Multi-Agent Request Processing With Self-Selecting Expert Agents
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Solution Overview
Problem
Existing multi-agent collaboration methodologies struggle to generate processing results for user requests that exceed the capabilities of individual agent modules, leading to limitations in handling tasks across multiple domains and requiring pre-designed collaboration methods.
Innovation Solution
A system and method for multi-agent collaboration that allows agents to self-determine collaboration methods and select optimal agents through natural language-based peer-to-peer communication, enabling seamless task processing across different domains by leveraging detailed agent information and external processing results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple expert agents are implemented to handle specialized tasks, then task processing capability is improved, but device complexity increases
Solution Approach 1:
The system segments task processing into specialized expert agents, each responsible for specific domains. The agent execution device divides the overall task into sub-tasks and distributes them to appropriate expert agents based on their specialized capabilities, thereby improving task processing capability while managing system complexity through structured division of labor.
Solution Approach 2:
The agent execution device serves as a universal coordinator that can manage multiple different expert agents across various domains. It provides a unified interface for receiving user queries, determining required capabilities, selecting appropriate agents, and integrating their outputs, thus handling diverse tasks without requiring separate dedicated systems for each domain.
2Reliability
If pre-designed collaboration methods are used for multi-agent operation, then collaboration reliability is improved, but adaptability to new tasks deteriorates
Solution Approach 1:
The system employs dynamic agent selection and task distribution mechanisms rather than fixed pre-designed collaboration methods. The agent execution device determines in real-time which expert agents to invoke based on the specific requirements of each user query, allowing the system to adapt to new and varying tasks while maintaining reliable collaboration through structured decision-making processes.
Solution Approach 2:
The expert agents operate autonomously to determine their own applicability to given tasks. Each agent can self-evaluate whether it possesses the necessary capabilities to handle a specific sub-task, reducing the need for complex pre-programmed collaboration logic while maintaining reliable task completion through self-directed agent participation.
3Productivity
If expert agents operate autonomously, then operation efficiency is improved, but coordination difficulty increases
Solution Approach 1:
The agent execution device acts as an intermediary coordinator between user queries and expert agents. It receives user input, analyzes task requirements, selects appropriate expert agents, manages their execution, and integrates their outputs into a unified response. This intermediary structure enables autonomous agent operation while simplifying coordination through a centralized management layer.
Solution Approach 2:
The system implements feedback mechanisms where expert agents report their execution results and capability assessments back to the agent execution device. This feedback loop enables the coordinator to make informed decisions about task distribution, agent selection, and result integration, thereby maintaining efficient autonomous operation while managing coordination complexity through continuous information exchange.
Data Source
AI summary
Provided is a computing system for processing a user's request based on multi-agent collaboration. The computing system comprises an agent information storage device storing detailed information on each of a plurality of agents, the detailed information including information on a responsible field of each agent and an agent execution system executing a first agent responsible for processing a user request introduced through a first channel and a second agent responsible for processing a user request introduced through a second channel different from the first channel.