Collaborative AI Agent Coordination for Shared App Data Protection
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Solution Overview
Problem
Traditional shared applications face challenges in managing interactions among multiple users, requiring advanced communication protocols and intelligent agents to ensure seamless collaboration and prevent conflicts, especially as complexity grows.
Innovation Solution
A collaborative AI agent system with coordinators that receive user requests, determine relevant information, consolidate data, and obfuscate sensitive information to prevent data leakage, while optimizing task performance through device, system, and local agents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional shared applications use basic communication protocols, then device complexity is low, but collaboration efficiency and conflict prevention deteriorate
Solution Approach 1:
The patent introduces coordinator AI agents as intermediary components that mediate between multiple user devices and the shared application. These coordinator agents handle complex communication protocols, message routing, and conflict resolution automatically, allowing the core application to remain relatively simple while achieving high collaboration efficiency through the intelligent mediation layer
Solution Approach 2:
The system employs AI agents that autonomously manage communication and coordination tasks without requiring complex manual configuration. The agents self-organize, detect conflicts, and resolve issues independently, reducing the need for overly complex communication protocols while maintaining high productivity in collaborative environments
2Productivity
If the system extracts more relevant information from within and outside the system, then task performance improves, but data leakage risk increases
Solution Approach 1:
The patent introduces an information filter as an intermediary component between data extraction and the rest of the system. This filter selectively processes and validates information extracted from internal and external sources, ensuring that only relevant and safe data is transmitted to AI agents for task execution, thereby preventing data leakage while maintaining high task performance
Solution Approach 2:
The system implements feedback mechanisms where the information filter continuously monitors and validates extracted data against predefined security criteria. This feedback loop allows the system to adjust extraction parameters in real-time, ensuring optimal information retrieval for task performance while automatically blocking potentially harmful data that could cause leakage
3Adaptability or versatility
If multiple AI agents collaborate to perform tasks, then functionality and versatility improve, but system complexity increases
Solution Approach 1:
The patent divides the AI system into specialized agent components, each responsible for specific functions such as information extraction, data filtering, coordination, and task execution. This segmentation allows the system to achieve high versatility and functionality through modular agents while keeping individual agent complexity low, making the overall architecture more manageable despite the increased number of components
Solution Approach 2:
The coordinator AI agents serve as universal multi-functional components that can work with various types of user devices, data sources, and task requirements. These universal agents handle diverse scenarios through standardized interfaces and protocols, enabling the system to achieve high adaptability and versatility without proportionally increasing architectural complexity
Data Source
AI summary
In an embodiment, the present invention discloses a method for collaborating one or more Artificial Intelligent (AI) agent systems with a plurality of coordinators to perform a task. The method includes receiving, by a system AI agent, a request from a user device for performing a task. The method includes determining, by the system AI agent, a coordinator amongst the plurality of coordinators configured to augment the request to be implemented with the request. The method includes extracting, by a support AI agent, relevant information associated with the request from within the system and outside the system. The method includes obfuscating, by a local AI agent, information associated with the system to prevent a data leakage, while the relevant information is being extracted. The method includes performing, by the system AI agent, the task associated with the request based on the relevant information. The system AI agent generates an output augmented with another coordinator amongst the plurality of coordinators.