Multi-Agent Orchestration With Minimal Task Agent Selection
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Solution Overview
Problem
Current agentic systems are inefficient, expensive, and prone to human error due to the challenge of determining optimal components for specific use cases, leading to resource waste and time-consuming deployment processes.
Innovation Solution
A multi-agent generation and deployment system that automatically selects a minimal set of data plane agents based on a use case description, using a super agent to identify tasks and a task assigner agent to map tasks to appropriate endpoints, optimizing the configuration without unnecessary components and enabling automated deployment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual methods are used to determine components for agentic systems, then expertise and human control are maintained, but the process becomes expensive, time-consuming, and prone to human error
Solution Approach 1:
The system enables self-service by allowing the agentic system to automatically determine its own component requirements. The language model analyzes the use case description and autonomously identifies necessary components, eliminating the need for manual expert configuration and enabling automated deployment while maintaining accuracy.
2Adaptability or versatility
If a comprehensive set of components is included in agentic systems, then versatility and adaptability are improved, but resource waste and system complexity increase
Solution Approach 1:
The system extracts only the necessary components from the available component pool by using a language model to analyze the specific use case description and identify which components are actually needed. This selective extraction approach ensures versatility for different use cases while avoiding resource waste from including unnecessary components.
3Ease of manufacture
If manual configuration of agentic systems is performed, then control and customization are maintained, but the process becomes expensive and time-consuming
Solution Approach 1:
The system replaces the mechanical process of manual component selection and configuration with an automated language model-based approach. The language model processes use case descriptions and automatically determines component requirements, substituting human expert manual work with automated intelligent processing that is both faster and more cost-effective.
Data Source
AI summary
Techniques for automatically orchestrating, generating, and/or deploying a multi-agent are disclosed. A super agent ingests metadata describing a use case to identify workflows and tasks. A task assigner agent assigns the tasks to task agents. A task performer agent performs a basic task. A vertical agent performs a task using vertical resources of a system. A specialized task agent performs specialized tasks, such as those performed using a specialized language model. An orchestration agent arranges task agents into an orchestration according to a graph representation of a workflow. A compiler agent packages the orchestration into an executable. The executable is called to perform the workflow. An orchestration engine automatically selects task agents used to complete the workflow and compiles them into the executable. The system collects feedback based on the results of the executable and uses the feedback to optimize the super agent and/or the task assigner agent.