AI Agent-Based Autonomous Service Orchestration and Intent-Based Network Management System in 6G Networks

Abstract

The invention relates to an intent-based network management system that enables service requests in 6G communication networks to be received via natural language or structured intent statements, converted into technical network parameters by AI agents, and the relevant network resources to be autonomously orchestrated. The system consists of an intent understanding and translation engine, an AI agent orchestrator, a service resource matching module, an inter-agent negotiation protocol, an autonomous service lifecycle manager, and a learning optimization engine. After receiving a user request, the appropriate RAN, Core, Edge, Transport, Security, and Slice agents are assigned; resources are optimized, the final configuration is created, and the service is automatically deployed. The system also continuously monitors performance, detects anomalies, and performs auto-scaling and self-healing processes. By learning from past service experiences, it makes future service deployments faster, more accurate, and more efficient.

Description

1 TARIFF AI Agent-Based Autonomous Service Orchestration and Intent in 6G Networks BASED NETWORK MANAGEMENT SYSTEM Technical Area 5 The invention relates to the processing of service requests in 6G communication networks using natural language or structured intention. these expressions are received, and these intentions are understood, interpreted, and networked by AI agents. Autonomous orchestration of resources and automatic activation of services. It relates to the system and method. State of the Art Currently, service requests are interpreted manually by network engineers. This is translated into technical requirements. Service configuration is done at different network layers (RAN, Core, Transport, and Edge are done separately and manually in multi-vendor environments. Service integration is complex, time-consuming, and carries a high risk of errors. The current 15 In many systems, days or even weeks can pass between a service request and commissioning. Different Automated negotiation and resource matching mechanism between network components. It is not available. The service lifecycle is managed reactively; proactive optimization and Predictive resource allocation is not possible. Learning from past service experiences and It lacks automatic optimization capabilities. Intent-based statements (e.g., "a low-latency 20") "I want a slice") Automatic translation of technical parameters is not supported. This The shortcomings result in high operational costs, long service commissioning times, and human resources. This leads to errors, resource inefficiency, and customer dissatisfaction. Due to the negative aspects described above and the current solutions regarding the issue... Due to its inadequacy, it has become necessary to make improvements in the relevant technical field. 25 Brief Description of the Invention The invention represents a new breakthrough in this field, unlike the structures used in existing technology. The aim is to create a structure with different technical specifications that bring these elements together. The primary aim of the invention is to completely transform service management in 6G communication networks, 30 An AI agent ecosystem with intent-based and autonomous orchestration capabilities. to present. Another purpose of the invention is to provide service delivered via natural language or structured expressions of intent. the requests being understood and interpreted by AI agents and network resources The goal is to automatically orchestrate and activate the services within minutes. 35 2 Another aim of the invention is to cover the entire lifecycle of services, from their creation to their termination. The goal is to create a system that autonomously manages its cycle. Another purpose of the invention is to monitor service performance, learn usage patterns, and a structure that suggests faster and more optimized configurations for similar future demands to create. 5 Another aim of the invention is to reduce service startup time from days to hours, or even minutes. It is to bring down. Another aim of the invention is to eliminate the risk of manual configuration errors. Another objective of the invention is to enable seamless integration across multi-vendor environments. Another aim of the invention is to prevent unnecessary costs by optimizing resource utilization. 10 Another aim of the invention is to increase the intelligence of the system with each service experience. Another aim of the invention is to support 5G, 6G, satellite networks (NTN), private networks, and IoT. The goal is to provide a structure that can be implemented in their infrastructures using the same methodology. Another objective of the invention is to provide API-based integration with existing OSS / BSS systems. The goal is to create a system that can be easily adapted. 15 Another aim of the invention is operational transparency through a visual and analytical management panel. to provide. Another aim of the invention is to enable operators to analyze the decisions, negotiation processes, and actions taken by AI agents. The goal is to enable real-time monitoring of service performance. Another aim of the invention is to enable operators, corporate customers, and vertical industries (healthcare, manufacturing, 20 It carries significant market potential for logistics, energy, and smart city projects. To fulfill the purposes described above, the invention provides services in 6G communication networks. making its management completely autonomous, intention-based and self-learning (learning) operates based on the principle of an AI agent ecosystem, where the user or Starting with the application expressing its service intent in natural language, the intent is parsed by an LLM-based parser 25 processing, semantic validation, and conversion into technical network parameters, The converted technical requirements are transmitted to the AI ​​Agent Orchestrator, Master Orchestrator The Agent determines which specialist agents will be involved in the process and assigns tasks. Specialized agents such as RAN Agent, Core Agent, Edge Agent, and Security Agent operate in parallel. The process involves each specialist agent interacting with the Service Resource Matching Module, 30 Resource Inventory Database querying, Constraint Solver Engine and Multi-objective Optimization algorithms are run to generate the most suitable resource recommendations, expertly... Reconciliation of proposals from agents through the Inter-Agent Negotiation Protocol, Balancing conflicting preferences, bidding for limited resources, and reaching consensus. Creating the final configuration agreed upon with the building algorithm, the agreed 35 the configuration is automatically determined by the Autonomous Service Lifecycle Manager 3 The implementation involves the Service Provisioning Engine networking based on Infrastructure as Code principles. Configuring the elements, Continuous Monitoring after the service is activated initiation; performance monitoring, anomaly detection, auto-scaling, and The self-healing mechanism is activated when needed, and the energy collected throughout this entire process Data analysis by a Learning Optimization Engine, Historical Data Analytics, 5 The system's decision-making is determined using Pattern Recognition and Reinforcement Learning algorithms. continuous improvement of quality, each service experience enriching the Knowledge Base, and This enables faster and more accurate decisions to be made in similar future requests. Thanks to the feeding cycle, the system learns over time, the system's intention acquisition → translation → Orchestration → Resource Matching → Negotiation → Provisioning → Monitoring → Learning 10 its transformation into a fully autonomous structure operating within a loop, eliminating manual intervention a self-sufficient, independent decision-maker that learns from its experiences and is constantly improving It is the system that enabled the emergence of the 6G service management system. Figures to Help Understand the Invention 15 Figure 1 shows the general architecture of the system that is the subject of the invention. Explanation of Part References 1. Intent Detection and Translation Engine 2. AI Agent Orchestrator 3. Service Resource Mapping Module 20 4. Inter-Agent Negotiation Protocol 5. Autonomous Service Lifecycle Manager 6. Learning Optimization Engine Detailed Description of the Invention 25 In this detailed explanation, the preferred configurations of the invention are not merely for better understanding the subject. This is intended to facilitate understanding and will not create any limiting effects. The invention, 6G intent-based systems that make service management in communication networks completely autonomous. based on the principle of an AI agent ecosystem that is both self-learning and receptive to learning. He / She is working. 30 The operating principle of the system covered by the invention is based on the natural service intent of the user or application. It begins with expressing it in language. The Intent Understanding and Translation Engine (1) parses this intent using an LLM-based parser. It processes the data, subjects it to semantic validation, and translates it into technical network parameters. The converted technical requirements are transmitted to the AI ​​Agent Orchestrator (2). Master Orchestrator The agent determines which specialist agents will be involved in the process and assigns tasks. RAN Agent, 35 Specialized agents such as Core Agent, Edge Agent, and Security Agent begin working in parallel. 4 Each expert agent interacts with the Service Resource Mapping Module (3). Resource Inventory Database is queried, Constraint Solver Engine and Multi-objective Optimization Algorithms are run to generate the most suitable resource recommendations. Suggestions from expert agents are reconciled through the Inter-Agent Negotiation Protocol (4). Conflicting preferences are balanced, bidding is done for limited resources, and consensus building 5 The final configuration agreed upon using the algorithm is created. The agreed configuration is determined by the Autonomous Service Lifecycle Manager (5). It is implemented automatically. Service Provisioning Engine, Infrastructure as Code principles. Configures network elements. Continuous Monitoring after the service is activated. It starts; performance is monitored, anomalies are detected, auto-scaling is performed, and self-10 is activated if necessary. The healing mechanism is activated. Data collected throughout this process are analyzed by the Learning Optimization Engine (6). Historical Data Analytics, Pattern Recognition, and Reinforcement Learning algorithms are used. The system's decision quality is continuously improved by using each service experience and the Knowledge Base. It enriches and enables faster, more accurate decisions to be made in similar future demands. provides. Thanks to the feedback loop, the system learns over time. The system takes intention → translates → orchestration → resource matching → negotiation → provisioning → monitoring → learning It transforms into a fully autonomous structure operating within its cycle. In conclusion, it is a self-deciding system that requires no manual intervention, 20 This results in a 6G service management system that learns from its experiences and continuously improves. The elements and functions of the system described in the invention are as follows: The Intent Understanding and Translation Engine (1) is a basic function that interprets the intentions of a user or third party Service intents (in natural language or structured format) coming from applications The function is to understand, interpret, and translate into technical network parameters. This is the LLM-based Intent 25 function. This is achieved through a parser structure. This parser supports large language models (Large Language Models) similar to GPT. The Semantic Validation Engine processes natural language expressions using a model. The Semantic Validation Engine technically processes intent. It checks whether it is feasible and whether it contains any contradictions. Technical Parameter Translator converts intent into structured technical parameters. This technical structure... Thanks to this, the system integrates human language into the network configuration without manual interpretation. It transforms. AI Agent Orchestrator (2) is the central coordination and task distribution unit of the system. Its function is: The goal is to enable collaboration among multiple AI agents, distribute tasks, and combine results. The Master Orchestrator Agent is the main agent that manages all processes. It operates across different networks within the system. There are specialized agents representing the layers: RAN Agent (radio resource management), Core 35 Agent (core network functions), Edge Agent (edge ​​computing resources), Transport Agent (transportation network optimization), Security Agent (security policies), Slice Agent (network (slicing management). Agent Registry records the capabilities, status, and loads of all available agents. It dynamically records its levels. The Agent Discovery Mechanism identifies the best options for a given mission. It selects the appropriate agents. The Task Distribution Engine breaks down intent requirements into subtasks and He assigns it to the relevant agents. 5 The Service Resource Mapping Module (3) functions to match service requirements with available network resources. The goal is to match all physical and virtual resources on the network in the most optimal way. The Resource Inventory Database It monitors resources in real time. The Constraint Solver Engine takes service constraints into account. It calculates suitable resource combinations by taking these factors into account. The Multi-objective Optimization module... It optimizes multiple targets simultaneously. The Resource Scoring Engine assigns 10 to each resource candidate. It provides a suitability score. Placement Optimizer determines which locations (data) service components should be placed in. determines where the site will be placed (center, edge site). Inter-Agent Negotiation Protocol (4), automated negotiation and agreement between AI agents. It provides the mechanism. Its function is to balance the conflicting preferences of different agents and reach a consensus. The goal is to create. The Negotiation Protocol Engine manages the negotiation process between agents. The protocol is 15 It supports multi-round negotiation. This structure is fully autonomous and distributed without a central authority. It enables decision-making. The Autonomous Service Lifecycle Manager (5) autonomously manages the entire lifecycle of services. The Service Provisioning Engine automatically applies the agreed-upon configurations. The Continuous Monitoring Module monitors service performance in real time. Telemetry Data 20 The Collector gathers metrics from multiple sources. The Anomaly Detection Engine analyzes normal behavior... It automatically detects deviations. The Auto-scaling Controller adjusts resources according to changes in demand. It increases dynamically or uses policy-based auto-scaling rules. Self-healing. The mechanism automatically detects service disruptions and takes corrective action. Service Termination Manager automatically releases resources when the service life ends, and 25 It performs the cleaning operations. This automation minimizes human intervention. The Learning Optimization Engine (6) functions to learn from past service experiences and The goal is to continuously improve future decisions. The Historical Data Analytics module is successful and It analyzes failed service configurations. Predictive Modeling analyzes future resource configurations. It anticipates their needs. The Knowledge Base includes 30 best practices and learned rules. It stores information in a structured format. The Reinforcement Learning Module is derived from each service experience. It improves decision quality by receiving feedback. The Model Retraining Scheduler periodically updates the AI. It retrains its models with new data. The operational steps of the system described in the invention are as follows: The system receives a service intent from the user or a third-party application. This intent is naturally 35 It can be in language form or in structured JSON / YAML format. Intent Understanding and 6 Translation Engine (1) processes this expression with LLM-based Intent Parser. Parser analyzes the semantic of the sentence. It performs an analysis and identifies key requirements: service type, performance target, reliability. Level, scope. The Semantic Validation Engine determines whether the intent is technically feasible. It checks if there are any conflicting requirements, and if so, makes physically impossible demands. It warns the user or requests corrections to suggest alternatives. Technical Parameter Translator, 5 It converts natural language intent into standard technical parameters. The Contextual Enrichment module... taking into account the user's past service preferences, current subscription profile, and industry sector. It adds additional context by taking it. The translated technical parameters are transmitted to the AI ​​Agent Orchestrator (2). Master Orchestrator Agent, It analyzes service requirements and determines which specialist agents should be involved in the process. It determines this. It checks the status of existing agents by consulting the Agent Registry. For this service... RAN Agent, Core Agent, Edge Agent, Security Agent and Slice Agent are required. task The Distribution Engine breaks down service requirements into subtasks. Each agent receives a task message from Agent. It is sent via Communication Protocol. Agents process their tasks in parallel. It starts. 15 Specialized agents use Service Resource Matching to complete their assigned missions. It interacts with module (3). Each agent queries the Resource Inventory Database. RAN Agent Checks the current spectrum status and base station capacities; Edge Agent edge Query the compute resources (CPU, GPU, memory availability) on the sites. Constraint Solver Engine filters suitable resources by considering each agent. Multi-objective 20 The optimization module is running. The system aims to meet both performance targets and cost. It takes into account both minimization and energy efficiency. Each agent is the best in its field. It identifies resource recommendations and reports these recommendations back to the Agent Orchestrator (2). AI Agent Orchestrator (2) collects resource suggestions from expert agents. However, some The proposals may conflict or there may be competition for limited resources. In this case, Agent 25 The Inter-Negotiation Protocol (4) comes into play. The Negotiation Protocol Engine is a multi-round system. It initiates negotiations. A bidding system is used: Edge Agent-1 and Edge Agent-2 are on the same computer. If they are competing for a resource, the resource is allocated according to the service priority score. Consensus The Building Algorithm produces a balanced solution that all agents can accept. Mediation Agent, He mediates in unresolved disputes. The 30 agreed upon at the end of the negotiation. The final resource configuration is created. The final configuration agreed upon is determined by the Autonomous Service Lifecycle Manager (5). It is implemented automatically. Service Provisioning Engine, Infrastructure as Code principles. It works. Configuration scripts are generated automatically. The relevant network is accessed via the API Gateway. Commands are sent to the elements. The provisioning process is carried out step by step, and each step has 35 Its success is verified. A rollback mechanism is present. When the service is successfully activated, 7 The Continuous Monitoring Module becomes active. The Telemetry Data Collector collects data from multiple sources. It starts collecting metrics. This data is reflected in the dashboard and the SLA Compliance Checker It is constantly monitored by [the relevant authority]. While the service is running, system (5) continuously monitors and optimizes. Continuous The Monitoring Module analyzes performance metrics in real time. Anomaly Detection 5 Engine detects deviations from normal behavior. The SLA Compliance Checker verifies that the service meets the SLA standards. It checks whether it meets the requirements. The Auto-scaling Controller, demand It dynamically adjusts resources according to changes. Self-healing Mechanism, service It automatically corrects malfunctions. Operators are only notified in critical situations; routine operations are different. Operations are carried out completely autonomously. 10 All data collected throughout the service lifecycle (5) by the Learning Optimization Engine (6) Historical Data Analytics analyzes this service and evaluates its success criteria. The Pattern Recognition Engine extracts patterns that can be learned from this experience. Knowledge Base is updated: new best practice rules are added. Reinforcement Learning Module, correct. It rewards agents who make sound decisions and punishes agents who make erroneous decisions. Model Retraining 15 Scheduler periodically trains AI models with new data. The system then prepares for the next similar training session. By using this learned information in service requests, we can be faster, more accurate, and more optimized. It produces decisions that have already been made.

Claims

8 REQUESTS Intent-based technology that makes service management fully autonomous in 1.6G communication networks. based on the principle of an (intent-based) and self-learning AI agent ecosystem It begins with the employee, user, or application expressing their service intent in natural language, The intent is processed by an LLM-based parser, subjected to semantic validation, and the technical network 5 Converting the parameters, and transmitting the converted technical requirements to the AI ​​Agent Orchestrator. The communication will specify which specialist agents the Master Orchestrator Agent will be involved in the process. defining and assigning tasks to agents such as RAN Agent, Core Agent, Edge Agent, and Security Agent. The parallel operation of specialist agents, with each specialist agent participating in Service Resource Matching. Interacting with the module, querying the Resource Inventory Database, Constraint Solver 10 The most suitable resource is determined by running Engine and Multi-objective Optimization algorithms. The formulation of recommendations, the consideration of recommendations from expert agents through Inter-Agent Negotiation. Reconciliation through the protocol, balancing conflicting preferences, for limited resources bidding is done and the final agreement is reached using the consensus building algorithm. The creation of the configuration, the agreed configuration, Autonomous Service Lifecycle 15 Automatic implementation by the Cycle Manager of the Service Provisioning Engine Configuring network elements and activating services using Infrastructure as Code principles. Continuous Monitoring begins after the device is received; performance is monitored, and anomalies are identified. detection, auto-scaling, and activation of the self-healing mechanism when necessary. The data collected throughout this entire process is processed by the Learning Optimization Engine in 20 steps. analysis, Historical Data Analytics, Pattern Recognition and Reinforcement Learning Continuous improvement of the system's decision quality using algorithms, for each service This experience will enrich the Knowledge Base and enable us to respond more quickly to similar requests in the future. enabling more accurate decisions through the feedback loop of the system Learning over time, the system's process: intention recognition → translation → orchestration → source matching → 25 a fully autonomous system operating in a negotiation → provisioning → monitoring → learning cycle its transformation into a structure that can make its own decisions without requiring manual intervention, the emergence of a 6G service management system that learns from its experiences and is constantly improving. It is a system that provides; its feature is; Natural language or structured 30 from the user or third-party applications understanding and interpreting service intents in the format and technical network parameters Translator intent understanding and translation engine (1), The central coordination and task distribution unit of the system, collaboration among multiple AI agents. AI agent orchestrator (2) that provides, distributes tasks and combines results, Service resource 35 that optimally matches service requirements with available network resources. matching module (3), 9 It enables an automated negotiation and agreement mechanism between AI agents, allowing different agents to... Inter-agent negotiation protocol (4) which balances conflicting preferences and creates consensus. Autonomous service lifecycle manager (5), which autonomously manages the entire lifecycle of services. a learner that learns from past service experiences and continuously improves future decisions. optimization engine (6) 5 It includes.

2. It is a system that complies with Request 1, and its feature is that it includes the intent understanding and translation engine (1). A Semantic Validation Engine that processes natural language expressions using an LLM-based Intent Parser structure. to check whether the intention is technically feasible and whether it contains any contradictions. and with Technical Parameter Translator, the intended technical parameters are configured in 10 It contains a transformative structure.

3. The system is compliant with Request 1 and its feature is; Master located within the AI ​​agent orchestrator (2). Orchestrator Agent is an expert tool that manages all processes and represents different network layers. agents (RAN Agent, Core Agent, Edge Agent, Transport Agent, Security Agent, Slice Agent) coordinating with the Agent Registry, it manages the capabilities and status of all existing agents. and dynamically records load levels for a specific task with the Agent Discovery Mechanism. Selecting the most suitable agents and subtracting intent requirements with Task Distribution Engine It includes a structure that divides tasks into categories and assigns them to the relevant agents.

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