Distributed AI Agent Resource Allocation With Ledger-Based Validation

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Solution Overview

Problem

Distributed AI agent networks face challenges due to conflicts between individual agent objective functions and collective network goals, leading to suboptimal resource allocation, network congestion, and lack of visibility into upstream agents, which conventional methods fail to address effectively.

Innovation Solution

A data monitoring platform using a distributed or federated ledger-based agent knowledge registry to register, monitor, and authenticate AI agents, dynamically allocate resources, and manage protected digital content, while detecting anomalous behavior and ensuring compliance across a multi-tiered network.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If AI agents operate autonomously with individual objective functions, then agent independence and operational flexibility are improved, but alignment with collective network goals deteriorates

Engineering Contradiction:

Improveagent independenceVSAvoidalignment with collective goals

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The system implements feedback mechanisms where agents receive rewards or penalties based on their alignment with collective network goals. The feedback loop continuously monitors agent behavior and adjusts individual objective functions to better align with network-wide objectives, resolving the contradiction between independence and collective alignment.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system dynamically changes parameters of agent objective functions based on network conditions and collective goals. By adjusting weights, priorities, and constraints in agent objectives, the system maintains agent independence while ensuring alignment with collective goals through parameter optimization.

Inventive Principle:

Principle #35Parameter changes

2Adaptability or versatility

If resources are allocated without centralized coordination, then system scalability is improved, but resource allocation efficiency deteriorates

Engineering Contradiction:

Improvesystem scalabilityVSAvoidresource allocation efficiency

Core Design Contradiction:

Adaptability or versatilityVSProductivity

Solution Approach 1:

Agents autonomously manage their own resource allocation based on local conditions and objectives. Each agent self-regulates resource consumption and allocation decisions, eliminating the need for centralized coordination while maintaining efficient resource utilization through decentralized self-service mechanisms.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system employs dynamic resource allocation where resource distribution adapts in real-time based on changing network conditions, agent priorities, and availability. This dynamic approach enables scalable systems that automatically optimize resource allocation efficiency without centralized control.

Inventive Principle:

Principle #15Dynamics

3Device complexity

If agents operate without visibility into upstream agents, then system complexity is reduced, but predictability of behavior deteriorates

Engineering Contradiction:

Improvesystem complexityVSAvoidbehavior predictability

Core Design Contradiction:

Device complexityVSReliability

Solution Approach 1:

The system introduces an intermediary layer (such as a marketplace or coordination protocol) that facilitates communication between agents without requiring direct visibility into upstream agents. This intermediary maintains system simplicity while enabling agents to predict and coordinate their behaviors through standardized interaction interfaces.

Inventive Principle:

Principle #24Intermediary (Mediator)

4Reliability

If monitoring and verification of agents is implemented, then compliance and reliability are improved, but system overhead and complexity increase

Engineering Contradiction:

Improvecompliance monitoringVSAvoidsystem overhead

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system creates simplified representations or copies of agent states, behaviors, and compliance status that can be monitored without direct observation of complex agent operations. These copies or abstractions enable compliance monitoring while reducing the overhead of directly tracking full agent states and behaviors.

Inventive Principle:

Principle #26Copying

Data Source

PatentUS12621253B2Allocating resources among autonomous artificial intelligence agents within a distributed computational network

Publication Date: 2026.05.05 CITIBANK N A

AI summary

Systems and methods disclosed herein automatically evaluate, select, and coordinate artificial intelligence (AI)-based agents for collaborative distributed task execution based on dynamic, multi-attribute scoring and resource allocation models. The system obtains a task specification request defining a computational requirement set, a performance metric set, and an available resource set for one or more tasks to be executed by a network of AI-based agents. A first AI model set generates domain-specific test datasets and validates prospective agents by comparing agent-generated fingerprints against predetermined hash values stored on a distributed or federated ledger. A second AI model set constructs a multi-dimensional scoring data structure for each agent by using historical performance metrics to compute weighted composite scores. The system selects a subset of AI-based agents, ranks the agents, and allocates resources proportional to each agent's composite score. A third AI model set coordinates and executes distributed computer-executable workflows across the selected agents.