Multi-Agent Document Review With Persona Feedback Merging
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Solution Overview
Problem
Existing electronic document management systems lack efficient methods for generating multi-agent reports that provide holistic analysis and reduce conflicts among agents, leading to increased computational burden, power consumption, and data transmission.
Innovation Solution
A system that utilizes machine learning models to generate specialized agents with distinct personas, such as legal, financial, and technical, which collaborate to provide feedback, resolve conflicts, and optimize workflows, reducing duplicative agents and improving accuracy through proactive merging and splitting based on vector embeddings and user profiles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple specialized agents are generated to provide holistic analysis of electronic documents, then the accuracy and comprehensiveness of document review is improved, but the system complexity and computational burden increase
Solution Approach 1:
The system divides the document review function into multiple specialized agents, each with distinct personas (legal, financial, technical, compliance) that independently analyze specific aspects of the document. This segmentation allows each agent to focus on specific criteria, improving overall accuracy while maintaining manageable complexity through specialization.
Solution Approach 2:
Each agent is designed with multi-functionality to handle various document types and review scenarios. The agents can adapt to different electronic documents (contracts, agreements, proposals) and provide holistic analysis across multiple dimensions, reducing the need for separate specialized systems for each function.
2Speed
If agents independently generate feedback without collaboration, then the speed of document review is improved, but conflicts and inconsistencies in feedback increase
Solution Approach 1:
The system implements feedback mechanisms where agents communicate their findings and adjust their feedback based on interactions with other agents. This feedback loop ensures consistency and reduces conflicts while maintaining the speed advantage of parallel processing, as agents can quickly validate or refine their findings through targeted interactions.
Solution Approach 2:
The system merges the outputs of multiple agents into a unified review report that integrates feedback from all specialized agents. This merging process resolves conflicts and inconsistencies by synthesizing multiple perspectives into a coherent, consistent feedback structure that maintains the speed benefits of parallel review.
3Measurement precision
If the system generates detailed multi-agent reports with comprehensive feedback, then the quality of document analysis is improved, but the amount of data transmission and processing increases
Solution Approach 1:
The system applies local quality by tailoring the depth and detail of feedback to the specific requirements of each agent persona and the particular document being reviewed. Rather than uniformly detailed reports, each agent provides appropriately granular feedback focused on its specialized domain, reducing overall data volume while maintaining high quality where needed.
Solution Approach 2:
The system employs partial action by generating comprehensive detailed feedback only for critical issues and high-priority areas, while providing summarized feedback for less critical aspects. This selective detail approach maintains quality for important analysis while significantly reducing the overall data transmission and processing requirements.
4Reliability
If the system maintains separate agents for different personas, then the specialization and expertise of each agent is improved, but the number of duplicative agents increases
Solution Approach 1:
The system designs agents with universal capabilities that allow a single agent to perform multiple specialized functions. For example, a legal agent can handle contract review, compliance checking, and risk assessment by adapting its analysis criteria. This multi-functionality reduces the total number of agents needed while maintaining specialization accuracy through configurable expertise.
Solution Approach 2:
The system employs dynamic agent configuration where agents can adapt their specialized criteria and focus areas based on the specific document type and review requirements. This dynamic adjustment allows agents to specialize in real-time without requiring separate static agents for each possible scenario, reducing overall agent proliferation.
Data Source
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.