



nmrg                                                        J. Zhao, Ed.
Internet-Draft                                              R. Pang, Ed.
Intended status: Standards Track                           S. Zhang, Ed.
Expires: 3 September 2026                                   China Unicom
                                                             H. Shi, Ed.
                                                             C. Sun, Ed.
                                            Purple Mountain Laboratories
                                                            2 March 2026


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

Abstract

   This document proposes an AI agent architecture for Network Digital
   Twin (NDT) that integrates AI agents with digital twin technology.

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   This Internet-Draft will expire on 3 September 2026.

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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 Network Digital Twin  . . . . . . .   2
   3.  Architecture Components . . . . . . . . . . . . . . . . . . .   3
     3.1.  Network Digital Twin 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.  Automated IP Network Configuration Generation . . . . . .   5
       5.1.1.  Intent Understanding and Policy Generation  . . . . .   5
       5.1.2.  Multi-Level Simulation and Verification . . . . . . .   6
       5.1.3.  Model Evolution and Feedback Loop . . . . . . . . . .   6
     5.2.  Cutover Simulation (Scenario Construction)  . . . . . . .   6
   6.  Security Considerations . . . . . . . . . . . . . . . . . . .   6
   7.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .   7
   8.  Informative References  . . . . . . . . . . . . . . . . . . .   7
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .   7

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 an 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 Network Digital Twin

   Based on the concept of the Network Management Agent (NMA)
   [I-D.zhao-nmop-network-management-agent], we propose an AI Agent
   architecture for Network Digital Twin (NDT)
   [I-D.irtf-nmrg-network-digital-twin-arch].  This architecture extends
   the traditional network digital twin 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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   +-------------------------------------------------------------------------------------------+
   |                        Network Digital Twin Management AI Agent                           |
   |                                                                                           |
   |  - Resource & Health Monitoring        - Intent Parsing & Logic Translation               |
   |  - NDT Instance Lifecycle Management   - Multi-Agent Session Orchestration                |
   |  - Virtual-Physical State Sync Control - Business Intent & Policy Derivation              |
   +-------------------------------------------------------------------------------------------+
               |                                                              |
               |                                                              |
   +---------------------------------------+               +----------------------------------+
   |         Functional Model Agent        |               |      Data Repository Agent       |
   |                                       |               |                                  |
   | - Service Model Optimization          |<------------->| - Real-time Telemetry Ingestion  |
   | - Automated Configuration Synthesis   |               |   & Anomaly Detection            |
   | - Multi-Vendor Syntax Mapping         |               |                                  |
   | - Hierarchical Simulation Sandbox     |               | - Historical Data Intelligence   |
   |   (Compliance, Logic, Impact)         |               |   & Pattern Mining               |
   | - Scenario-specific Modeling          |               |                                  |
   | - Verification Feedback Loop          |               | - Knowledge Base Management      |
   +---------------------------------------+               |   (Vendor Docs/Best Practice)    |
               |                                           |                                  |
               |                                           | - Adaptive Data Retrieval        |
   +---------------------------------------+               |   & Conflict Resolution          |
   |           Basic Model Agent           |               |                                  |
   |                                       |<------------->| - Standardized Validation        |
   | - Network Element Digital Models      |               |   Report Storage                 |
   |   (Config, State, Env)                |               +----------------------------------+
   | - Topology & Connectivity Models      |
   | - Real-time Model Self-Updates        |
   +---------------------------------------+

       Figure 1: AI Agent Architecture for Network Digital Twin

   The following figure illustrates the hierarchical interaction between
   the Management, Functional, Basic, and Data agents.

3.  Architecture Components

3.1.  Network Digital Twin Management AI Agent

   The Network Digital Twin Management Agent serves as the central
   coordination and management component, 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.




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

   *  Session Control: Orchestrates communication sessions and
      interactions among various AI agents to ensure coherent operation.

   *  Intent Translation & Policy Derivation: Derives executable
      policies from high-level business intents through semantic parsing
      and internal logic models.

   *  Virtual-Physical Synchronization Control: Manages bidirectional
      data flow between the NDT and the physical network to ensure
      accurate representation.

3.2.  Functional Model AI Agent

   The Functional Model Agent is responsible for advanced service
   modeling, configuration generation, and optimization.  It
   autonomously invokes required functional models per validation
   policies and refines them via historical data analysis.

   *  Service Model Optimization: Refines models through performance
      analysis and adaptive learning algorithms.

   *  Automated Configuration Synthesis: Generates vendor-specific
      configurations or intermediate policy representations based on the
      intent model.

   *  Hierarchical Simulation Sandbox: Provides a multi-stage
      environment to verify configurations across compliance, logic, and
      business impact layers.

   TBD.

3.3.  Basic Model AI Agent

   The Basic Model Agent maintains fundamental network element and
   topology representations.  It is capable of updating digital models
   in real-time based on physical network changes 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.  It autonomously manages the
   data lifecycle with the following capabilities:




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   *  Real-time Data Collection: Implements multi-protocol ingestion for
      streaming telemetry while autonomously detecting data anomalies.

   *  Historical Data Intelligence: Curates structured data and
      knowledge graphs to support pattern mining, and model training.

   *  Adaptive Data Services: Provides context-aware data retrieval with
      intelligent caching and conflict resolution.

   *  Knowledge Base Integration: Stores network configurations, vendor
      documents, and best practices to support incremental model
      updates.

   *  Validation Reporting: Generates standardized, machine-readable
      validation reports including rule IDs, configuration items, and
      evidence.

4.  Agent Interactions

   The architecture employs bidirectional Agent-to-Agent (A2A)
   communication: the Functional and Basic Model Agents interact with
   the Data Repository Agent for data synchronization, while the
   Management Agent centrally orchestrates these interactions to
   maintain a coherent workflow.

   Inter-agent interactions SHOULD support state rollback mechanisms to
   ensure the virtual state remains synchronized with the physical
   network during failed intent decompositions.

5.  Intelligent Use Case Realization

5.1.  Automated IP Network Configuration Generation

   This use case demonstrates how the AI Agent architecture automates
   the end-to-end lifecycle of network configuration.

5.1.1.  Intent Understanding and Policy Generation

   *  Intent Parsing: The Management Agent receives declarative intents,
      identifying network objects and resolving logic conflicts between
      multiple intents.

   *  Config Generation: The Functional Model Agent produces vendor-
      specific configurations.  It maintains context awareness by
      retrieving current states from the Basic Model Agent.






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   *  Vendor-agnostic Abstraction: The system uses an intermediate
      policy representation to ensure functional consistency across
      heterogeneous hardware.

5.1.2.  Multi-Level Simulation and Verification

   Before deployment, configurations MUST undergo a multi-stage
   verification process:

   *  Semantic Consistency Verification: Ensures that synthesized
      configurations result in deterministic network behavior, bridging
      the gap between probabilistic AI generation and deterministic
      network operations..

   *  Hierarchical Validation: The Functional Model Agent executes
      simulations in a sandbox layer-by-layer: 1.  Compliance: Detecting
      syntax errors and policy violations. 2.  Functional Correctness:
      Verifying reachability and protocol convergence via the Basic
      Model Agent. 3.  Service Impact: Evaluating potential performance
      degradation using traffic patterns from the Data Repository Agent.

5.1.3.  Model Evolution and Feedback Loop

   *  Closed-loop Optimization: If verification fails, the Management
      Agent feeds error reports back to the generation models of the
      Functional Model Agent for iterative optimization.

   *  Incremental Learning: Experts can manually correct AI outputs,
      which are stored in the Data Repository Agent to fine-tune future
      generation and verification models.

5.2.  Cutover Simulation (Scenario Construction)

   *  Process Reproduction: The architecture simulates the complete
      cutover lifecycle, including device startup/shutdown, routing
      adjustments, and configuration delivery.

   *  Risk Mitigation: By monitoring link status and business capacity
      in the NDT, the Network Digital Twin Management AI Agent and
      Functional Model Agent jointly identify plan loopholes and
      optimize emergency response sequences before physical execution.

6.  Security Considerations

   AI-Generated Risks: Specific checks must be implemented to detect
   "hallucinated" commands or non-compliant security policies generated
   by large language models.




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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-12, 27 February 2026,
              <https://datatracker.ietf.org/doc/html/draft-irtf-nmrg-
              network-digital-twin-arch-12>.

   [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-04, 26 February
              2026, <https://datatracker.ietf.org/doc/html/draft-zhao-
              nmop-network-management-agent-04>.

Authors' Addresses

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


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


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







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   Hongwei Shi (editor)
   Purple Mountain Laboratories
   Nanjing
   China
   Email: shihongwei@pmlabs.com.cn


   Chen Su (editor)
   Purple Mountain Laboratories
   Nanjing
   China
   Email: suchen@pmlabs.com.cn







































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