Data Query Routing With Agent Escalation for Deadline Compliance
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Solution Overview
Problem
Large enterprise organizations face challenges in efficiently managing and resolving structured data queries, particularly those with strict response deadlines, such as requests from regulatory bodies like the FDIC, due to the complexity and time sensitivity of these queries.
Innovation Solution
A system and method utilizing a computing system with a processor and non-transitory storage, enabling a resolution agent to select and communicate with operations agents through an email system, employing a machine learning algorithm to prioritize agents based on query details and deadlines, and automatically rerouting queries if initial agents cannot meet response times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual query routing procedures are used, then flexibility in agent selection is maintained, but query resolution time increases and deadline compliance deteriorates
Solution Approach 1:
The system performs preliminary actions by pre-storing agent contact information, responsibilities, and availability status in a database before queries arrive. When a query comes in, the system immediately retrieves relevant agent data and automatically routes the query without manual intervention, significantly reducing resolution time while maintaining appropriate system complexity through structured data organization.
Solution Approach 2:
The query routing system operates autonomously by automatically selecting appropriate agents based on stored criteria, sending queries via email, and monitoring for acknowledgments without requiring manual routing decisions. This self-service capability accelerates query resolution while the automated nature of the process keeps system complexity manageable through standardized algorithms.
2Loss of time
If automated query routing is implemented, then query resolution time decreases, but system complexity increases
Solution Approach 1:
The system implements feedback mechanisms by monitoring whether sent queries receive acknowledgments from agents within expected timeframes. If no acknowledgment is received or the agent indicates inability to respond by the due date, the system automatically identifies alternative agents and re-routes the query. This feedback loop reduces query response time while the systematic approach to handling failures keeps complexity manageable.
Solution Approach 2:
The system changes operational parameters by dynamically selecting different agents based on real-time conditions such as agent availability, query urgency, and response deadlines. The automated system adjusts routing decisions based on stored agent performance data and current workload conditions, reducing response time through optimized parameter selection without requiring overly complex decision-making algorithms.
3Adaptability or versatility
If multiple agents are available for query handling, then query resolution flexibility improves, but coordination complexity increases
Solution Approach 1:
The system segments the agent pool into distinct categories based on stored responsibility assignments and expertise areas. When a query arrives, the system divides the problem by identifying which agent segments are most appropriate based on the query type and stored agent specializations. This segmentation approach maintains flexibility in agent selection while reducing coordination complexity by pre-organizing agents into logical groups with defined roles.
Solution Approach 2:
The automated routing system provides universal functionality by handling multiple query types and agent selection scenarios through a single unified process. The system can route queries to different agents based on various criteria (expertise, availability, workload) while using the same automated framework, maintaining adaptability without increasing coordination complexity through standardized multi-functional handling.
Data Source
AI summary
A system and method for data base management and resolution of data queries includes a computing system communicating with a resolution agent and multiple operations agents and executing an application causing a processor to perform the method steps: storing data associated with the operations agents including contact information and responsibilities information; when a data query is received from an external source, the resolution agent accesses the stored data and selects a first of the operations agents to respond; the resolution agent creates a message and sends the message to the first operations agent; when no acknowledgement of the message is received within a first predetermined time period, the message is resent; and when no acknowledgement is received within a second predetermined time period after the message was resent, the resolution agent accesses the stored data to select a second operations agent and sends the message to the second operations agent.