LLM Chatbot Workflow Policies for Automated Ticket Triage
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Solution Overview
Problem
Conventional customer support systems face inefficiencies due to the reliance on human agents for labeling and routing tickets, leading to resource-intensive training, long response delays, and limited categorization of issues, which frustrates customers and increases labor costs.
Innovation Solution
Implementing an AI-augmented customer support system that utilizes machine learning models for automatic ticket categorization, triage, and response generation, including a Solve Module for generating template answers, a Triage Module for routing, and an Assist Module for agent assistance, leveraging supervised learning and natural language understanding to enhance efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human agents manually search and categorize tickets using institutional knowledge, then accurate ticket routing is achieved, but response time increases and labor costs rise
Solution Approach 1:
The system performs preliminary categorization and routing decisions using AI models before human agents intervene. The triage module automatically analyzes incoming tickets, determines appropriate categories, and routes them to the correct queues, eliminating the need for agents to manually search institutional knowledge for every ticket.
Solution Approach 2:
The ticketing system provides self-service capabilities through automated categorization and routing. The AI-powered triage module independently processes tickets without requiring human agent intervention for initial classification, allowing the system to serve itself in the categorization function.
2Reliability
If human experts manually label and route tickets, then accurate routing decisions are made, but labor costs and resource intensity increase
Solution Approach 1:
The system replaces the mechanical process of human expert analysis with AI-based automated triage. Machine learning models analyze ticket content, customer history, and issue patterns to make routing decisions, substituting human cognitive labor with computational processes that are both accurate and cost-effective.
Solution Approach 2:
The triage module acts as an intermediary between ticket submission and human agent assignment. This intermediate AI layer processes tickets, determines appropriate routing, and only escalates complex cases to human experts, reducing the overall burden on human resources while maintaining routing accuracy.
3Device complexity
If a small number of ticket categories are used, then system simplicity is maintained, but customer frustration increases due to limited categorization options
Solution Approach 1:
The system segments the categorization process into multiple hierarchical levels. Instead of using a single flat category structure, tickets are classified through layered categories (e.g., primary category, sub-category, specific issue type), allowing for fine-grained classification without increasing surface-level complexity for users.
Solution Approach 2:
The categorization system is dynamic and adaptive, automatically creating new categories or modifying existing ones based on emerging ticket patterns and customer feedback. This allows the system to handle diverse customer issues without requiring manual expansion of the category structure, maintaining simplicity while increasing capability.
Data Source
AI summary
An autonomous customer support Chatbot Agent utilizes a large language model to aid in implementing a workflow to solve a customer issue. A natural language workflow policy may be selected by an admin, along with tools such as API calls. The large language model determines the implementation details for the workflow based on the workflow policy and the selected tools.