Automated Agent Federation Routing for Technical Support
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Solution Overview
Problem
Existing systems for providing technical support, such as automated software agents, struggle to efficiently route tickets to the most appropriate agents and measure the quality of suggestions in real-time, especially when human expertise is not immediately available.
Innovation Solution
A method for controlling a federation of automated agents that involves receiving a ticket, routing it to multiple agents, measuring the similarity and quality of their suggestions, and providing a subset of suggestions based on agent ratings and similarity, while also updating agent ratings based on user feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a ticket is routed to multiple automated agents for suggestions, then the quality and reliability of suggestions improve through comparison, but the system complexity and processing time increase
Solution Approach 1:
The system segments the ticket handling process by routing to multiple specialized automated agents simultaneously, each generating suggestions independently. This segmentation allows parallel processing while maintaining system manageability through modular agent design.
Solution Approach 2:
The system merges suggestions from multiple automated agents through a consolidation process that combines individual agent responses into a unified set of recommendations, achieving improved reliability through aggregation while managing complexity through systematic integration.
2Adaptability or versatility
If automated agents continuously update their ratings based on user feedback, then the adaptability and quality of future suggestions improve, but the computational overhead and processing time increase
Solution Approach 1:
The system implements continuous feedback loops where user responses to suggestions are automatically captured and used to update agent ratings. This feedback mechanism enables agents to adapt and improve their performance over time without requiring manual reconfiguration.
Solution Approach 2:
The system performs preliminary rating updates in the background during low-traffic periods, preparing updated agent ratings in advance before they are needed for ticket routing, thereby minimizing the impact on real-time processing performance.
3Manufacturing precision
If the system measures similarity of suggestions from multiple agents, then the quality control and consistency of responses improve, but the measurement complexity and computational resources increase
Solution Approach 1:
The system replaces complex manual analysis of suggestion similarity with automated text comparison algorithms that use computational methods to measure semantic similarity, linguistic patterns, and structural characteristics of suggestions from different agents.
Solution Approach 2:
The system transforms the abstract concept of suggestion similarity into measurable parameters such as text overlap metrics, semantic distance scores, and structural similarity indices, enabling automated comparison and quality control through quantitative analysis.
4Reliability
If human expertise is used for ticket resolution, then the quality and accuracy of solutions improve, but the availability and response time decrease when human experts are not immediately available
Solution Approach 1:
The system creates automated copies of human expert knowledge through trained automated agents that replicate expert problem-solving patterns and decision-making processes, enabling 24/7 availability while maintaining solution quality comparable to human experts.
Solution Approach 2:
The system enables self-service ticket resolution by providing users with automated suggestions and solutions that can be immediately applied without requiring human expert intervention, thereby reducing wait time while maintaining solution effectiveness through intelligent automation.
Data Source
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AI summary
A method of controlling a federation of automated agents includes receiving a ticket from a terminal, routing the ticket to a number of in-service automated agents among the plurality of in-service automated agents, measuring a similarity of suggestions generated by the number of in-service automated agents among the plurality of in-service automated agents, the suggestions being suggestions for responding to the ticket, determining a subset of the suggestions based on a rating of the number of in-service automated agents and the similarity of suggestions generated by the number of in-service automated agents, the rating being based on a previous score indicative of a quality of a previous subset of suggestions, and providing the subset of suggestions to the terminal.