LLM Multi-Agent Orchestration for Regulatory Data Alignment
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Solution Overview
Problem
Traditional data governance and access control systems struggle with managing complex, distributed data ecosystems, leading to data breaches, underutilization, and subpar analysis outcomes due to insufficient scalability, manual metadata management, and keyword-based search inefficiencies, which fail to capture nuanced meanings and maintain data integrity.
Innovation Solution
A multi-agent system leveraging large language models (LLMs), quantum computing, and advanced machine learning for automated metadata management, dynamic data access control, and intelligent semantic search, using agents like MEGAN, SEPHYR, and GAMA to align reporting standards and ensure regulatory compliance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional keyword-based search methods are used, then system complexity is reduced, but search precision and ability to capture nuanced meanings deteriorates
Solution Approach 1:
The patent replaces traditional keyword-based mechanical search mechanisms with large language model-based semantic search. The LLM processes natural language queries and retrieves semantically relevant data, enabling the system to capture nuanced meanings and contextual relationships that keyword matching cannot achieve, thereby significantly improving search precision.
Solution Approach 2:
The patent introduces a large language model as an intermediary between the user's search query and the data storage system. The LLM acts as a mediator that translates semantic queries into effective search operations, enabling precise retrieval of relevant information without requiring complex search algorithms or structured query languages from the user.
2Manufacturing precision
If manual metadata management techniques are used, then system complexity is reduced, but data governance quality and completeness deteriorates
Solution Approach 1:
The patent implements automated metadata management where the system performs its own metadata extraction, classification, and governance tasks without requiring manual intervention. The large language models automatically process data, generate metadata, and maintain data quality standards, enabling high-precision data governance at scale while reducing operational complexity through automation.
Solution Approach 2:
The patent replaces manual metadata management processes with automated AI-based systems. Large language models automatically extract metadata from unstructured data, classify information according to governance policies, and maintain data quality, eliminating the need for manual curation while achieving superior data governance quality and completeness.
3Reliability
If conventional data access control systems are used, then system simplicity is maintained, but security and data integrity protection deteriorates
Solution Approach 1:
The patent replaces conventional rule-based access control systems with AI-powered security mechanisms. Large language models analyze user context, data sensitivity, and access patterns to dynamically determine authorization decisions, providing adaptive security that responds to evolving threats and maintains data integrity without requiring overly complex policy configuration.
Solution Approach 2:
The patent implements dynamic access control where permissions are not static but adapt based on real-time analysis of user behavior, data context, and security policies. The system continuously adjusts access decisions based on changing conditions, enhancing security and data integrity protection while managing complexity through automated adaptive decision-making rather than rigid manual policies.
4Adaptability or versatility
If distributed data ecosystems are expanded, then data availability and versatility improve, but data integrity maintenance and provenance tracking deteriorates
Solution Approach 1:
The patent replaces traditional provenance tracking mechanisms with AI-based verification systems. Large language models automatically verify data integrity, detect anomalies, and maintain trust across distributed ecosystems by analyzing data provenance and validating information authenticity, enabling high data availability while preserving integrity through intelligent verification rather than complex manual auditing.
Data Source
AI summary
In a described embodiment, a multi-agent system for processing information is provided including a data processing agent configured to ingest and normalize raw data inputs to produce standardized data and a standards integration agent configured to apply reporting standards into the standardized data thereby generating integrated reporting standards. The system further includes a performance alignment agent configured to align performance indicators based on the standardized data and the integrated reporting standards and an information synthesis agent configured to process narrative information from the standardized data and the integrated reporting standards. An orchestration framework configured to manage operations of the data processing agent, the standards integration agent, and the performance alignment agent to produce a regulatory repot compliant with regulatory requirements is further provided. The orchestration framework is further executable by a large language model.