Multi-Agent Document Review Conflict Resolution Workflow
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Solution Overview
Problem
Existing document management systems lack efficient methods for generating multi-agent reports and resolving conflicts between agents, leading to increased computational burden, power consumption, data transmission, and memory usage.
Innovation Solution
A system that uses machine learning models to generate specialized agents with distinct personas, such as legal, financial, and technical agents, which collaborate to provide holistic analysis and resolve conflicts, while proactively merging or splitting agents based on vector embeddings similarity, and determining optimized workflows.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple specialized agents are generated to provide holistic analysis, then the accuracy and comprehensiveness of document analysis is improved, but the computational burden, power consumption, and system complexity increase
Solution Approach 1:
The system divides the document analysis function into multiple specialized agents, each responsible for specific aspects (legal, financial, technical, compliance). Each agent is generated independently with specific expertise, allowing parallel processing while maintaining comprehensive coverage. This segmentation enables the system to achieve high analysis accuracy through specialized perspectives without requiring a single overly complex monolithic system.
Solution Approach 2:
The multi-agent system framework provides universal functionality where each agent can operate independently yet contribute to a holistic analysis. The agents share common infrastructure for document processing, conflict resolution, and report generation, making the system multi-functional while maintaining manageable complexity through standardized interfaces and protocols.
2Adaptability or versatility
If multiple agents operate independently to provide specialized feedback, then the comprehensiveness of review is improved, but the time required for coordination and conflict resolution increases
Solution Approach 1:
The system implements feedback mechanisms where agents communicate their findings and conflicts to a coordinator, which then facilitates resolution. Agents receive updated information and adjust their analyses accordingly. This feedback loop enables comprehensive review while managing coordination time through structured communication protocols and priority-based processing.
Solution Approach 2:
The system performs preliminary actions by pre-defining agent roles, expertise areas, and communication protocols before document analysis begins. Conflict resolution strategies and coordination frameworks are established in advance, allowing agents to operate independently initially while reducing the time needed for subsequent coordination and conflict resolution.
3Measurement precision
If agents continuously monitor and update feedback information, then the accuracy of analysis is improved, but the computational resources and energy consumption increase
Solution Approach 1:
Instead of continuous monitoring, the system employs periodic action where agents update feedback information at scheduled intervals or when significant changes occur in the document analysis. The coordinator periodically aggregates updates and resolves conflicts. This periodic approach maintains high analysis accuracy through regular updates while significantly reducing computational resources and energy consumption compared to continuous operation.
Solution Approach 2:
The system dynamically adjusts operational parameters such as update frequency, agent activation levels, and computational intensity based on document complexity, urgency, and current workload. For routine documents, agents operate with lower computational intensity and longer update cycles. For complex or time-sensitive documents, the system increases computational resources and update frequency. This parameter adaptation maintains analysis accuracy while optimizing power consumption.
Data Source
AI summary
A system generates, based on a first agent for a first persona and content of an electronic document, first feedback information for the first persona. Based on a determination that the first feedback information for the first persona indicates a first value for an attribute of the electronic document that causes a conflict with second feedback information for a second persona and that a second value for the attribute of the electronic document has been selected to resolve the conflict, the system generates, based on the first agent for the first persona, the second value for the attribute, and the content of the electronic document, updated first feedback information for the first persona. The system generates, based on the updated first feedback information for the first persona, a multi-agent report for the electronic document and output the multi-agent report.