System (100) for agent-based orchestration of microservices using self-directed AI executors in Java

Abstract

System (100) for agent-based orchestration of microservices using self-directed AI executors in Java, consisting of: a variety of self-directed AI executors implemented in the Java programming language, where each executor contains machine learning algorithms for autonomous decision-making regarding the deployment, scaling, monitoring, and optimization of distributed microservices, a central orchestration engine that coordinates the activities of the aforementioned AI executors via advanced communication protocols and simultaneously maintains a comprehensive system knowledge base, an intelligent service register that contains metadata on all system services, including performance characteristics and dependency relationships, where the AI ​​executors continuously analyze real-time metrics such as service performance, resource utilization, and user demand patterns to autonomously optimize system behavior without human intervention, the system uses neural network models trained on historical system performance data and service behavior patterns to enable predictive analytics and a proactive system.

Description

[0001] The present invention relates to autonomous artificial intelligence systems for the orchestration of microservices using self-directed executors in distributed computing environments.

[0002] Microservice architectures have become increasingly prevalent in modern software development due to their scalability, modularity, and fault isolation capabilities. However, orchestrating multiple microservices presents significant challenges, including service discovery, load balancing, fault tolerance, and workflow management. Traditional orchestration systems require extensive manual configuration and monitoring, resulting in high operational complexity and potential sources of error. Current microservice orchestration solutions typically rely on static configuration files and predefined workflows that cannot adapt to changing system conditions or business requirements.Service mesh technologies like Istio and container orchestration platforms like Kubernetes offer basic orchestration capabilities, but lack intelligent decision-making capabilities for autonomous system management.

[0003] Existing artificial intelligence applications in distributed systems primarily focus on optimizing individual services rather than providing comprehensive orchestration intelligence. Most current AI-powered orchestration tools require significant manual intervention for configuration, monitoring, and adaptation to changing workloads or system conditions. The complexity of managing interdependent microservices increases exponentially with system size, leading to challenges in maintaining optimal performance, ensuring reliability, and efficiently managing resource utilization. Traditional approaches, due to their reactive rather than predictive nature, often result in resource overprovisioning or a degradation of service quality during peak loads.Furthermore, existing orchestration frameworks lack the ability to understand overarching business requirements and automatically translate them into optimal microservice deployment and interaction patterns. This limitation necessitates extensive technical expertise and manual intervention to align system behavior with business objectives.

[0004] To solve this problem, the present invention offers a system for agent-based microservice orchestration using self-directed AI executors in Java.

[0005] The system offers fully autonomous microservice orchestration, without requiring human intervention for routine operations, configuration changes, or system adjustments.

[0006] The system achieves intelligent resource optimization through advanced machine learning algorithms that continuously analyze service performance indicators, resource usage patterns, and interdependencies between microservices.

[0007] The system offers improved fault tolerance and self-healing capabilities through distributed AI-supported monitoring and automatic recovery mechanisms implemented on all execution agents.

[0008] The system enables seamless scalability through intelligent workload forecasting and automatic service provisioning functions, eliminating manual scaling decisions and configuration bottlenecks.

[0009] The system offers intelligent workflow orchestration that automatically translates high-level business requirements into optimal interaction patterns for microservices and deployment strategies.

[0010] In one embodiment, the present invention relates to an agent-based microservice orchestration system using self-directed AI executors in Java. The system comprises multiple AI executor agents, each equipped with machine learning capabilities for autonomous decision-making regarding service delivery, scaling, monitoring, and optimization. The central orchestration engine coordinates the executor activities via advanced communication protocols and maintains a comprehensive system knowledge base for informed decision-making. Each self-directed executor has neural network models trained on system performance data, service behavior patterns, and business requirements to enable predictive analytics and proactive system management.The executors continuously analyze real-time metrics such as service levels, resource utilization, and user demand patterns to autonomously optimize system behavior. The invention implements advanced natural language processing capabilities, allowing business users to formulate requirements in simple language, which the system automatically translates into appropriate microservice orchestration patterns and deployment configurations. The AI ​​executors use augmented learning algorithms to continuously improve their decision-making abilities based on system feedback and performance results. The system incorporates comprehensive fault tolerance mechanisms with distributed monitoring, automatic fault detection, and intelligent recovery procedures that minimize service disruptions and maintain system reliability.Advanced algorithms for load balancing and resource optimization ensure efficient use of computing resources while maintaining service quality standards and meeting performance targets.

[0011] The invention is explained again below with reference to the figure. This shows: Fig. : a system for agent-based microservice orchestration using self-directed AI executors in Java.

[0012] Fig.This document demonstrates a system for agent-based microservice orchestration using self-directed AI executors in Java. This autonomous microservice orchestration system represents a significant advancement in distributed computing management by integrating artificial intelligence into traditional orchestration frameworks. The system architecture comprises three main components: the central orchestration engine, self-directed AI executors, and the intelligent service register, all of which work together in a coordinated manner to enable comprehensive autonomous management of microservice environments. The central orchestration engine serves as the primary coordination hub for all system activities and maintains a comprehensive knowledge base about system topology, service dependencies, performance metrics, and business requirements.This engine utilizes advanced graphical neural networks to model complex relationships between services and predict optimal orchestration strategies based on the current system state and historical performance data. The engine continuously updates its internal models based on real-time feedback from deployed execution modules to ensure that orchestration decisions remain accurate and effective even under changing system conditions. Self-directed AI executors represent the core innovation of this invention, with each executor acting as an autonomous agent capable of making independent decisions regarding service management tasks. These executors are implemented in Java using advanced machine learning frameworks and incorporate multiple specialized neural network models for various aspects of orchestration management.Each executor has its own local knowledge base and decision-making capabilities, while simultaneously contributing to collective intelligence through communication with other executors and the central engine. The executor architecture includes specialized modules for service discovery, load balancing, fault detection, resource optimization, and workflow orchestration. The service discovery module uses reinforcement learning algorithms to optimize service routing decisions based on performance metrics, network topology, and service availability.

[0013] The load balancing module uses predictive analytics to forecast demand patterns and proactively adjust resource allocation to maintain optimal performance levels. Fault detection capabilities within each execution module employ anomaly detection algorithms trained on normal system domain patterns to identify potential problems before they impact service availability. When anomalies are detected, the execution modules automatically perform corrective actions, including service migration, resource reallocation, and load redistribution, while notifying other system components of these corrective actions. The resource optimization module continuously analyzes system performance metrics to identify opportunities for efficiency improvements and cost reductions.This module uses multi-criteria optimization algorithms that balance performance requirements, cost constraints, and service level agreements to determine optimal resource allocation strategies. The module also considers future demand forecasts and seasonal patterns when making resource management decisions. The system implements comprehensive security measures, including encrypted communication channels, authentication and authorization mechanisms, and audit logging capabilities. Security policies are automatically applied to all system components, with AI executors continuously monitoring potential security threats and implementing appropriate safeguards. Reference symbol list 100 System

Claims

[1] System (100) for agent-based orchestration of microservices using self-directed AI executors in Java, consisting of: a variety of self-directed AI executors implemented in the Java programming language, where each executor contains machine learning algorithms for autonomous decision-making regarding the deployment, scaling, monitoring, and optimization of distributed microservices, a central orchestration engine that coordinates the activities of the aforementioned AI executors via advanced communication protocols and simultaneously maintains a comprehensive system knowledge base, an intelligent service register that contains metadata on all system services, including performance characteristics and dependency relationships, where the AI ​​executors continuously analyze real-time metrics such as service performance, resource utilization, and user demand patterns to autonomously optimize system behavior without human intervention, the system uses neural network models trained on historical system performance data and service behavior patterns to enable predictive analytics and a proactive system. [2] System (100 according to claim 1, wherein each AI executor comprises specialized modules including service detection via reinforcement learning, predictive load balancing, anomaly-based fault detection, resource optimization and natural language processing for business requirements interpretation. [3] System (100) according to claim 1, wherein the central orchestration engine uses graphical neural networks to model service relationships and predict optimal orchestration strategies based on real-time feedback and historical performance data. [4] System (100) according to claim 1, wherein the fault detection mechanisms comprise distributed monitoring, automatic anomaly detection, intelligent root cause analysis and self-healing functions that learn from fault patterns. [5] System (100) according to claim 1, wherein the intelligent scaling functions comprise algorithms for predicting workload, automatic service provisioning, decisions for horizontal and vertical scaling, and consideration of service dependencies and business priorities. [6] System (100) according to claim 1, wherein the resource optimization functionality comprises continuous performance analysis, multi-objective optimization algorithms that balance cost and performance, and neural networks that analyze multidimensional system metrics. [7] System (100) according to claim 1, wherein the natural language processing functions interpret business requirements in simple language and automatically generate service flows without requiring technical expertise. [8] System (100) according to claim 1, wherein the natural language processing functions interpret business requirements in simple language and automatically generate service workflows without requiring technical expertise. [9] System (100) according to claim 1, wherein the communication framework comprises secure message protocols, automatic retry mechanisms, message queues and intelligent routing to maintain connectivity during interruptions.

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