AI Agent Resource Anomaly Detection for Distributed Networks

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Solution Overview

Problem

Distributed AI agent networks face challenges in aligning individual agent objective functions with 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 detect anomalous behavior, ensuring compliance and performance through AI models and smart contracts.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If distributed AI agent networks operate autonomously with individual objective functions, then agent independence and operational flexibility are improved, but resource allocation efficiency deteriorates due to misalignment with collective network goals

Engineering Contradiction:

Improveagent independenceVSAvoidresource allocation efficiency

Core Design Contradiction:

Adaptability or versatilityVSProductivity

Solution Approach 1:

The patent implements a feedback mechanism where the centralized registry monitors resource allocation and performance metrics across distributed agents, providing feedback signals that guide agents to adjust their resource consumption and task execution to align with collective network goals while preserving their autonomous operation

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The centralized registry acts as an intermediary between individual autonomous agents and the collective network goals, mediating resource allocation decisions by receiving resource requests from agents, evaluating them against network objectives, and granting or denying access to shared resources accordingly

Inventive Principle:

Principle #24Intermediary (Mediator)

2Productivity

If AI agents are deployed across distributed networks, then system scalability and coverage are improved, but visibility into upstream agents and network-wide behavior deteriorates

Engineering Contradiction:

Improvesystem scalabilityVSAvoidvisibility into upstream agents

Core Design Contradiction:

ProductivityVSLoss of information

Solution Approach 1:

The patent merges the distributed identities, actions, and resource usage data of multiple autonomous agents into a unified registry system, combining individual agent information with network-wide context to maintain complete visibility and audit trails across the entire distributed network hierarchy

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The centralized registry serves multiple functions simultaneously: it acts as an identity verification authority, resource allocation manager, performance monitoring system, and audit trail repository, providing universal visibility into all upstream agents regardless of their position in the distributed network hierarchy

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Ease of operation

If resource allocation in distributed AI networks is decentralized to agents, then operational autonomy is improved, but resource utilization optimization deteriorates

Engineering Contradiction:

Improveoperational autonomyVSAvoidresource utilization optimization

Core Design Contradiction:

Ease of operationVSLoss of energy

Solution Approach 1:

The patent implements preliminary action by having the centralized registry pre-establish resource allocation policies, performance thresholds, and optimization criteria before agents execute tasks, enabling autonomous agents to make resource allocation decisions that are optimized for overall network efficiency without sacrificing their operational independence

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS12592897B2Detecting anomalous resource distribution patterns in distributed artificial intelligence-based agent networks

Publication Date: 2026.03.31 CITIBANK N A

AI summary

Systems and methods disclosed herein automatically detect, analyze, and mitigate anomalous resource distribution among artificial intelligence (AI)-based agents within a distributed computational network. The system receives a resource allocation request specifying computational resources, agent parameters, and performance objectives for a network of agents. A first AI model set monitors agent activity by tracking resource consumption and behavioral deviations from baseline profiles. The system compares resource usage of agents with historical norms and/or predetermined thresholds to generate an anomaly score for each agent. A second AI model set aggregates scores to construct a multi-dimensional data structure that indicates the comparison and anomaly score. The system ranks agents by anomaly severity to isolate agents with high scores (e.g., misaligned agents), and reallocates resources and updates access privileges for the misaligned agents.