Multi-Agent Task Collaboration with Dynamic Request Decomposition
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Solution Overview
Problem
Existing agent modules are limited to processing tasks within their specific domain and require multiple expert agents for task automation, necessitating pre-designed collaboration methodologies that are inflexible and unable to handle diverse user requests effectively.
Innovation Solution
A system and method enabling peer-to-peer natural language-based communication between agents to automatically select optimal collaborators for processing tasks that exceed individual agent capabilities, using large language models to generate and evaluate step-by-step plans for task decomposition and result generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple expert agents are implemented to process diverse user requests, then the system's capability to handle complex tasks is improved, but the device complexity increases
Solution Approach 1:
The system divides complex tasks into sub-tasks that can be handled by different expert agents. Each agent is specialized in specific domains, and the overall system coordinates these segmented capabilities to handle complex user requests that would be impossible for a single agent to process alone.
Solution Approach 2:
A task manager agent acts as an intermediary between users and multiple expert agents. The task manager receives user requests, decomposes them into sub-tasks, selects appropriate expert agents, coordinates their work, and aggregates results. This mediator approach manages system complexity by centralizing coordination logic rather than requiring direct connections between all agents.
2Extent of automation
If pre-designed collaboration methodologies are used for multi-agent cooperation, then the automation of specific tasks is improved, but the adaptability to handle diverse and unexpected requests deteriorates
Solution Approach 1:
The collaboration methodology is made dynamic rather than static. The task manager agent adapts its coordination strategy based on the specific user request, selecting and assigning agents flexibly rather than following a predetermined collaboration script. This allows the system to handle diverse and unexpected requests while maintaining automation.
Solution Approach 2:
The system changes operational parameters dynamically - different agents are selected, different collaboration patterns are established, and task decomposition varies based on the specific user request. This parameter flexibility allows the same automated framework to adapt to diverse requests without requiring pre-designed methodologies for each scenario.
Data Source
AI summary
A method for processing a task through collaboration between agents includes: receiving a user request by a first agent from a user terminal; inquiring detailed information on each of a plurality of agents when the first agent determines that it cannot process the user request by itself, and determining a recipient of a first task processing request, which requests to process at least some of a plurality of sub-tasks constituting a target task for generating a processing result for the user request, based on the detailed information, transmitting the first task processing request to a second agent determined as the recipient of the first task processing request; receiving first external processing result data from the second agent in response to the first task processing request; and generating final processing result data for the user request by using the first external processing result data.