Agent Onboarding by Context Similarity for New Feature Queries
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing large language models (LLMs) may not be configured to generate accurate responses to user queries pertaining to new features or services introduced by online resources, leading to inaccuracies in response generation.
Innovation Solution
A method for adding a new agent to a group of existing agents configured to respond to user queries, where the new agent is selectively added based on its description and context similarity, allowing the system to handle queries related to new features or services by decomposing queries into sub-queries and routing them to appropriate agents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system uses a fixed group of existing agents configured with predefined LLMs, then the system structure remains stable and simple, but the system cannot accurately respond to new features or services
Solution Approach 1:
The patent implements dynamic agent management by allowing the system to selectively add new agents to the agent group based on received descriptions and contexts. The computing system evaluates whether to add new agents dynamically rather than maintaining a static agent group, enabling the system to adapt its composition based on emerging needs for new features or services.
Solution Approach 2:
The patent decomposes user queries into multiple sub-queries and routes different sub-queries to different agents based on their specialized contexts. This segmentation allows the system to distribute query handling across multiple specialized agents rather than requiring a single generalist agent to handle all query types.
2Reliability
If the system decomposes queries into multiple sub-queries and routes them to different agents, then the response accuracy improves, but the system complexity increases
Solution Approach 1:
The patent introduces a routing mechanism as an intermediary component that receives user queries, decomposes them into sub-queries, and routes appropriate sub-queries to suitable agents. This intermediary layer manages the complexity of coordinating multiple agents while presenting a simplified interface to users.
Solution Approach 2:
The patent segments the query processing function into distinct components: query decomposition, routing logic, agent execution, and response aggregation. This functional segmentation makes the complex system more manageable and maintainable by separating concerns into independent modules.
3Adaptability or versatility
If the system continuously updates the agent group by adding new agents, then the adaptability to new features improves, but the time required to process queries increases
Solution Approach 1:
The patent performs query decomposition and agent selection in advance by analyzing the user query structure and identifying which agents should handle which sub-queries before actual processing begins. This preliminary routing decision reduces delays during query execution by avoiding dynamic agent selection during the processing phase.
Solution Approach 2:
The patent implements dynamic agent group updates where new agents are selectively added based on received descriptions and evaluated against existing agents. This dynamic composition allows the system to adapt its agent group size and composition based on actual workload and query patterns, optimizing processing efficiency.
Data Source
AI summary
An online resource is disclosed that can selectively add a new agent to a group of existing agents configured to generate responses to user queries. The online resource can compare a description of the new agent with one or more contexts associated with the new feature, and then add the new agent when the comparison indicates a minimum degree of similarity between the agent description and the one or more contexts associated with the new feature.