LLM Agent Routing for Accurate Low-Latency Query Handling
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Solution Overview
Problem
Existing large language models (LLMs) vary in their effectiveness for different types of user queries, leading to inefficiencies in response generation due to differing configurations and training processes, resulting in latency and reduced accuracy.
Innovation Solution
A method for pairing user queries with agents using large language models (LLMs) based on context analysis, where each agent is configured to handle specific contexts or groups of contexts, ensuring accurate and efficient response generation by routing sub-queries to the most suitable agent.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single large language model is used to handle all types of user queries, then the system structure is simple, but the response accuracy and effectiveness decrease for specific query types
Solution Approach 1:
The patent segments the monolithic LLM system into multiple specialized agents (e.g., navigation agent, product agent, support agent), each trained on specific query types. This segmentation allows each agent to excel at its specialized function while maintaining overall system manageability through a query routing mechanism that directs queries to appropriate agents.
Solution Approach 2:
The patent creates a universal agent selection system that can handle diverse query types through multiple specialized agents. The query routing mechanism provides multi-functionality by adapting to different query categories (navigation, product information, support) and directing them to the most suitable specialized agent, making the system universally applicable across various query types.
2Manufacturing precision
If multiple specialized large language models are used for different query types, then the response accuracy improves, but the system complexity and query routing overhead increase
Solution Approach 1:
The patent introduces an intermediary agent selection system that mediates between user queries and specialized agents. This intermediary analyzes the incoming query, determines the appropriate agent based on query characteristics, and routes the query accordingly. This intermediary layer manages the complexity of having multiple agents while maintaining high response accuracy through specialized processing.
Solution Approach 2:
The patent implements a dynamic agent selection mechanism that adapts to different query types in real-time. The system dynamically determines which agent to invoke based on the specific characteristics of each query, rather than using a static assignment. This dynamic approach optimizes the balance between utilizing specialized agents for accuracy and managing system complexity through flexible routing.
3Manufacturing precision
If multiple specialized large language models are used for different query types, then the response effectiveness for specific contexts improves, but the latency due to model selection and routing increases
Solution Approach 1:
The patent performs preliminary classification of queries by analyzing query characteristics and determining the appropriate agent before actual query processing begins. By pre-determining the routing destination based on query patterns and characteristics, the system avoids unnecessary processing delays and directly engages the most suitable agent, reducing overall latency while maintaining specialized response effectiveness.
4Productivity
If generic training data is used for all agents, then the training process is efficient and simple, but the context-specific response quality decreases
Solution Approach 1:
The patent applies local quality by training each agent with query-and-response data specific to its designated context (e.g., navigation agents trained on navigation queries, product agents on product-related queries). This localized training approach ensures that each agent develops specialized knowledge and response patterns tailored to its specific domain, significantly improving context-specific response quality while maintaining reasonable training efficiency through focused datasets.
Data Source
AI summary
An online resource receives a plurality of queries from a user, identifies a plurality of agents to which each query of the plurality of queries may be assigned, pairs each query with a corresponding agent of the plurality of agents based at least in part on a comparison of the respective query with agent descriptions associated with the plurality of agents, and transmits, via a communications interface, each query to its corresponding agent.