Multi-agent AI System for Siloed Data Synthesis
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Solution Overview
Problem
Integrating and synthesizing data assets from siloed data repositories is challenging due to incompatible data formats, lack of standardized metadata, and inconsistent data quality, as well as differing data access protocols and maintenance peculiarities.
Innovation Solution
A system utilizing multiple AI neural network agents to generate an execution plan for accessing and synthesizing data assets across siloed repositories, by decomposing requests into sub-requests, assigning them to specialized agents, and synthesizing responses based on relationships between sub-requests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple AI neural network agents are used to access and synthesize data from siloed repositories, then productivity and response time are improved, but device complexity increases
Solution Approach 1:
The system divides the complex task of data synthesis into multiple specialized AI agents, each responsible for specific functions (retrieval, synthesis, formatting). This segmentation allows parallel processing of different data repositories simultaneously, improving productivity while managing complexity through functional decomposition.
Solution Approach 2:
The AI agents are designed with multi-functionality to handle various data formats, schemas, and repository types through standardized interfaces. The synthesis agent can process data from multiple specialized retrieval agents, creating a universal system that adapts to different data sources without requiring separate dedicated systems for each repository.
2Loss of information
If data assets from siloed repositories are integrated and synthesized, then completeness and quality of information are improved, but difficulty of operation increases due to incompatible formats and access protocols
Solution Approach 1:
The synthesis agent acts as an intermediary that receives data from multiple specialized retrieval agents, standardizes the information, and produces unified outputs. This intermediary layer abstracts away the complexity of incompatible formats and access protocols from the user, while still enabling comprehensive information integration from siloed repositories.
Solution Approach 2:
The system transforms data from various repositories by changing parameters such as data format, schema structure, and access protocols into a unified representation. The AI agents dynamically adjust retrieval parameters based on the specific characteristics of each data repository while maintaining consistent output standards.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating an execution plan for a request that involves accessing and synthesizing data assets from siloed data repositories. In one aspect, a method comprises receiving a first request, generating a request execution plan comprising (i) a sequence of second requests and (ii) a respective indicator of a relationship between a corresponding response for each second request and an overall response to the first request by processing the first request using a request planner artificial intelligence (AI) neural network agent, sequentially, assigning and providing each second request in the sequence of second requests to a particular retriever AI neural network agent in accordance with metadata corresponding with a particular data repository, obtaining the corresponding response to the second request, and generating the overall response to the first request using a synthesizer AI neural network agent.