Semantic Orchestrator Routing for Generative Model Agents
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Solution Overview
Problem
Existing generative models and natural language processing technologies struggle to handle specialized tasks and retrieve specialized information, often requiring users to switch between multiple platforms and interfaces, leading to inefficiencies and a lack of domain-specific expertise.
Innovation Solution
A computing system is developed that includes processing circuitry configured to execute multiple agents, each specializing in a domain, and an orchestrator that uses semantic decision-based routing to direct user requests to the appropriate agents, integrating with a trained generative model to generate responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single generative model is used to handle all types of queries, then the system is simple to operate, but it lacks domain-specific expertise and cannot perform specialized tasks effectively
Solution Approach 1:
The system segments the single generative model into multiple specialized agents, each trained on specific domains (e.g., healthcare, law, finance). The orchestrator divides incoming queries and routes them to appropriate agents, enabling domain-specific expertise while maintaining a unified system architecture.
Solution Approach 2:
The orchestrator acts as an intermediary between the user and multiple specialized agents. It receives queries, determines the appropriate domain, routes to the relevant agent, and aggregates responses, thereby managing complexity while preserving versatility.
2Adaptability or versatility
If multiple specialized platforms are used for different tasks, then domain-specific expertise is improved, but users must switch between interfaces which reduces ease of operation
Solution Approach 1:
The system provides a universal interface that can handle multiple specialized tasks through a single chat window. The orchestrator enables one interface to communicate with multiple specialized agents, eliminating the need for users to switch between different platforms while maintaining access to domain-specific expertise.
3Productivity
If multiple specialized agents are introduced to handle different domains, then task handling capability is improved, but system complexity increases
Solution Approach 1:
The orchestrator serves as a central intermediary that manages communications between users and multiple specialized agents. It handles query routing, response aggregation, and coordination, thereby managing the complexity introduced by multiple agents while maintaining high task handling capability.
Solution Approach 2:
The system merges multiple specialized agents into a unified framework managed by the orchestrator. Rather than requiring separate systems for each domain, the orchestrator combines their capabilities into a single coordinated system that handles diverse tasks efficiently.
Data Source
AI summary
A system is provided for managing specialized tasks and information retrieval processes. Agents are configured to perform tasks and/or retrieve information in a specialized domain. The system receives, via an interaction interface, a message from a user for the trained generative model to generate an output, generates a context of the message, generates a request including the context and the message, executes an orchestrator configured to: receive the request, determine, using semantic decision making, one or more agents to handle the request, input the request into one or more agents to perform a task and/or retrieve information in specialized domains, generate a prompt based on the retrieved information and/or the performed task and the message from the user, provide the prompt to the trained generative model, receive, in response to the prompt, a response from the trained generative model, and output the response to the user.