation.
Outside AI agent's hours: The rule randomly assigns a human agent from the group to manage the conversation, and the AI agent does not respond automatically.
Groovy Scripts:
Usage: Groovy Scripts offer a flexible way to write custom dispatch logic, enabling dynamic query handling that can switch between human and AI Agents based on real-time data.
Example: All incoming messages that are linked to a group and this specific rule will be reassigned to an AI agent for handling.
3. Seamless Integration and Quick Switching
Dynamic Switching Between Agents and AI Agents:
Seamless Integration: xBot allows for seamless collaboration between human Agents and AI Agents within the same conversational group. This means that if an AI Agent reaches the limit of its capabilities, the conversation can be seamlessly handed over to a human Agent without disrupting the user experience.
Quick Switching: Dispatch Rules can be configured to switch queries between AI Agents and human Agents based on real-time conditions, such as workload, query complexity, or user satisfaction metrics. This quick switching ensures that each query is handled by the most suitable resource, maximizing productivity and maintaining high service quality.
4. Testing and Refining Dispatch Rules
Test Rule Effectiveness:
Use the testing tools in the Admin Dashboard to simulate queries and observe how they are routed based on your dispatch rules.
Ensure that queries are consistently directed to the correct AI Flow, AI Agent, or human Agent.
Monitor and Adjust:
Continuously monitor the performance of your dispatch rules and make adjustments as needed to improve accuracy and efficiency.
Best Practices for Dispatch Rules
5. Leveraging AI Agent Allocation Rules
Specific Allocation: Use AI Agent Allocation Rules to ensure that routine queries are handled by AI Agents, freeing up human Agents to focus on more complex interactions.
Dynamic Allocation: Consider using Groovy Scripts for real-time allocation, adjusting based on factors like agent availability, query complexity, or time of day.
6. Regularly Review and Update Rules
Adapt to Changing Needs: As your organization’s needs evolve, regularly review and update your dispatch rules to ensure they remain relevant and effective.
Incorporate Feedback: Use feedback from users and performance data to refine your dispatch criteria, improving the overall accuracy of query routing.
7. Prioritize Critical Queries
High-Priority Routing: Ensure that critical queries, such as those related to security or urgent support requests, are routed to the most capable AI Agent or directly to a human Agent.
Conclusion
Dispatch Rules are a powerful tool within xBot, enabling precise control over how user queries are managed. By carefully configuring and regularly refining these rules—particularly through the effective use of AI Agent Allocation Rules, custom logic via Java Beans or Groovy Scripts, and the dynamic switching between human Agents and AI Agents—you can ensure that each query is handled efficiently and accurately. This approach maximizes productivity, enhances user satisfaction, and maintains high operational efficiency.
For further assistance or advanced configuration tips, please refer to the AI Flow Setup Guide or contact your xBot support team.
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