Multi-Agent Document Review to Cut Negotiation Time
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Solution Overview
Problem
Existing document management systems face inefficiencies in managing electronic documents due to the complexity of multi-agent interactions, leading to increased computational burden, power consumption, data transmission, and memory usage, as well as prolonged negotiation times and unnecessary document versions.
Innovation Solution
A system that utilizes machine learning models to generate multi-agent reports by identifying and collaborating specialized personas, resolving conflicts, merging or splitting agents based on vector embeddings, and determining optimized workflows to improve efficiency and accuracy in document management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple specialized agents are used to analyze electronic documents, then analysis accuracy and comprehensiveness are improved, but computational burden and processing time increase
Solution Approach 1:
The system segments the document analysis task into multiple specialized agents, each responsible for specific aspects such as legal analysis, financial review, compliance checking, and technical validation. This segmentation allows each agent to focus on its expertise area, improving overall analysis accuracy while maintaining manageable computational complexity through modular design.
Solution Approach 2:
The system creates a universal multi-agent framework that can handle various types of electronic documents (agreements, contracts, proposals) through a common architecture. Each agent is designed with multi-functional capabilities to analyze different document types and clauses, reducing the need for separate specialized systems and thereby lowering overall computational burden.
2Measurement precision
If multiple specialized agents are used to analyze electronic documents, then analysis accuracy and comprehensiveness are improved, but power consumption increases
Solution Approach 1:
The system performs preliminary actions by pre-configuring specialized agents with their respective expertise and analysis protocols before document processing begins. Historical document data and organizational profiles are pre-loaded into agent memory, allowing agents to perform analyses with reduced real-time computational requirements and lower power consumption during actual document review.
3Measurement precision
If multiple specialized agents are used to analyze electronic documents, then analysis accuracy and comprehensiveness are improved, but data transmission and memory usage increase
Solution Approach 1:
The system extracts and isolates specific document portions relevant to each agent's expertise, such as extracting legal clauses for the legal agent, financial terms for the financial agent, and technical specifications for the technical agent. This extraction approach reduces the amount of data each agent needs to process and transmit, thereby lowering overall data transmission requirements while maintaining comprehensive analysis accuracy.
4Measurement precision
If multiple specialized agents are used to analyze electronic documents, then analysis accuracy and comprehensiveness are improved, but negotiation time increases
Solution Approach 1:
The system implements continuous parallel processing where multiple specialized agents analyze different aspects of the electronic document simultaneously rather than sequentially. The legal agent, financial agent, compliance agent, and technical agent all process their respective portions of the document at the same time, with results aggregated into a comprehensive multi-agent report, thereby maintaining high analysis accuracy while significantly reducing total negotiation time.
5Measurement precision
If multiple specialized agents are used to analyze electronic documents, then analysis accuracy and comprehensiveness are improved, but the number of document versions increases
Solution Approach 1:
The system merges the analysis results from all specialized agents into a single integrated multi-agent report that consolidates legal, financial, compliance, and technical feedback. This merging approach presents a unified set of recommendations and identified issues to users, avoiding the proliferation of separate document versions for each agent's analysis while maintaining comprehensive review coverage.
Data Source
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AI summary
A system determines first revision information based on a first plurality of changes and generates, based on the first revision information and content of an electronic document, first feedback information for the first persona. The system determines second revision information based on the second plurality of changes and generates, based on the second revision information and the content of the electronic document, second feedback information for the second persona. The system generates, based on the first feedback information for the first persona and the second feedback information for the second persona, a multi-agent report for the electronic document and outputs the multi-agent report.