Document Workflow Generator for Multi-Agent Conflict Resolution
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Solution Overview
Problem
Existing document management systems lack efficient methods for generating multi-agent reports and workflows that reduce complexity, computational burden, and data usage by effectively managing and resolving conflicts among agents with different roles and expertise.
Innovation Solution
A document management system that utilizes machine learning models to generate specialized agents with distinct personas, such as legal, financial, and technical, which collaborate to provide holistic analysis, resolve conflicts, and determine optimized workflows based on user profiles and historical data, reducing duplicative agents and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple specialized agents are generated to provide holistic analysis, then analysis accuracy and relevance are improved, but system complexity and computational burden increase
Solution Approach 1:
The system divides the analysis function into multiple specialized agents, each representing a different persona (legal, financial, technical, etc.). Each agent independently analyzes specific aspects of the document, improving overall accuracy while maintaining manageable complexity through modular design. The workflow generator orchestrates these segmented agents coordinate their analysis.
Solution Approach 2:
The system generates agents dynamically based on the document type and analysis requirements. A single agent framework can serve multiple functions by adapting personas and workflows, reducing the need for separate dedicated systems for each analysis type while maintaining specialized capabilities.
2Reliability
If multiple agents collaborate to analyze documents, then comprehensive feedback is improved, but time required for analysis increases
Solution Approach 1:
The system pre-defines workflows and agent collaborations based on document types and analysis requirements. Common analysis patterns are prepared in advance, allowing agents to execute standardized collaboration sequences without real-time negotiation, reducing overall analysis time while maintaining comprehensive feedback.
Solution Approach 2:
Agents operate in parallel to perform their analysis simultaneously rather than sequentially, with the workflow generator coordinating continuous progress. This parallel execution maintains comprehensive multi-perspective feedback while significantly reducing total analysis time compared to sequential processing.
3Adaptability or versatility
If agents are dynamically generated based on profiles, then adaptability to different scenarios is improved, but computational resources required increase
Solution Approach 1:
The system generates only the necessary number and type of agents required for each specific analysis task rather than maintaining a full complement of agents always active. The workflow generator determines the minimal required agent configuration based on document type and analysis requirements, reducing computational resource consumption while maintaining adaptability.
Solution Approach 2:
The system dynamically adjusts agent parameters such as persona specialization, analysis depth, and collaboration intensity based on the specific document and analysis requirements. This parameter adaptation allows the system to optimize computational resource usage for each scenario while maintaining high adaptability to different analysis needs.
4Measurement precision
If conflicts between agents are resolved manually, then accuracy of resolved conflicts is improved, but productivity decreases
Solution Approach 1:
The workflow generator automatically resolves conflicts between agents by applying predefined resolution rules and prioritization logic. Agents autonomously negotiate and resolve discrepancies without requiring manual human intervention, maintaining high accuracy through systematic resolution while significantly improving processing speed and productivity.
Data Source
AI summary
A system determines definition information for an agent of a plurality of agents configured to perform one or more actions associated with an electronic document. The system determines, based on the definition information for the agent and user information for a user associated with the electronic document, an ordered list of agent assignments indicating a plurality of actions to be performed by the plurality of agents. Based on a determination that the ordered list of agent assignments indicates the agent, the system determines, based on the definition information for the agent, input data for the agent and cause the agent to generate output data based on the input data. The system generates an executed document based on the output data.