Multi-Agent Query Routing with Dynamic Query Enhancement
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Solution Overview
Problem
Conversational AI systems often provide suboptimal or inaccurate responses due to the complexity and variability of user queries exceeding the processing capabilities of a single language model, necessitating improved adaptability and scalability.
Innovation Solution
A multi-digital agent architecture with a dynamic query enhancement microservice and a multi-agent arbiter that processes unrefined query data through embeddings, historical user dialogue data, and rephrasing to enhance query data, selecting the appropriate digital agent for accurate response generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single language model is used for query processing, then the system structure remains simple, but the response accuracy deteriorates due to the complexity and variability of user queries exceeding processing capabilities
Solution Approach 1:
The system segments the single language model into multiple specialized digital agents (e.g., search agent, synthesis agent, calculator agent, etc.), each capable of handling specific types of queries. The arbiter component divides incoming queries and routes them to appropriate agents, improving response accuracy while maintaining manageable system complexity through modular organization.
Solution Approach 2:
The arbiter serves as a universal component that handles multiple functions: receiving queries, determining query types, selecting appropriate digital agents, and managing the coordination between different agents. This multi-functional design improves system efficiency without proportionally increasing complexity.
2Reliability
If multiple digital agents are deployed to handle complex queries, then response accuracy improves, but computational resource usage and energy consumption increase
Solution Approach 1:
Instead of deploying all digital agents for every query, the arbiter selectively activates only the necessary agents based on query type analysis. For example, simple factual queries may only require a search agent, while complex analytical queries may involve multiple agents. This partial action approach maintains high response accuracy while significantly reducing energy consumption and computational resource usage.
Solution Approach 2:
The arbiter acts as an intermediary that intelligently mediates between incoming queries and the pool of digital agents. It analyzes queries and determines the minimal set of agents needed to handle each query effectively, preventing unnecessary activation of agents and thereby reducing overall system energy consumption while maintaining response quality.
3Adaptability or versatility
If multiple digital agents are used to process queries, then system adaptability improves, but the complexity of agent selection and coordination increases
Solution Approach 1:
The arbiter uses parameter-based query analysis (such as query type, complexity, and topic categories) to dynamically select appropriate digital agents. By changing the selection parameters based on query characteristics, the system achieves high adaptability to different query types while keeping the selection mechanism itself relatively simple and rule-based rather than requiring complex decision-making algorithms.
Data Source
AI summary
A system for query-enhanced digital agent selection within a multi-digital agent architecture is described. The system may include one or more processors, a graphical user interface module rendering an interactive agent querying interface for receiving unrefined query data, and a dynamic query enhancement microservice for transforming the unrefined query data to enhanced query data. The system may further include a multi-agent arbiter communicably interposed between the dynamic query enhancement microservice and a set of digital agents, the multi-agent arbiter for selecting a digital agent of the set to receive an agent selection control signal from the multi-agent arbiter and for forwarding the enhanced query data to the selected digital agent. The selected digital agent may generate a response to the unrefined query data based on a processing of the enhanced query data against data accessed within a computer data source to which the selected digital agent is specifically permissioned.