AI Agent Interaction Layer for API-Light Task Negotiation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current online computing systems lack effective mechanisms to engage with AI agents and leverage their interactions and knowledge base, limiting their ability to perform tasks efficiently and effectively.
Innovation Solution
Deploy system AI agents configured to access machine-learning language models, interact with user AI agents, and execute actions based on model responses, facilitating negotiations and decision-making processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional online computing systems are used to interact with users, then the system can process basic user requests, but it lacks the capability to effectively engage with AI agents and leverage their interactions and knowledge base
Solution Approach 1:
The patent introduces an AI agent interaction layer that serves as an intermediary between the traditional online computing system and AI agents. This layer includes components such as an AI agent detector, interaction manager, and knowledge base extractor that enable the system to communicate with and leverage AI agents without requiring complete system redesign. The intermediary layer handles AI agent-specific protocols and transforms AI agent interactions into formats compatible with existing system infrastructure.
Solution Approach 2:
The system is enhanced with multi-functional capabilities to handle both traditional user interactions and AI agent interactions through a unified architecture. The interaction manager can detect and route different types of interactions (human users vs. AI agents) through appropriate processing paths, while the knowledge base extraction mechanism serves multiple purposes including improving recommendation systems, enhancing customer service, and supporting decision-making processes across various system modules.
2Adaptability or versatility
If complex APIs are implemented to enable AI agent interactions, then the system can communicate with AI agents, but the complexity of the system increases significantly
Solution Approach 1:
The patent extracts and isolates the AI agent interaction logic into separate, dedicated components rather than embedding it throughout the system. The AI agent detector, interaction manager, and knowledge base extractor are implemented as independent modules that can be added to existing systems without requiring changes to core API structures. This extraction approach allows the system to gain AI agent capabilities while maintaining the simplicity of existing APIs for traditional users.
Solution Approach 2:
The AI agent interaction system is segmented into distinct functional components: detection module, communication module, knowledge extraction module, and execution module. Each segment handles specific aspects of AI agent interaction, allowing for independent development, testing, and maintenance. This segmentation reduces the complexity burden on any single component and enables gradual implementation across the system.
3Productivity
If human users directly interact with the online system for tasks like order management and negotiation, then the system can complete transactions, but human emotional responses may interfere with efficient decision-making
Solution Approach 1:
The system enables AI agents to autonomously perform tasks such as order management, negotiation, and decision-making without requiring human emotional involvement. The AI agent execution module can independently evaluate options, negotiate terms, and complete transactions based on predefined objectives and real-time data analysis. This self-service capability eliminates emotional interference while maintaining high productivity through automated, consistent decision-making processes.
Solution Approach 2:
The system performs preliminary analysis and preparation of negotiation parameters, options, and decision frameworks before human or AI agent interaction occurs. The knowledge base extraction and processing modules pre-process information, identify relevant factors, and prepare structured data for efficient evaluation. This preliminary action ensures that when decisions are made, whether by humans or AI agents, the process is accelerated and consistency is improved through standardized evaluation criteria.
Data Source
AI summary
An online system configures one or more system AI agent instances that interact with user AI agents and performs one or more tasks on behalf of the online system. Thus, responsive to detecting the presence of a user AI agent representing a particular user, the online system directs the session for the user to communicate and interact with a system AI agent.