AI Agent Query Decomposition and Ranking for Cross-Platform Execution
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Solution Overview
Problem
Conventional AI systems are siloed and lack interoperability, leading to inefficiencies, redundancies, and security vulnerabilities, and fail to effectively identify and execute tasks across multiple platforms.
Innovation Solution
A decentralized network protocol for AI agents that enables platform-agnostic query decomposition, agent ranking, and execution, using a core node to verify user agents and dynamically select worker agents based on similarity and query plan criteria, ensuring seamless interaction and tailored responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional AI systems operate in siloed platforms, then each platform can maintain its own technical requirements and functionality, but interoperability is lost and inefficiencies arise
Solution Approach 1:
The patent introduces a core node as an intermediary component that mediates between user agents and worker agents. The core node receives queries from user agents, decomposes them into sub-tasks, ranks appropriate worker agents, and coordinates execution. This intermediary layer enables different AI platforms to interoperate without requiring direct compatibility between each platform, resolving the siloed nature of conventional systems while maintaining individual platform functionality.
Solution Approach 2:
The patent segments the AI system into distinct functional components: user agents (which interface with users), worker agents (which execute tasks), and a core node (which coordinates between them). This segmentation allows each component to operate independently with its own technical requirements while maintaining overall system interoperability through standardized communication protocols.
2Adaptability or versatility
If multiple AI platforms are integrated, then functionality and interoperability improve, but system complexity increases
Solution Approach 1:
The core node is designed as a universal coordinator that can handle queries from any user agent and route them to appropriate worker agents across different platforms. It provides multi-functional capabilities including query decomposition, agent ranking, task assignment, and result aggregation, thereby simplifying the overall system architecture despite the integration of multiple AI platforms.
3Productivity
If AI agents execute tasks autonomously, then productivity increases, but verification of agent behavior and security becomes more difficult
Solution Approach 1:
The patent implements feedback mechanisms where the core node receives outputs from worker agents and can verify their correctness against expected results. The system also incorporates ranking mechanisms that evaluate agent performance and use this feedback to adjust future task assignments. This feedback loop enables the system to maintain high productivity while ensuring reliability through continuous verification and monitoring of agent behavior.
Data Source
AI summary
A method for verifying a user agent includes transmitting, by a core node, to a user agent, a predefined set of user requests. For each user request in the predefined set of user requests, the core node receives an embedding generated based on the user request, thereby receiving a plurality of embeddings based on the predefined set of user requests. The core node determines whether the plurality of generated embeddings satisfy a similarity criterion. The core node transmits, to the user agent, a task to generate a query plan and receives a query plan generated by the user agent for processing a user request identified in the task. The core node determines whether the query plan satisfies a query plan criterion, thereby producing query plan adequacy output. The core node determines whether to approve the user agent based on the similarity output and the query plan adequacy output.