Persona-Based Agent Splitting for Document Workflow Conflicts
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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, and data transmission, while relying on manual or predefined rules for agent selection and workflow generation.
Innovation Solution
A document management system that uses machine learning models to generate specialized agents with distinct personas, proactively splits or merges agents based on vector embeddings, resolves conflicts, and determines customized workflows to improve efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual or predefined rules are used for agent selection and workflow generation, then system complexity is reduced, but productivity and accuracy of document management deteriorates
Solution Approach 1:
The system automatically generates agents and workflows based on document content and requirements, eliminating manual configuration. The agent selection and workflow generation processes are autonomous, with the system self-adjusting to different document types and negotiation scenarios, thereby improving productivity without requiring complex manual setup.
Solution Approach 2:
The system dynamically adjusts agent parameters and workflow configurations based on document characteristics, negotiation history, and real-time analysis. By automatically modifying system parameters according to contextual data, the system achieves high adaptability and efficiency while maintaining manageable complexity through automated parameter tuning.
2Measurement precision
If multiple specialized agents are generated for different personas, then analysis accuracy is improved, but computational burden and power consumption increase
Solution Approach 1:
The system divides the analysis task into specialized agent segments, each responsible for specific personas (legal, financial, technical, etc.). This segmentation allows parallel processing of different analysis dimensions, improving overall accuracy while managing computational load through distributed task execution rather than monolithic processing.
Solution Approach 2:
The system generates and activates only the necessary number of agents based on document complexity and negotiation requirements. Instead of always deploying the full agent ensemble, the system applies partial action by selecting and activating only relevant agents for each specific analysis task, thereby reducing unnecessary computational burden while maintaining high accuracy for the required analysis.
3Measurement precision
If agents actively communicate and resolve conflicts, then output accuracy is improved, but data transmission and processing requirements increase
Solution Approach 1:
The system implements feedback mechanisms where agents exchange information about their findings and conflicts, and where the system learns from negotiation outcomes to improve future analyses. This feedback loop enhances output accuracy by allowing agents to refine their conclusions based on peer input and historical data, while the feedback is efficiently processed through targeted data exchange rather than exhaustive data transmission.
Data Source
AI summary
A system generates a first agent for the first persona based on both a first plurality of changes and first source data. The system determines a first plurality of vector embeddings for the first agent based on the first source data and determines a second plurality of vector embeddings based on second source data. The system determines a similarity value based on the first plurality of vector embeddings and the second plurality of vector embeddings. Based on the similarity value, the system determines that the first source data is associated with a second persona different from the first persona. The system generates a second agent for the second persona based on the first source data.