



nmrg                                                        J. Zhao, Ed.
Internet-Draft                                              R. Pang, Ed.
Intended status: Standards Track                           S. Zhang, Ed.
Expires: 23 April 2026                                      China Unicom
                                                         20 October 2025


           AI Agent Architecture for DTN Digital Twin Network
                  draft-zhao-nmrg-ai-agent-for-dtn-00

Abstract

   This document proposes an AI agent architecture for Digital Twin
   Network (DTN) that integrates AI agents with digital twin technology.
   The architecture extends the traditional DTN architecture by
   incorporating autonomous AI agents at each component level, enabling
   more intelligent and adaptive network management capabilities.

Status of This Memo

   This Internet-Draft is submitted in full conformance with the
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   Internet-Drafts are working documents of the Internet Engineering
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   This Internet-Draft will expire on 23 April 2026.

Copyright Notice

   Copyright (c) 2025 IETF Trust and the persons identified as the
   document authors.  All rights reserved.

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   provided without warranty as described in the Revised BSD License.



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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
   2.  AI Agent Architecture for Digital Twin Network  . . . . . . .   2
   3.  Architecture Components . . . . . . . . . . . . . . . . . . .   3
     3.1.  Digital Twin Network Management AI Agent  . . . . . . . .   3
     3.2.  Functional Model AI Agent . . . . . . . . . . . . . . . .   4
     3.3.  Basic Model AI Agent  . . . . . . . . . . . . . . . . . .   4
     3.4.  Data Repository AI Agent  . . . . . . . . . . . . . . . .   4
   4.  Agent Interactions  . . . . . . . . . . . . . . . . . . . . .   5
   5.  Intelligent Use Case Realization  . . . . . . . . . . . . . .   5
     5.1.  Simulation Scenario Construction  . . . . . . . . . . . .   5
     5.2.  Simulation Execution  . . . . . . . . . . . . . . . . . .   5
   6.  Security Considerations . . . . . . . . . . . . . . . . . . .   6
   7.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .   6
   8.  Informative References  . . . . . . . . . . . . . . . . . . .   6
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .   6

1.  Introduction

   Digital twins have emerged as a powerful paradigm for network
   management, providing virtual representations of physical networks
   that enable simulation, analysis, and optimization.  However,
   traditional digital twin architectures often lack the autonomous
   decision-making capabilities needed for modern network environments.
   This document proposes a architecture that combines digital twin
   concepts with intelligent AI agents, creating a more dynamic and
   responsive network management system.

   The architecture is designed to be compatible with existing digital
   twin architectures.  This approach enables distributed decision-
   making, adaptive behavior, and enhanced collaboration between digital
   twin components.

2.  AI Agent Architecture for Digital Twin Network

   Based on the concept of the Network Management Agent (NMA)
   [I-D.zhao-nmop-network-management-agent], we propose an AI Agent
   architecture for Digital Twin Networks (DTN)
   [I-D.irtf-nmrg-network-digital-twin-arch].  This architecture extends
   the traditional digital twin network by integrating AI agents into
   each core component.  While preserving the fundamental structure of
   digital twins, the architecture introduces enhanced autonomous
   capabilities and intelligent decision-making across the network
   management lifecycle.






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   +----------------------------------------------------------------------------------------------------------------------------+
   |                                            Digital Twin Network Management Agent                                           |
   |                                       - Resource Monitor                                                                   |
   |                                       - Lifecycle Management (DTN Instantiation)                                           |
   |                                       - Intent Translation & Policy Derivation                                             |
   |                                       - Virtual-Physical Synchronization Control                                           |
   +----------------------------------------------------------------------------------------------------------------------------+
                                   |                                                                         |
   +-----------------------------------------------------------------+    +-----------------------------------------------------------------+
   |                    Functional Model Agent                       |    |                        Data Repository Agent                    |
   |                                                                 |    |                                                                 |
   |                                                                 |<-->|                    - Real-time Data Collection                  |
   | - Service Model Optimization                                    |    |                                                                 |
   | - Scenario-specific Model Creation                              |    |                    - Historical Data Intelligence Management    |
   +-----------------------------------------------------------------+    |                                                                 |
                                  |                                       |                    - Adaptive Data Services                     |
   +-----------------------------------------------------------------+    |                                                                 |
   |                     Basic Model Agent                           |<-->|                                                                 |
   |                                                                 |    +-----------------------------------------------------------------+
   | - Network Element Models (Config, State, Environment)           |
   | - Topology Models (Connectivity, Link Relationships)            |
   +-----------------------------------------------------------------+

       Figure 1: AI Agent Architecture for Digital Twin Network

   TBD.

3.  Architecture Components

3.1.  Digital Twin Network Management AI Agent

   The Digital Twin Network Management Agent serves as the central
   coordination and management component in the architecture, providing
   the following key functionalities:

   *  Resource Monitoring: Continuously tracks and monitors the status,
      performance metrics, and operational health of all resources
      within the digital twin environment.

   *  Lifecycle Management: Governs the complete lifecycle of Digital
      Twin Network instances, encompassing instantiation, configuration,
      state synchronization, maintenance, and termination.

   *  Session Control: Manages and orchestrates communication sessions
      and interactions among the various AI agents within the
      architecture to ensure coherent operation.





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   *  Intent Translation & Policy Derivation: Translates high-level
      business or operational intents from users into specific,
      executable policies and configuration models for the digital twin
      and its physical counterpart.

   *  Virtual-Physical Synchronization Control: Manages the
      bidirectional data flow and state synchronization between the
      digital twin and the physical network to ensure accurate
      representation and control.

3.2.  Functional Model AI Agent

   The Functional Model Agent is responsible for advanced service
   modeling and optimization capabilities, it can autonomously invoke
   the required functional models based on validation policies, while
   continuously optimizing models through the analysis of historical
   data.  Additionally, it develops specialized models tailored to
   specific operational scenarios, use cases, and network conditions.

   *  Service Model Optimization: Continuously refines and optimizes
      service models through performance analysis and adaptive learning
      algorithms.

   *  Scenario-specific Model Creation.

   TBD.

3.3.  Basic Model AI Agent

   The Basic Model Agent maintains fundamental network element and
   topology representations, capable of updating itself in real-time
   based on changes in the physical network to ensure the accuracy of
   validation.

3.4.  Data Repository AI Agent

   The Data Repository AI Agent serves as the intelligent data
   governance and provisioning component, enabling data-driven
   operations across the digital twin ecosystem.  It autonomously
   manages data lifecycle with the following AI-enhanced capabilities:

   *  Real-time Data Collection: Implementing multi-protocol ingestion
      for streaming network telemetry and performance metrics, while
      autonomously detecting and flagging data anomalies or
      inconsistencies.






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   *  Historical Data Intelligence Management: Building structured data
      and integrates intelligent analytics capabilities to support trend
      analysis and pattern mining, providing data foundation for model
      training and proactive optimization.

   *  Adaptive Data Services: Providing context-aware data retrieval
      with intelligent caching, pre-processing, and conflict resolution,
      dynamically prioritizing datasets for critical tasks such as
      simulation or root cause analysis.

4.  Agent Interactions

   The architecture employs bidirectional Agent-to-Agent (A2A)
   communication to ensure seamless operation: the Functional and Basic
   Model Agents interact with the Data Repository Agent for data access
   and synchronization, while the Digital Twin Network Management Agent
   centrally orchestrates these interactions and manages inter-agent
   dependencies to maintain a coherent workflow across the entire
   system.

5.  Intelligent Use Case Realization

5.1.  Simulation Scenario Construction

   S1: The Digital Twin Network Management Agent receives user
   instructions, performs intent translation, and generates simulation
   strategies.

   S2: The Functional Model Agent constructs functional models or
   coordinates existing models based on the strategies.

   S3: The Basic Model Agent provides real-time configuration models and
   topological relationships for the migration scenario.

   S4: The Data Repository Agent injects real-time traffic information.

   All agents collaborate to create a simulation sandbox consistent with
   the actual physical network, within which the Functional Model Agent
   simulates the complete migration process.

5.2.  Simulation Execution

   S1: The Functional Model Agent continuously evaluates network
   performance indicators.

   S2: If KPIs fail to meet standards or risks are detected, a rollback
   mechanism is immediately triggered.  The agent coordinates with the
   Basic Model Agent to develop and execute a rollback plan.



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   S3: After analyzing and optimizing the solution, the simulation
   restarts and cycles iteratively until a compliant migration plan is
   generated.

   S4: Upon simulation completion, the Functional Model Agent leverages
   historical data to optimize existing service models.

6.  Security Considerations

   TBD.

7.  IANA Considerations

   TBD.

8.  Informative References

   [I-D.irtf-nmrg-network-digital-twin-arch]
              Zhou, C., Yang, H., Duan, X., Lopez, D., Pastor, A., Wu,
              Q., Boucadair, M., and C. Jacquenet, "Network Digital
              Twin: Concepts and Reference Architecture", Work in
              Progress, Internet-Draft, draft-irtf-nmrg-network-digital-
              twin-arch-11, 6 July 2025,
              <https://datatracker.ietf.org/doc/html/draft-irtf-nmrg-
              network-digital-twin-arch-11>.

   [I-D.zhao-nmop-network-management-agent]
              XingZhao, Wang, M., Wu, B., Ceccarelli, D., Zheng, H., and
              J. Zhou, "AI based Network Management Agent(NMA): Concepts
              and Architecture", Work in Progress, Internet-Draft,
              draft-zhao-nmop-network-management-agent-03, 17 October
              2025, <https://datatracker.ietf.org/doc/html/draft-zhao-
              nmop-network-management-agent-03>.

Authors' Addresses

   Jing Zhao (editor)
   China Unicom
   Beijing
   China
   Email: zhaoj501@chinaunicom.cn


   Ran Pang (editor)
   China Unicom
   Beijing
   China
   Email: pangran@chinaunicom.cn



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   Shuai Zhang (editor)
   China Unicom
   Beijing
   China
   Email: zhangs366@chinaunicom.cn














































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