Mediator Language Model Agents for Multi-System Task Delegation
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Solution Overview
Problem
Existing technologies lack an efficient and scalable way to integrate multiple systems and devices, such as smartphones, smart home devices, and smart vehicles, to handle complex user interactions and tasks through inter-agent cooperation using natural language processing.
Innovation Solution
Implementing a mediator and delegate agent system with a language model (LM) framework that allows agents to communicate using natural language, delegate tasks, and leverage external resources, utilizing prompts like ReAct and chain-of-thought processing to break down tasks into subtasks and manage agent interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple systems and devices are integrated to handle complex user interactions, then user interaction capabilities and task completion are enhanced, but system complexity and integration difficulty increase
Solution Approach 1:
The system segments complex tasks into smaller subtasks and assigns them to different agents with specific expertise. Each agent handles particular types of tasks independently, reducing the complexity any single component must manage while maintaining overall system versatility.
Solution Approach 2:
A mediator agent is introduced to coordinate communications and task assignments between multiple agents and external systems. This intermediary layer simplifies integration by providing standardized interfaces and handling coordination logic, reducing the complexity of direct multi-system integration.
2Ease of operation
If agents communicate using natural language processing, then ease of operation and user interaction improve, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary processing by pre-defining agent capabilities, communication protocols, and task decomposition rules. This preparation work is done beforehand, allowing agents to quickly process natural language requests without extensive real-time analysis, reducing processing time while maintaining ease of operation.
3Productivity
If tasks are broken down into subtasks for delegation, then task completion efficiency improves, but coordination overhead and system complexity increase
Solution Approach 1:
The mediator agent serves as a central coordination point that manages task decomposition and delegation. It receives complex tasks, breaks them into subtasks, assigns them to appropriate agents, and coordinates their execution. This centralized mediation simplifies coordination overhead compared to distributed peer-to-peer coordination while maintaining high task completion efficiency.
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.