Central Brain and Tentacle AI Agents for Trade Compliance Automation

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Solution Overview

Problem

Existing distributed AI management systems face challenges in efficiently managing complex logistics and customs procedures in global trade, leading to high operational costs, compliance issues, and lack of transparency in international trade, which hinders vendors in optimizing costs and navigating regulatory complexities.

Innovation Solution

A centralized distributed agentic artificial intelligence system with a central brain agent and tentacle agents, including sales, workflow, and marketplace agents, that automates tasks such as data entry, compliance checks, and logistics management, utilizing large language models and reinforcement learning to optimize operations and reduce manual labor.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If manual labor is used for data entry, compliance checks, and logistics management in global trade, then operational flexibility and human judgment are maintained, but labor costs increase significantly and productivity decreases

Engineering Contradiction:

Improveoperational efficiencyVSAvoidautomation level

Core Design Contradiction:

ProductivityVSExtent of automation

Solution Approach 1:

The AI agent system performs compliance checks, data entry, and logistics management autonomously without requiring human intervention for routine tasks. The system self-updates its knowledge base and automatically navigates regulatory requirements, enabling self-service operation that eliminates manual labor while maintaining high productivity

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

Manual mechanical processes (human data entry, compliance verification, document processing) are replaced with an intelligent software system that uses large language models and reinforcement learning to perform these tasks digitally, achieving higher speed and accuracy without physical constraints

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Adaptability or versatility

If traditional centralized AI systems are used for managing global trade operations, then decision-making control is maintained, but system complexity increases and adaptability to local regulations decreases

Engineering Contradiction:

Improveadaptability to local regulationsVSAvoidsystem architecture complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system is divided into a central coordinator agent and multiple specialized tentacle agents, each responsible for specific functions (sales, workflow, marketplace). This segmentation allows each agent to specialize in particular regulatory domains while the central coordinator maintains overall control, reducing the complexity each component must handle individually

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system adds a hierarchical dimension to the architecture, with the central brain operating at one level and distributed tentacle agents operating at edge levels. This multi-dimensional structure enables simultaneous centralized coordination and distributed adaptability, managing complexity through hierarchical organization rather than flat monolithic design

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

3Productivity

If AI agents continuously learn and update policies through reinforcement learning, then operational optimization improves, but computational energy consumption increases

Engineering Contradiction:

Improveoperational optimizationVSAvoidcomputational energy consumption

Core Design Contradiction:

ProductivityVSUse of energy by moving object

Solution Approach 1:

The reinforcement learning policy optimization operates periodically rather than continuously, with the central coordinator agent receiving updates at scheduled intervals and distributing them to tentacle agents. This periodic update mechanism reduces computational energy consumption while maintaining operational optimization through regular policy improvements

Inventive Principle:

Principle #19Periodic action

Solution Approach 2:

Each tentacle agent maintains local knowledge and makes decisions based on local conditions, reducing the need for constant centralized computation. The system updates specific local policies where needed rather than globally retraining all agents, optimizing energy usage by applying computational resources only where improvements are necessary

Inventive Principle:

Principle #3Local quality

Data Source

PatentUS20260004101A1Centralized distributed agentic ai

Publication Date: 2026.01.01 ZHANG DUO

AI summary

A centralized distributed agentic artificial intelligence system has a central brain agent. The central brain agent is configured to act as a coordinator agent. The coordinator agent receives input from an operator. The coordinator agent has a coordinator agent large language model that updates knowledge to a knowledge graph. The coordinator agent receives historical data from the knowledge graph to a reinforcement learning policy optimization. The reinforcement learning policy optimization sends model optimizing policy to the coordinator agent large language model. Tentacle agents are configured to act as interface agents. The interface agents have a third-party system integration interface to a third-party system. The plurality of tentacle agents each have an interface agent large language model, local decision making model, and a goal setting and task delegation model. The interface agent large language model receives feedback from an interface agent reinforcement learning policy optimization.