AI Agent Interaction Model for API-Light Task Negotiation
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Solution Overview
Problem
Current online computing systems lack effective mechanisms to engage with AI agents and leverage their interactions and knowledge base, hindering efficient decision-making and task execution.
Innovation Solution
Deploying a system AI agent configured to access a machine-learning language model, which interacts with user AI agents to execute tasks, negotiate agreements, and perform actions on behalf of the online system, utilizing an agent executor instance to trigger tool execution and generate messages based on model responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If AI agents are deployed to represent users and perform tasks in conjunction with the online system, then user interaction efficiency and decision-making capability are improved, but the system complexity increases due to the need for complex APIs and integration mechanisms
Solution Approach 1:
The patent introduces a standardized interaction protocol that acts as an intermediary between AI agents and the online system. This protocol simplifies the integration mechanism by providing a uniform interface for communication, reducing the need for complex custom APIs while enabling efficient AI agent deployment and operation.
2Device complexity
If the system engages with AI agents through standardized interactions, then the need for complex APIs is reduced, but the ability to handle nuanced human emotional responses during negotiations is compromised
Solution Approach 1:
The patent extracts and separates the negotiation handling functionality from the core interaction protocol. By isolating emotional response handling in a dedicated module, the system maintains standardized simple APIs while preserving the capability to manage nuanced human emotional responses through specialized processing logic.
3Productivity
If the system extracts and executes proposed agreements from AI agent interactions, then task execution efficiency is improved, but the risk of misinterpretation or execution errors increases
Solution Approach 1:
The patent implements a feedback mechanism that validates extracted agreements against predefined criteria and allows for correction before execution. This feedback loop ensures that proposed agreements are accurately interpreted and executed, maintaining high reliability while preserving task execution efficiency through automated processing.
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.