Mediator-Delegate Language Model Agents for Task Decomposition
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Solution Overview
Problem
Existing natural language processing systems face challenges in efficiently coordinating interactions between multiple agents, particularly in dynamic environments where agents have varying knowledge, capabilities, and responsibilities, leading to inefficiencies in task decomposition and execution.
Innovation Solution
Implementing a framework for inter-agent cooperation using a mediator and delegate agent model, where agents communicate through a combination of structured and unstructured natural language messages, leveraging a ReAct prompt to decompose tasks into subtasks and utilize external resources effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple agents are used to handle complex tasks, then task capabilities and knowledge coverage are improved, but coordination complexity and communication overhead increase
Solution Approach 1:
The patent implements a mediator agent that serves as an intermediary between multiple delegate agents. The mediator receives task requests, decomposes them into subtasks, assigns them to appropriate delegate agents based on their capabilities, and coordinates their interactions. This mediator structure resolves the coordination complexity by centralizing the communication hub, allowing agents to interact through a standardized interface rather than requiring direct peer-to-peer coordination among all agents.
Solution Approach 2:
The patent segments complex tasks into smaller subtasks that can be handled by individual delegate agents. The mediator agent divides the overall task into manageable components, assigns each subtask to the most suitable delegate based on capability matching, and then integrates the results. This segmentation reduces the cognitive load on each agent and simplifies the coordination required, as each delegate focuses on a specific subtask rather than the entire complex task.
2Productivity
If tasks are decomposed into subtasks for better management, then task execution efficiency is improved, but communication overhead and coordination time increase
Solution Approach 1:
The patent implements preliminary action by having the mediator agent pre-establish capability profiles and task templates for each delegate agent. When a task arrives, the mediator can quickly match subtasks to delegates using pre-analyzed capability information rather than performing complex analysis in real-time. Task templates predefine common subtask structures and communication protocols, reducing the time needed for coordination and enabling faster task decomposition and assignment.
3Measurement precision
If agents have specialized knowledge and capabilities, then task performance quality is improved, but system adaptability to new task types decreases
Solution Approach 1:
The patent implements universality through the mediator agent, which serves multiple functions: task decomposition, capability matching, subtask assignment, result integration, and error handling. This single mediator component handles diverse task types and coordination scenarios, providing system-wide adaptability. Meanwhile, individual delegate agents maintain specialized knowledge for their specific domains, achieving both specialization and versatility through this multi-functional coordination layer.
Data Source
AI summary
A system may be configured for cooperation between language model agents. An agent may be, for example, a computer system, or a software component executing on a computer system, that can accept text and/or natural language inputs, draw upon an LM to process the inputs and perform a function, and respond via text and/or natural language outputs. An agent may act as a mediator to interact with a user, identify a task requested by the user, and delegate one or more subtasks to another agent or other resource. An agent may act as a delegate to handle tasks or subtasks delegated by a mediator. Agents may communicate with each other using a combination of structured and unstructured language; for example, one or more parameters and a natural language message.