draft-wmz-nmrg-agent-ndt-arch-04

Network Management Q. Wu
Internet-Draft Huawei
Intended status: Informational C. Zhou
Expires: 22 November 2026 China Mobile
L. M. Contreras
Telefonica
S. Han
China Unicom
Y. Hong
Daejeon University
21 May 2026

Network Digital Twin and Agentic AI based Architecture for AI driven
Network Operations
draft-wmz-nmrg-agent-ndt-arch-04

Abstract

A Network Digital Twin (NDT) provides a network emulation tool usable
for different purposes such as scenario planning, impact analysis,
and change management. Agentic AI enables dynamic goal-driven
execution and adaptive behavior and closed-loop autonomy. By
integrating a Network Digital Twin into network management together
with the Agentic AI, it allows the network management activities to
take user intent or service requirements as input, automatically
assess, model, and refine optimization strategies under realistic
conditions but in a risk-free environment. Such environment that
operates to meet these types of requirements is said to have AI
driven Network Operations.

AI driven Network Operations brings together existing technologies
such as Agentic AI and Network Digital Twin which may be seen as the
use of a toolbox of existing components enhanced with a few new
elements.

This document describes an architecture for AI driven network
operations and shows how these components work together with network
digital twin and Agentic AI capabilities. It provides a cookbook of
existing technologies to satisfy the architecture and realize intent-
based network management to meet the needs of the network service.

Discussion Venues

This note is to be removed before publishing as an RFC.

Discussion of this document takes place on the Network Management
mailing list (nmrg@irtf.org), which is archived at
https://mailarchive.ietf.org/arch/browse/nmrg.

Source for this draft and an issue tracker can be found at
https://github.com/QiufangMa/Agent-architecture.

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Table of Contents

1. Introduction
2. Conventions and Definitions
3. Introduction of Concepts
3.1. Generative AI and Agentic AI
3.2. Network Digital Twin
4. Characteristics of AI driven Network Operations
5. Architecture Design
5.1. Overall Architecture
5.2. Functional Components
5.2.1. Network Applications
5.2.2. Autonomous Domain
5.3. Functional Interfaces
5.3.1. Human Oversight (Human on the Loop)
5.3.2. Application to Network AI Agent Interface (Intent based
Management)
5.3.3. Network AI Agent to Task AI Agent Interface (Single
Autonomous Domain)
5.3.4. Network AI Agent to Network AI Agent Interface (Cross
Autonomous Domain)
5.3.5. Hybrid Agent System to Agent Fabric Interface (Agent
Lifecycle Management)
5.3.6. Hybrid Agent System to Network Digital Twin Interface
5.3.7. Hybrid Agent System to Knowledge Base Interface
5.3.8. Hybrid Agent System to Physical Network Interface
5.3.9. Feedback-driven Improvement Interface
6. AI Driven Network Operations: Relationship Between
Characteristics and Functional Components
7. AI Agent Registration and Team formation
8. Agent to Agent Communication Security
9. AI Driven Network Operations: A collection of Use Cases
9.1. Multi-Agent Collaboration on Network Configuration Change
9.2. Multi-Agent Collaboration on Network Troubleshooting
9.3. Multi-Agent Collaboration on Network Optimization
9.4. Network level Energy Efficiency Management in the
IP+Optical network
9.5. Network Security Drills (Human on the Loop)
10. Challenges of Integrating Network Digital Twin and Agentic AI
into Network Management
10.1. Make IETF developed YANG models AI ready
10.2. Trust and Security
10.3. Protocols between Agent and Agent/Human operator/Tools
10.3.1. High Risk Operations
10.3.2. The Timeliness Requirements of Collaboration
10.3.3. Collaboration Reliability
10.4. Agent Observability,Intervention and Control
10.5. Agent Benchmarking vs Model Benchmarking
11. Security Considerations
12. IANA Considerations
13. Conclusion
14. References
14.1. Normative References
14.2. Informative References
Appendix A. Network Element AI Agent and Network AI Agent
Collaboration
Appendix B. Acknowledgements
Appendix C. Changes between Revisions
Contributors
Authors' Addresses

1. Introduction

With the rapid expansion of network scale and the increasing demands
on these networks, significant challenges arise with keeping device
configurations consistent with the needs of services and rapidly
adapting the network at scale. The intent based management has been
first introduced in [RFC9315] to address these challenges and
necessitate of continuous network reconfiguration to better adapt to
ever-changing service requirements.

Since network changes are directly related to service operations, any
successful change needs to not only ensure that new services are
provisioned smoothly, but also that existing services are not
affected and that no problems are introduced with the new
configurations. On one hand, these changes might still require
manual operation or iterative input from network Operators to provide
operational guidance and information about the goals, purposes, and
service instances that the network is to serve and refine the intent.
On the other hand, pre-programmed automation scripts or policy
templates may not be sufficient to realize the intent. Network
operators are, therefore, increasingly cautious about making network
changes. For example, they need to review the solution design as
well as evaluate all change impacts, before making any change. Then,
after the change, they need to perform dialing tests, monitor
traffic, and manually check table entries.

The Network Digital Twin (NDT)
[I-D.irtf-nmrg-network-digital-twin-arch] provides Network operators
with a safe sandbox for testing complex configuration changes,
software updates, or new protocols before they are deployed in
production so that the risk of accidental outages or service
disruptions can be greatly reduced.

In addition, Agentic AI introduces disruptive paradigm to the network
management which:

* Allows declarative intent interpretation and refinement with
multi-step task management,

* Enables dynamic goal-driven execution and adaptive behavior
instead of stepwise static instruction,

* And enables closed-loop autonomy across layer or across domain
instead of deterministic closed-loop automation.

Integrating a Network Digital Twin into network management together
with Agentic AI, it allows network management activities to
dynamically adapt to customer needs, network changes, as well as to
automatically assess, model, and refine optimization strategies under
realistic conditions but in a risk-free environment. An environment
that operates to meet these types of requirements is said to have AI
driven network operations.

AI Driven network operations provide the following capabilities to
applications by coordinating the components that operate and manage
the network:

* Service intent and service assurance work together to ensure that
the network change or network optimization aligns with business
goals and that the services provided meet the agreed-upon Service
Level Agreements (SLAs).

* Provide network capacity planning and ensure that the network has
sufficient capacity , resources, and infrastructure to meet
current and future demands.

* Provide simulation on fault scenarios, formulate recovery plans,
and verify whether the plans are applicable and effective so that
the service will not be affected during disaster recovery drill.

* Support fault and risk detection and provide network health check
and network risk check.

* Model the network configuration change and use a virtual topology
model to test network changes and assess the effect of the network
configuration changes on the network.

* Model the protocol operations and interactions among devices in
the network and simulate specific networking protocols such as IS-
IS, OSPF, BGP, SR, etc to understand how they perform under
different conditions.

* Model traffic flow across the network, including traffic
generation, flow control, routing, and congestion control and
evaluate traffic's impact on network performance.

* Support generation of repair solutions for potential network risks
and provide verification on the repair solution in seconds,
including loop, address conflict, and security policy conflict.

* Model the performance characteristics of each flow according to
traffic conditions change by using a virtual topology in order to
understand which flows and network portions can be potentially
affected by performance degradation and act to prevent it

This document describes an architecture for AI Driven network
operations, showing how these components work together with network
digital and AI capabilities. It provides a cookbook of existing
technologies to satisfy the architecture and realize intent-based
networking to meet the needs of applications.

2. Conventions and Definitions

The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and
"OPTIONAL" in this document are to be interpreted as described in
BCP 14 [RFC2119] [RFC8174] when, and only when, they appear in all
capitals, as shown here.

The document uses the following definitions and acronyms defined in
[I-D.irtf-nmrg-network-digital-twin-arch]:

* Network Digital Twin (NDT)

* Artificial Intelligence (AI)

The following acronyms are used throughout this document:

* Generative Artificial Intelligence (Gen-AI)

* Large Language Model (LLM)

* Retrieval-Augmented Generation (RAG)

* Agentic AI [I-D.hong-nmrg-agenticai-ps]

* Remote Code Execution (RCE)

Besides, this document defines the following terminology:

Network AI Agent: Network AI Agent is an autonomous system or entity
with awareness of its environment, capable of conducting analysis,
making decisions, and executing actions with specific intent based
on its knowledge representation to achieve a set of service goals
[TMF-1251D]. In addition, it is able of planning the tasks and
decompose the tasks into several sub-tasks and coordinate with
Task agent for these sub-tasks.

Task AI Agent: Task AI Agent is responsible for coordinating with
Network AI Agent in the Hybrid Agent System and executing specific
task assigned by Network AI Agent.

Autonomy: The ability to operate under uncertainty and adapt to
changing objectives without explicit external intervention.

3. Introduction of Concepts

3.1. Generative AI and Agentic AI

The integration of AI into network operations has marked a
significant leap forward in the pursuit of network automation and
intelligence, while generative AI further enhances the role of AI
driven network operations and management. Generative AI is a
subfield of AI that uses generative models such as Large Language
Models (LLMs) to generate new and original content such as text,
images, videos, or other forms of data with the capability to adapt
and make decisions to achieve specific goals.

Agentic AI refers to the broader category of AI systems that exhibit
"agency"—the ability to act independently and iteratively to perform
tasks without constant human prompting [I-D.hong-nmrg-agenticai-ps].
In the context of network operations and management, Network AI
agents are increasingly being designed to interact with physical
world and act upon it based on tools [Google-Agents-Whitepaper] and
perform network management tasks such as understanding user intent,
generating network configurations, diagnosing and resolving network
incidents [I-D.ietf-nmop-network-incident-yang]. Meanwhile, other
SDOs also try to define terms related to Network AI agent in the
context of network operations and management, e.g., TM Forum defines
Autonomous Agent in [TMF-1251D] as one of AN (Autonomous Network)
Terminologies.

3.2. Network Digital Twin

The Network Digital Twin is a digital representation that is used in
the context of network. The concept and architecture of the Network
Digital Twin are specified in
[I-D.irtf-nmrg-network-digital-twin-arch]. Three core functional
components which includes Data Repository component, a Service
Mapping Models component, and an NDT Management component are
introduced to characterize the Network Digital Twin and its reference
architecture.

The Network Digital Twin is widely recognized to be useful as an
advanced platform for network emulation, serving as a tool for
scenario planning, impact analysis, and change management. By
delivering applications requests to the Network Digital Twin through
standardized interfaces (see Section 9.4 of
[I-D.irtf-nmrg-network-digital-twin-arch]), the Network Digital Twin
exposes the various capabilities to network applications.

4. Characteristics of AI driven Network Operations

AIOPS was first defined by Gartner in 2016, combining "artificial
intelligence" and "IT operations" to describe the application of AI
and machine learning to enhance IT operations. However there is no
unified definition for characteristic of "AI driven network
operations" within the networking industry. Referring to the
characteristics of AIOPS in IT field and the characteristics of
networking itself, this document introduces six key elements (i.e.,
awareness, decision, analysis, execution, intent and knowledge) to
characterize the AI driven network operation and its use, as shown in
Figure 1. They together form a close-loop of network operation and
management.

+---------------------------------------------------+
| |
| +---------+ +-----------+ |
| | Intent | | Knowledge | |
| +---------+ +-----------+ |
| |
| -------- |
| +-----------+ //// \\\\ +-----------+ |
| | Analysis | |AI Driven Network| | Decision | |
| +-----------+ | Operations | +-----------+ |
| \\\\ //// |
| -------- |
| +-----------+ +------------+ |
| | Awareness| | Execution | |
| +-----------+ +------------+ |
| |
+---------------------------------------------------+

Figure 1: Six Key Elements to Characterize AI driven network
operation

* Intent: Intent is defined as a set of operational goals and
outcomes defined in a declarative manner without specifying how
to achieve or implement them in [RFC9315]. The Network AI
Agent must accurately interpret and understand the user's high-
level business or operational objectives, this involves
translating declarative requirements into specific network
instructions, e.g., configurations.

* Knowledge: The Network AI agent relies on a knowledge base that
includes network policies, historical data, expert experience,
extra-system experience (updates to LLMs/their implied
‘knowledge bases’) and Manually or semi-manually entered
knowledge, e.g., new equipment spec sheets,best practices in
product manual. The knowledge is used to inform its analysis,
decision-making, and execution processes. Over time, the
Network AI agent can expand its knowledge through machine
learning, incorporating new data and experiences to improve its
performance. For example, it learns which configurations are
optimal for specific scenarios or how to respond most
effectively to particular types of network incidents
[I-D.ietf-nmop-network-incident-yang].

* Analysis: The Network AI agent continuously analyzes vast amounts
of network data from various sources, including network
telemetry [RFC9232] and external feeds, and identify the gap
between user intent and the existing network status. By
integrating Network digital twin
[I-D.irtf-nmrg-network-digital-twin-arch] with Network AI agent
and leveraging machine learning and other data analytics
techniques, it also identifies network fault, problem,
incident, anomaly and perform data driven intelligent analysis
such as service impact analysis, and so on. Their distinction
is further discussed in [I-D.ietf-nmop-terminology].

* Decision: Based on the intent and network analysis, AI makes
informed decisions. By integrating network digital twin
[I-D.irtf-nmrg-network-digital-twin-arch] and AI, the
intelligence decisions making can be realized. These decisions
could involve dynamically adjusting network parameters, e.g.,
rerouting traffic to avoid congestion. The decision-making
process is driven by predefined policies, real-time data
analysis, and AI models (e.g., LLMs) that enable the Network AI
agent to choose the best course of action to meet the specified
intent. Network AI agent may also verify the correctness of
the decision outcome by performing some network simulation or
validation process.

* Awareness: Awareness is achieved through real-time monitoring and
data collection. The Network AI agent maintains a
comprehensive visibility of the network, enabling it to make
context-aware decisions. Network operators can also use the
awareness understand the exact cause of specific network issues
and achieve closed-loop decision-making.

* Execution: Once a decision is made, the Network AI agent executes
the necessary actions to implement it. This could involve,
e.g., sending configuration to network controllers or network
devices through NETCONF/RESTCONF protocols. The execution is
carried out in a controlled and precise manner to ensure that
the network behaves as intended without causing disruptions.
The Network AI agent also verifies that the executed actions
have the desired effect and makes the proper adjustments if
needed.

5. Architecture Design

5.1. Overall Architecture

Figure 2 provides the overall architecture for integrating Network
Digital Twin and Network AI Agent System. The components and
functional interfaces are discussed in Section 5.2 and Section 5.3,
respectively. The use cases described in Section 9 show how
different components are used selectively to provide different
services. It is important to understand that the relationships and
interfaces shown between components in this figure are illustrative
of some of the common or likely interactions; however, this figure
does not preclude other interfaces and relationships as necessary to
realize specific functionality.

+------------------------------------------------------------------------+
|Network Applications |
| +-------+ +-------+ +-------+ |
| | App 1 | | App 2 | ... | App n | |
| +-------+ +-------+ +-------+ |
+-------------------------^----------------------------------------------+
| Intent
+-------------------------+----------------------------------------------+
|Autonomous Domain | |
|+------------------------+-----------------------+ +----------------+ |
||Hybrid Agent System | | | Agent Fabric | |
|| +-------v--------+ | | | |
|| |Network AI Agent| | |+--------------+| |
|| +-------^--------+ | || Registration || |
|| | | |+--------------+| |
|| +------------+---+------------+--------+ <--->+--------------+| |
|| | | | | | ||Security&Trust|| |
||+------v---+ +-----v----+ +--------v------+ v | |+--------------+| |
|||Task Agent<->|Task Agent<->|Function Module|...| |+--------------+| |
||+----------+ +----------+ +---------------+ | ||Observability || |
|+-----------^-----------------------^------------+ |+--------------+| |
| | | |+--------------+| |
|+-----------v----------+ | ||Knowledge Base|| |
|| Network Digital Twin | | |+--------------+| |
|+-----------^----------+ | +----------------+ |
| | | |
|+-----------------------------------v---------------------------------+ |
||Physical Network | |
|| +---------+ +------------------+ +---------+ | |
|| | | | | | | | |
|| | NE | |NE(lightweight AI)| ... | NE | | |
|| | | | | | | | |
|| +---------+ +------------------+ +---------+ | |
|+----------------+----------------------------------------------------+ |
+------------------------------------------------------------------------+

Figure 2: An Architecture for Integrating Network AI Agent with
Network Digital Twin

5.2. Functional Components

This section describes the functional components shown as boxes in
Figure 2. The interactions between those components, the functional
interfaces, are described in Section 5.3.

5.2.1. Network Applications

Various network applications at the service level can effectively run
over a AI driven Network operation platform to implement either
conventional or innovative network operations, with low cost and less
service impact on real networks.

A network application may be a software tool that a user uses to make
requests to the network to set up specific services such as end-to-
end connections or scheduled bandwidth reservations or NOC
Application /Service AI Agent Application that is responsible for
monitoring, managing, and maintaining the health, performance, and
availability of complex networks.

Network applications make requests that need to be addressed by the
AI driven network. Such requests are exchanged through a northbound
intent interface (e.g., Restful API, Natural Language Programming
Interface(NLPI),A2A, A2A-T), so that they can be applied by Hybrid
Agent System at the appropriate twin instance(s).

5.2.2. Autonomous Domain

An autonomous domain is a self-governing network that integrates NDT
and AI driven capabilities to achieve autonomous network management.
It comprises the following sub-components.

5.2.2.1. Hybrid Agent System

Hybrid agent system acts as the smart brain of the Autonomous Domain,
which is responsible for conducting AI-based analysis and making
decisions regarding network management operations. It usually
comprises a Network AI Agent and multiple task agents or function
modules (e.g., Agent skills, network management functional APIs or
tools). It is worth mentioning that function modules could be the
functions and services of existing network management systems, it
offers a set of APIs and tools that can be consumed by agents. In
some simplified scenarios, the system may consist of only one single
AI Agent equipped with multiple function modules, e.g., skills and
toolsets that integrates both network-level coordination and task-
specific execution.

The Network AI Agent may coordinate cross-task-agent collaboration,
aligns tasks with user intent, and supervises the execution of each
task agent or function module. And task agents and function modules
are designed to perform specific functionalities, they could be
scenario-oriented and classified according to the function they
perform. The hybrid agent system can adapt to new circumstances
through access to evolving knowledge and reasoning, planning. It
leverages the inference of LLM, the simulation of Network Digital
Twin, and the contextual and domain-specific knowledge provided by
Knowledge Base to accomplish specific network operation task.

5.2.2.2. Agent Fabric

The Agent fabric, which serves as a central management hub, provides
essential services for the Hybrid Agent System, including agent
registration/discovery, authentication, observability, and knowledge
base.

5.2.2.2.1. Registration

AI Agents need to first discover each other and understand their
capabilities to collaborate. Agent Registration manages the process
by which new agents could join the system, making them discoverable
and available. It supports the unified registration of all AI agents
within the autonomous domain, including those residing on network
devices. Each Agent instance submits its own metadata information
including URI, supported authentication methods, and capabilities to
the Agent Registry. And the consumer Agent (e.g., the Network AI
Agent or task agent) could query or subscribe to the Agent Registry
to find appropriate Agents for task execution.

Agent skills [Agent-skills], introduced by Anthropic, provides a new
way for Agents to improve how they perform specific tasks through
folds that include instructions, scripts, and resources that are only
loaded when needed. Skills are folds containing a "skill.md" file,
Registration component may also work as a skill hub that enables
Agents to query directories for required skills.

[A2A] implements Agent Registration by providing the Agent Card
mechanism to ensure Agents from different vendors can register and
discover other Agents they need.

5.2.2.2.2. Security & Trust

The security component enforces trusted inter-Agent communication by
verifying the identity of AI Agents and enforcing security policies
throughout their interaction. It provides unified security
functionalities for all AI Agents within the autonomous domain,
including those residing on network devices. Some existing
authentication methods such as OAuth 2.0, allow to issue each AI
Agent its own authentication credentials to establish trusted
communication.

Standardized protocols like TLS (Transport Level Security) could be
leveraged to protect sensitive data exchanged between AI Agents.

It is also worth noting that once authenticated, authorization
defines the specific tools and data an agent can access, which often
using a Least Privilege access control method. It is also
recommended to log every Agent decision and tooling call to maintain
audit trail.

5.2.2.3. Observability

Observability component provides unified monitoring capabilities for
all AI agents within the autonomous domain and enables network
operators to gain deep insights into Agent behaviors.

OpenTelemetry(OTel) has emerged as a vendor-neutral approach for AI
Agent observability. It provides a consistent instrumentation layer,
semantic conventions, and telemetry export mechanisms, ensuring
end-to-end visibility across network AI agents, task AI agents, LLM
inference, and tool/skill executions.

By leveraging Semantic Conventions for GenAI [OTel-gen-ai], the
system unifies the collection of LLM inference metadata, token
consumption, reasoning processes, and skill invocation events, etc.
This allows comprehensive tracking of agent runtime status, cross-
component interaction flows, and abnormal behaviors in both multi-
agent collaboration scenarios and simplified single-agent deployments
with integrated multiple skills and tools.

5.2.2.3.1. Knowledge Base

The Knowledge Base serves as a crucial repository of information
within the architecture. It enables the injection of expert
knowledge and chain of thoughts, provides the necessary knowledge and
memory that helps Agents make more accurate and practive context-
aware decisions. It also helps mitigate the hallucination problems
that can arise in large-scale models, which enhances the accuracy of
task execution. Additionally, the Knowledge Base plays a key role in
providing the data needed for techniques like Retrieval-Augmented
Generation (RAG), which further boosts the system's ability to
generate reliable and relevant outputs.

In case of coupling MCP [MCP] with the network management system, the
new knowledge also can be used to support modification of the
currently operating automation Closed Loop, such as: - Choice of
tools (data, analytics, algorithms/decision processes, closed loops)
- Orchestration of tools

5.2.2.4. Network Digital Twin

A Network Digital Twin provides an enhanced and optimized solution in
the face of increasing network and business types, scale, and
complexity. It simulates the behavior, performance, and
characteristics of the actual network, which could help in validation
and testing scenarios, analyzing and predicting network behavior
without affecting the real physical network.

As described in Section 7 of
[I-D.irtf-nmrg-network-digital-twin-arch], the core functional
components of an Network Digital Twin includes Data Repository,
Service Mapping Models, and a Network Digital Twin Management
component. The Network Digital Twin collects the real-time
operational and instrumentation data from network through the
appropriate real network-facing input interfaces, and it delivers NDT
services through appropriate application-facing output interfaces,
which is the interfaces to Network AI Agent(s) in Figure 2.

5.2.2.5. Physical Network

This is the actual hardware and infrastructure that makes up the
network, which includes a set of network devices and wiring. In a
physical network, Network Elements (NEs) with Lightweight AI
[I-D.irtf-nmrg-ai-challenges] or AI Agent may also achieve some local
close loop without relying on human intervention. It is also
possible for Lightweight AI or AI Agent to coordinate with other AI
Agent(s) to enhance the automation and efficiency of network
operations. The Network Lightweight AI models could be trained,
validated, deployed, and executed on Network Elements, and further
refined (e.g., model re-training) through monitoring and continuous
optimization based on feedback from LLM.

5.3. Functional Interfaces

This section describes the interfaces between functional components
that might be externalized in an implementation allowing the
components to be distributed across platforms. Where existing
protocols might provide all or most of the necessary capabilities,
they are noted.

As noted in Section 5.1, it is important to understand that the
relationships and interfaces shown between components in Figure 2 are
illustrative of some of the common or likely interactions; however,
this figure and the descriptions in the subsections below do not
preclude other interfaces and relationships as necessary to realize
specific functionality. Thus, some of the interfaces described below
might not be visible as specific relationships in Figure 2, but they
can nevertheless exist.

5.3.1. Human Oversight (Human on the Loop)

The architecture allows human experts to monitor, guide, approve, or
intervene in the AI driven network operations. Human may provide
guidance and make critical decisions when necessary. By involving
human in the process, the architecture can leverage their insights
and experience, ensuring AI actions align with organizational goals.

Human on the loop is also helpful to provide a safeguard for complex
or sensitive decisions, where human judgement is essential to avoid
potential errors or ethical dilemmas.

This typically uses natural language as the primary mode of
interaction, a chat platform that allows for conversational
interaction with AI Agents can be leveraged. In some scenarios,
operators may use structured format for strategy injection via
workflows. Protocols like A2A [A2A], and RESTful API can be
leveraged.

5.3.2. Application to Network AI Agent Interface (Intent based
Management)

Intent based Network Management helps in delivering application
requests to the AI Driven network operation platform and exposing the
various platform capabilities to network applications.

Standardized protocols and interfaces facilitate smooth communication
between applications and AI driven network operation platform and
ensures different systems from various vendors can work together
seamlessly. The interfaces between Network applications and Network
AI Agent can adopt IG1453 Agent to Agent Protocol for Telecoms (A2A-
T) [A2A-T] specified by TM Forum.

5.3.3. Network AI Agent to Task AI Agent Interface (Single Autonomous
Domain)

This interface governs the coordination and task delegation within
the Hybrid Agent System of a single Autonomous Domain. The Network
AI Agent, acting as the principal coordinator, uses this interface to
decompose high-level goals into specific tasks and assign them to
specialized Task Agents (e.g., for configuration generation or fault
diagnosis). It facilitates communication for task assignment,
progress monitoring, and result aggregation. This coordination can
be implemented using protocols like [A2A-T].

5.3.4. Network AI Agent to Network AI Agent Interface (Cross Autonomous
Domain)

This interface enables collaboration and information exchange between
Network AI Agents residing in different Autonomous Domains. It is
essential for scenarios requiring end-to-end service assurance or
coordinated optimization across multi-domain networks. Through this
interface, Network AI Agents can negotiate resource allocation, share
summarized domain-specific insights (while preserving detail
isolation for privacy and scalability), and coordinate actions to
fulfill cross-domain objectives. Standardized protocols like A2A-T
[A2A-T], designed for agent interoperability in telecommunication
area, are candidate technologies for implementing this cross-domain
interface, ensuring secure and reliable interaction between
autonomous systems from different administrative domains.

5.3.5. Hybrid Agent System to Agent Fabric Interface (Agent Lifecycle
Management)

The interface between Hybrid Agent System and Agent Fabric serves as
the management bridge which encompasses a set of services designed to
manage the lifecycle of AI agents, and collaborative capabilities of
AI Agents such as Registration, Security and Trust, Observability.

* Registration Registration handles Agent onboarding, lifecycle
tracking (e.g., heartbeat monitoring, status updates), and
capability-based Agent discovery. Interfaces like RESTful APIs
with structural schema for AI Agents metadata description could be
leveraged. Protocols like A2A [A2A] Agent card mechanism may also
be used to ensure interoperability among different Agent vendors.
It is also worth noting that message queue mechanisms such as
Kafka could also be a candidate interface for asynchronous
communications for agent registration and discovery. This
interface also supports skill management, including skill query,
loading, unloading, and version control, enabling agents to
dynamically obtain and use skills provided by the Registration
component.

* Security and Trust Security and Trust ensures trusted inter-Agent
communication by verifying the identity of AI Agents and enforcing
security policies throughout their interaction. Protocols like
Transport Layer Security (TLS) could be leveraged for in-transit
data Protection. While OAuth 2.0 and OpenID Connect are
increasingly used to authenticate AI Agents.

* Observability The observability component enables the network
operators to gain deep insights of agent behaviors. It collects
audit logs, metrics and traces of each agent and provides
visibility of task process. In addition, to make the behavior of
Hybrid Agent System declarative and traceable, it is essential to
have the capability to distinguish whether an operation action is
taken by agents automatically or by human operators. For example,
in the fault management scenarios that need on-site repairing, the
demarcation and locating task could be accomplished by agents, and
the repair solution implementation and verification task is
accomplished by human operators, maybe with the help of
intelligent copilot. The traceable logs are essential for both
clarifying the duty boundary between agents and human operators,
and also for evaluating the effectiveness of the agentic system.

The interface between AI Agent and Knowledge Base is specified in
Section 5.3.7.

5.3.6. Hybrid Agent System to Network Digital Twin Interface

The interface between Hybrid Agent System and Network Digital Twin
are the application-facing interface as defined in
[I-D.irtf-nmrg-network-digital-twin-arch]. Furthermore, the Model
Context Protocol (MCP) [MCP] can be leveraged to standardize this
interaction, enabling the NDT to expose its simulation and analysis
capabilities as a set of discoverable "tools" that the AI Agent can
dynamically invoke. This MCP-based approach facilitates seamless
integration and richer contextual exchange between the Agent and the
NDT.

5.3.7. Hybrid Agent System to Knowledge Base Interface

Knowledge Base service provides contextual data and insights to
enhance the decision-making accuracy of the Hybrid Agent System.

Interfaces such as Cypher or SPARQL with schema-defined data models
(e.g., LPG or RDF for knowledge representation) allow efficient
retrieval and updates. Other high-throughput interfaces such as gRPC
or RESTful API can be the candidate for synchronous semantic search
queries. For large-scale knowledge operations, asynchronous data
message systems (e.g., Kafka) can also be employed for data ingestion
and real-time knowledge synchronization across distributed Agents.

Additionally, the Model Context Protocol (MCP) [MCP] could also serve
as a standardized interface for AI Agents to dynamically access and
utilize a wide range of tools and data sources provided by the
Knowledge Base. It enables the Knowledge Base to expose contextual
information, expert rules, and external data as "tools" that Agents
can invoke, significantly enhancing their reasoning and problem-
solving capabilities.

5.3.8. Hybrid Agent System to Physical Network Interface

The interface between the hybrid agent system and the physical
network is established via its internal function modules interacting
with network elements (NEs). AI agent can invoke function modules to
perform specific network operations.

5.3.8.1. Data Collection

Data Collection interface is responsible for gathering data from the
physical network through various different tools and methods (e.g.,
IPFIX [RFC7011], YANG-push [RFC8639],[RFC8641], and BMP [RFC7854]).
It collects various types of network data including configuration
data, operational data, network topology, routing data, logs, and
trace on management plane, control plane, and forwarding plane as
needed. The collected data is fed into the Network Digital Twin and
hybrid agent system to provide with up-to-date information about the
current state of the physical network.

5.3.8.2. Configuration

Once network decisions are made and confirmed, the Hybrid Agent
System performs specific actions to the physical network, e.g.,
modify specific configuration through protocols like NETCONF
[RFC6241] and RESTCONF [RFC8040], which utilize YANG [RFC7950] as the
data modeling language. It is the component that makes the planned
control and management changes a reality in the real physical
network.

5.3.8.3. Lightweight AI and Large AI Model Collaboration Interface

Collaboration between small AI model and large AI model is also
designed to be supported by this interface.

In the past, we only support AI and machine learning technologies at
the network level, e.g., we can use collected various different
network data to provide network analysis and generate network
insight. With more intelligence introduced into the network element,
more GPU/NPU resource can be allocated for AI inference, this make
collaboration between large AI model and small AI model possible.

Large AI models can provide basic logical reasoning and generalized
analytical decision-making capabilities While specialized small AI
models can provide efficient problem-solving capabilities in
specialized areas. The synergy between the two allows the AI agent
to combine both multitasking generalization capabilities and domain
expertise, thus minimizing the reliance on human intervention in the
network management process.

On one hand, we can use accumulated field engineering expertise to
train large AI model into one foundation model for fault management
AI agent, On the other hand, we can deploy small AI model, leverage
hardware resource or chipset resource in the intelligent network
element to collect more fine granularity data or provide local
processing for Collected data and summary report generation, Trend
prediction, etc. When small AI model is outdated and unable to
detect specific applications or security risk, these specific
applications and security risk information can be collected by
network analytics platform to retrain this small AI model and re-
deploy it in the same network element when this small AI model has
been trained to work correctly to detect applications or security
risk.

With collaboration between large AI model and small AI model, we can
allow Network AI Agent within the Network controller interact with
network element and has more quick response to network change.

This collaboration, facilitated by APIs or agent communication
protocols like A2A [A2A], combines the generalization power of large
models with the efficiency and low-latency of specialized small
models, leading to quicker and more context-aware responses to
network change.

5.3.9. Feedback-driven Improvement Interface

The architecture should incorporate mechanism for continuous
improvement based on feedback. This includes collecting data on AI
decisions, network performance, and user feedback to identify areas
for enhancement. By analyzing the feedback, the system can adapt and
optimize its operations over time, leading to better performance and
more accurate decision-making. For example, if a Network AI Agent
fails to accurately identify the exact cause of a network incident,
the relevant records can be submitted as negative samples to the LLM
which provides inference services, this allows the LLM to be trained
on these negative samples for optimization.

This interface is implemented through a combination of system
interfaces that collect, process, and apply feedback. Operational
feedback—including the outcomes of AI decisions, network state
metrics—is collected as structured data via system logging streams
(e.g., in JSON format) and message queues (e.g., Kafka). This data
is then consumed by analytics components and machine learning
platforms through APIs (e.g., RESTful, gRPC) to refine AI models, for
instance, by using failure records as negative samples for fine-
tuning. Subsequently, optimized models and updated knowledge are
deployed back into the runtime system via model serving and
configuration management interfaces, closing the improvement loop.

6. AI Driven Network Operations: Relationship Between Characteristics
and Functional Components

The architecture in Figure 2 provides a concrete implementation
framework to realize the six key characteristics of AI-driven network
operations described in Section 4. Each characteristic is directly
supported by specific functional components within the Autonomous
Domain. The following clarifies how the architecture operationalizes
these characteristics:

* Intent: The Network Applications Layer conveys a high-level user
intent via northbound interfaces. The Network AI Agent
interprets this intent and translates it into actionable
network operation tasks to each task Agent.

* Knowledge: The Knowledge Base in Agent Fabric serves as the
central repository for domain-specific knowledge, expert rules,
and historical data. It provides the necessary context and
long/short memory to support accurate decision-making by task
Agents.

* Analysis: The AI Agent in Hybrid Agent System performs
intelligent analysis using data and tools. It leverages the
Network Digital Twin to simulate and validate scenarios,
enabling data-driven insights and gap analysis between intent
and current network state.

* Decision: The AI Agent in Hybrid Agent System makes informed
decisions based on its analysis results. It utilizes the
Network Digital Twin for risk-free validation before finalizing
decisions. The decision may be sent to human operators for
confirmation before actions are taken.

* Awareness: The AI Agent in Hybrid Agent System gathers data from
the Physical Network, it may also fetch data from the Network
Digital Twin which maintains a dynamic, virtual representation
of Physical Network. Together, they provide comprehensive
network visibility and context-aware awareness.

* Execution: ：The AI Agent in Hybrid Agent System implements
validated decisions by applying configurations or control actions
to the Physical Network via southbound interfaces such as NETCONF,
RESTCONF, or Model Context Protocol [MCP].

7. AI Agent Registration and Team formation

+----------------+
|Agent Fabric |
+-----------+ | +------------+ |
| Network | Register | | | |
| AI Agent +--------------------+-> | |
+-----------+ | |Registration| |
|---------------+----------------+---------+-> | |
Register Register Register | | Center | |
| | | | +------------+ |
+------+----+ +------+-----+ +-----+----+ | |
| Fault | |Optimization| | Change | | |
| Task Agent| |Task Agent | |Task Agent| ...| |
+-----------+ +------------+ +----------+ +----------------+

Figure 3: AI Agent Registration and Team formation Usage Example

The Agent Fabric at the network level provides agent registration for
both embedded AI agent in each network element and network AI agent
and associated task agents. The following steps are performed to
provide Event driven AI Agent Team formation within the Agentic AI
network management architecture:

Step 1: Human Operator pre-provision user intent which comprises
objective, incident list and corresponding action list.

Step 2: Network AI Agent receieves user intent and generate
corresponding policies which comprise objective, incident list and
corresponding action list. In addition, Network AI Agent
subscribe corresponding incidents from incident center.

Step3: Network AI agent generate task planning based on objective
and then discover matched task agent lists based on planned task
from registration center within the agent fabric.

Step4: Upon receiving incident from the incident center, network AI
agent assign the tasks to task agents corresponding to specific
incident received.

Step 5: Network AI Agent distribute task to corresponding task
agents and complete task agent team formation.

8. Agent to Agent Communication Security

Artifacts Delivery
+-----------+
| | +----------------+
| | Agent Fabric |
| +--V--------+ Agent Identity Discovery | +------------+ |
| | Task <---------------------------+-> Agent | |
| | AI Agent <---------------------+ | | | |
| +-^------^--+ Authentication | +--+->Registration| |
| | | Handshake | | | | | |
| Secure | Stateful | | | | Center | |
| Task | Monitoring | | | +------------+ |
Negotiation| | | | | |
| | | Agent Identity | | | +-------------+|
| +-V------V--+ Discoverey +--+--+-> Agent ||
| | Task +------------------------+ | |Authorization||
+--------> AI Agent <---------------------------+-> Server ||
+-----------+ Authentication | | ||
Handshake | +-------------+|
+----------------+

Figure 4: Agent to Agent Communication Security Usage Example

The following steps are performed to provide Agent to Agent
Communication Security within the Agentic AI network management
architecture:

Step 1. Discovery via Agent Card: The workflow begins when a client
agent requests the Agent Card from the remote agent's /.well-
known/agent.json endpoint. This JSON file acts as a secure
manifest, declaring the agent’s identity, capabilities, and
required security schemes.

Step 2. Authentication Handshake: Before any task is sent, the
client must fulfill the authentication requirements listed in the
Agent Card. This typically involves an OAuth 2.0 flow where the
client obtains a JSON Web Token (JWT) to prove its identity and
permissions.

Step 3. Secure Task Initiation: :Communication is established over
HTTPS/TLS. The client sends a tasks/send request using JSON-RPC 2.0.
The server validates the token and authorizes the specific task based
on the client's role.

Step 4. Stateful Monitoring & Feedback: :The task moves through a
strictly defined lifecycle (submitted → working → completed).
Security is maintained throughout as updates are streamed via Server-
Sent Events (SSE) or webhooks, each tied to the unique, authorized
Task ID.

Step 5. Artifact Delivery: Final results (Artifacts) are delivered
only after the task reaches a completed state. These are
structured objects (text, files, or data) returned to the verified
requester, ensuring data integrity and preventing unauthorized
access to output.

9. AI Driven Network Operations: A collection of Use Cases

Network AI Agent could help in the following phases which are usually
mentioned in network management:

* Network Planning and Design: includes the understanding of user
intent, generation of solutions, and simulation for decision-
making.

* Service Deployment: includes the construction of the physical
network, as well as intent understanding, pre-deployment
simulation, automated configuration, post-deployment validation,
and other capabilities to enhance the efficiency and accuracy of
network configuration for service deployment.

* Network Monitoring and Troubleshooting: includes intent
monitoring, issues identification, solution generation, evaluation
and decision-making, solution implementation, and service
validation.

* Network Change and Optimization: involves the design, evaluation,
decision-making, implementation, and validation of network
configuration changes or optimizations to improve network
operation efficiency.

In all phases and use cases, after the Agent performs specific
action, it always continuously monitors the network by data
collection. Based on the result of network running analysis and user
explicit feedback, it may adjust and optimize the management strategy
if necessary.

9.1. Multi-Agent Collaboration on Network Configuration Change

Network configuration changes are needed in scenarios such as
optimizing network or service performance, provisioning new network
services, or resolving network incidents/faults.

+------------+
Network | Service |
Operator -----> AI Agent |
| |
+-----+------+
|Configuration +---------+
|Change Intent | |
+-------V---------+ | |
|Network AI Agent | | |
| Goal | | |
+-------| Task-1,Task-2 +------------------> |
| | ... Task-n +-------+ | Network |
| +-------+---------+ | | Digital |
| | | | Twin |
| | | | Task |
+-----V------+ +-----V-------+ +------V----+ | Agent |
| Config | | Config | | Config <-----> |
| Generation | | Distribution| |Validation | | |
| Task Agent | | Task Agent | | Task Agent| | |
+------------+ +-------------+ +-----^-----+ +---------+
|
+------V------+
| Resource |
| Allocation |
| Task Agent |
+-------------+

Figure 5: Intent Based Network Configuration Change Usage Example

Network configuration change leveraging Network AI Agent and Network
Digital Twin may experience the following typical steps:

Step 1: The network operator inputs the intent of network
configuration change into the Network AI Agent using natural
language. The network operator may simply explain the objectives
and requirements of the changes.

Step 2: Network AI Agent first verifies the identity of the user
requesting the change and checks the user's permissions to make
certain types of network changes against predefined rules or
policies. It then understands and parses the initial intent of
the request, by leveraging the powerful knowledge and reasoning
capabilities of LLM and decompose the tasks into configuration
generation task, configuration distribution task, configuration
validation task and assign to corresponding task agents.
Configuration generation Task Agent first generates initial
suggestions for specific network configuration update, which may
include multiple possible network configuration change plans if
possible.

Step 3: Network AI Agent further communicates with the Configuration
Validation task agent and Network Digital Twin task agent to
validate the suggested configuration change, including the syntax
and semantics of the configuration, verification of effected
application and resources. The network digital Twin task agent
may generate a report indicating the validation result, and
suggested configuration fix when the validation fails after
network simulation leveraging the current physical network
operational state.

Step 4: Network AI Agent may generate a configuration change plan
and submit to the network operator for approval. Based on the
feedback from the operator, Network AI Agent then further decides
whether to optimize the change plan or deliver the plan to the
Configuration Distribution task agent to conduct the physical
network configuration change. The configuration distribution task
agent may further communicate with resource allocation task agent
to obtain network resource (e.g.,vlan, IP subnet) allocated by
resource allocation task agent.

9.2. Multi-Agent Collaboration on Network Troubleshooting

Figure 6: Intent based Network Troubleshooting Usage Example

The network operator inputs the intent of network configuration
change into the Network AI Agent using natural language. Network AI
Agent could plan and decompose network troubleshooting tasks and
coordinate with fault identification task agent, fault diagnosis task
agent, fault repair task agent and fault prediction task agent to
assist in network troubleshooting in the following significant
aspects:

* Fault Identification: Network AI Agent coordinates with fault
identification task agent continuously monitors and aggregates
data from various sources, the comprehensive data collection
provides a holistic view of the network operational state. By
analyzing the real-time data, fault identification task Agent
could detect network anomalies swiftly, which enables the
prompt identification of potential issues before they escalate
into major faults, minimizing downtime or service disruptions.
In some cases, the Lightweight AI located in the Network
Element may handle some simple fault identification tasks
(e.g., optical module fault automatic identification) to
enhance the awareness, while the fault identification task
agent and LLM could leverage their powerful processing
capabilities to analyze the time-domain data collected from the
optical module.

* Fault Diagnosis: Once a fault is identified, Network AI Agent
coordinate with fault diagnosis task agent to delve into
diagnosing the exact cause, fault diagnosis task agent may also
invoke some existing operations such as "incident-diagnose" RPC
defined in [I-D.ietf-nmop-network-incident-yang]. By
correlating symptoms and/or applying AI models trained on
historical data, fault diagnosis task agent can narrow down the
potential causes and pinpoint the exact cause, which
accelerates the diagnosis process and reduces the time needed
to address the issue.

* Fault Repair: After diagnosing the fault, Network AI Agent can
coordinate with fault repair task agent to generate targeted
repair solutions. These solutions range from specific
configuration adjustments to more complex fixes (e.g., hardware
replacement). Fault Repair task Agent would also communicate
with the Network Digital Twin task agent to simulate the
proposed repair solutions and get feedback from the Network
Digital Twin task agent. In advanced setups, fault repair task
agent may automatically execute these repairs, ensuring quick
restoration of normal operations and enhancing the overall
reliability and efficiency of network management. But the
fault repair task agent may also first present the fault
details and repair advice to the network operator for review,
and proceed to carry out the repair task once it is confirmed.

* Fault Prediction As an advanced enhancement of fault management
capabilities, fault prediction aims to reduce network risks
through proactive management that prevents problems before they
occur. Before a fault actually occurs, the fault prediction
task agent can coordinate with network digital twin task agent
to construct a dynamic simulation model by collecting real-time
multi-dimensional operational state data, including network
topology, traffic load, and device performance indicators.
Based on the network data, the fault predication task agent
uses large models and machine learning algorithms (such as
time-series prediction models and anomaly detection models) to
reason and analyze potential faults—for example, predicting the
risk of physical link interruption based on optical cable
signal attenuation data. Furthermore, the fault prediction
task Agent generates recommended operations to avoid faults and
validates them through simulation in the network digital twin
task agent, thereby achieving predictive maintenance of the
network.

9.3. Multi-Agent Collaboration on Network Optimization

Figure 7: Intent based Network Optimization Usage Example

Network optimization is often introduced due to the Network AI
Agent's awareness of some potential network faults or anomalies
through continuously monitoring of network operational state, e.g.,
AI models may predicts network congestion by analyzing historical and
real-time network traffic data. It may also be triggered by the
network operator actively inputting the network optimization intent.

Based on the analysis of network data and user's intent (if any),
Network AI Agent collaborate with Optimization Solution Generation
Task Agent to propose network optimization strategies. For instance,
once the network congestion sometime in the future is predicted, it
may proactively optimize the network configuration, or suggest
scaling up to meet specific demands.

Before the network optimization is conducted, Network AI Agent
coordinates with the network digital twin task agent to implement and
evaluate the optimization solution using the Network Digital Twin
platform. This may need repeated trials and validations based on
specific evaluation criteria, before the optimal strategy could be
selected. Network AI Agent may also first present the suggested
network optimization solution to the network operator for review, and
apply it to the physical network through optimization solution
distribution task agent after obtaining approval from the network
operator.

9.4. Network level Energy Efficiency Management in the IP+Optical
network

Figure 8: Intent based Network level Energy Efficiency Management
Usage Example

Network level Energy Efficiency refer to a set of processes used to
discover a inventory of capabilities, use specific metrics to monitor
and assess energy consumption of the entire IP+Optical network ,
operate, and control the use of available energy in an optimized
manner while achieving the network’s functional and performance
requirements by improving overall network utilization.

Multi-Domain AI Agent can work together with network AI Agent in each
autonomous domain to allow network operators not only see real time
energy consumption in the network devices of large scale network
through interaction with the GREEN Network AI Agent, but also allow
them see

o which network devices enable energy saving, which devices not,
which are legacy ones,

o The total energy consumption changing trend over the time of the
day, for all network devices,

o Energy efficiency changing trend over the time of the day for the
whole network.

On the other hand, With the end to end observability to energy
consumption statistics data and energy efficiency statistics data,
the Network AI Agent in each autonomous domain can collaborate with
network digital twin to know which part of the network need to be
adjusted or optimized based on network status change.

9.5. Network Security Drills (Human on the Loop)

+----------------+ Human
| Agent Fabric | Operator
+-----------------+ | | /---------\
| Network AI Agent| |+--------------+| | Analyze |
| Goal | || || | Define |
| Task-1,Task-2 <-->|Observability |<-> Inject |
| ....Task-n | || || \---------/
+-------+---------+ |+--------------+|
| | |
| +---------^------+
| |
+-------V------ ---------------V----------------+
| Dynamic Attack and Defense Verification System|
| Based on Network Digital Twin |
| +---------+ +---------+ |
| | Dynamic | | Dynamic | |
| | Security| |Security | |
| | Attack | |Defense | |
| | Task | | Task | |
| | Agent | | Agent | |
| +---------+ +---------+ |
+--------------------^--------------------------+
|Data Collection
+--------------------+--------------------------+
| Network Infrastructure |
+-----------------------------------------------+

Figure 9: Intent based Network Security Drill Usage Example

The human operator can work together with the Network AI Agent to
conduct Network security Drill. The human operator can instruct the
Network AI Agent with specific injection policy to work with network
digital twin help construct a dynamic attack-defense verification
system in network security drills through NDT and AI reasoning
capabilities. The dynamic attack-defense verification system
comprise dynamic security attack task agent and dynamic security
defense task agent which are responsible security risk attack task
and security risk defense task respectively assigned by the network
AI agent. The dynamic security attack task agent uses generative AI
to automatically generate diversified attack paths, models network
topologies with graph neural networks, covers attack stages such as
reconnaissance and penetration, and dynamically adjusts strategies
via reinforcement learning to simulate the adaptive characteristics
of network attacks. The virtual range built based on the NDT can 1:1
map the production environment, supporting simulations of composite
scenarios like ransomware chain attacks and supply chain attacks —
such as simulating the entire process of Contivirus laterally
penetrating to domain controllers through weak passwords.

During drills, Human operator can instruct the Network AI Agent to
work with the dynamic security defense task agent to automatically
deploy virtual environments with vulnerabilities, collect defense
response data in real time through NDT, and generate attack path
heatmaps and repair suggestions. This capability can further verify
emergency response processes, inject real-time threat intelligence to
dynamically update drill scenarios, and simulate end-to-end automated
deployment, vulnerability injection, and real-time analysis of
security drills, enhancing the proactive verification ability of
defense systems against real-world threats.

10. Challenges of Integrating Network Digital Twin and Agentic AI into
Network Management

In addition to the research challenges in coupling AI and network
management specified in [I-D.irtf-nmrg-ai-challenges], this document
also identifies some challenges that need to be considered when
integrating agentic AI together with network digital twin for network
operations.

10.1. Make IETF developed YANG models AI ready

Network management has evolved towards model-driven programmability
using YANG (RFC 7950). While YANG provides a rigorous structure for
data, it was designed for deterministic software clients rather than
the probabilistic reasoning of Large Language Models (LLMs).
Consequently, AI agents often struggle to navigate complex YANG
hierarchies, leading to "hallucinations" or incorrect configuration
logic.

The emerging Model Context Protocol (MCP) provides a framework for
connecting AI models to external tools and data. However, there is
currently no standardized way to map YANG primitives to MCP
components (Resources, Tools, and Prompts). This gap results in:

o Semantic Ambiguity: LLMs lack the contextual hints needed to
understand the operational impact of specific YANG leaves.

o Interoperability Barriers: AI agents require custom "glue code" for
every vendor's unique interpretation of how a YANG model should be
exposed to an AI.

o Scaling Issues: Massive YANG schemas exceed LLM context windows,
requiring standardized methods for sub-schema discovery and pruning.

10.2. Trust and Security

Multi-Agent Collaborations and interactions can be break down into 4
typical scenarios:

o Human operator-> AI Agent-> APIs/Tools/APIs/LLMs

In a single-agent scenario, Human operators access services through
the network management AI agent. The network management AI agent has
multiple functions (fault and optimization), and authentication is
required to prevent users from Performing unauthorized actions by
exploiting privilege vulnerabilities,e.g., accessing the optimization
function interface when they only have fault agent permissions.

o AI Agent -> API Services

In a single-agent scenario, the network management AI agent triggers
tasks automatically based on trace and log information. In some
cases, the logging or decision-making process cannot be traced.

o Human operator-> AI Agent -> multiple AI Agent

Multiple agents may call each other. For example, if a faulty agent
A calls an optimization agent B, authentication is required to
prevent Manipulating communication channels of agents to influence
decision-making processes.

o External AI Agent ->AI Agent-> APIs/Tools/APIs/LLMs

External AI agents can directly access the network management AI
Agent by simulating human operation through interface protocols. For
example, a customer AI agent can access the Network AI Agent through
a northbound interface protocol such as A2A, MCP. In some case,
there might have target flaws in protocols like MCP or A2A;
e.g.,consent bypass, context hijacking, etc.

Ensuring robust security throughout the entire AI-based network
management architecture is essential to prevent unauthorized access
and maintain the security of the network infrastructure. The
security risk can be break down into the following cases:

o External system interacts with AI agent

Human Operators or external systems bypass their privileges to
operate the Network management AI agent

* Human operators operate through a network management AI agent, but
the service scope that the AI agent can handle may exceed the
user's authorized privileges, leading to unauthorized access.

* Human operators manipulate business operations using agent
delegation and authorization.

* Human operators Coerce intelligent agents to manipulate users into
performing covert operations.

o AI Agent Interact with Tools/APIs/LLMs

* Interact with Tools/APIs/LLMs with Privilege Escalation

Since network management AI agents rely on LLM for inference when
accessing APIs and tools, there is a possibility of malicious
injection scenarios where the APIs accessed by the agent exceed
the expected scope,e.g.,Using AI to generate execution
environments and inject malicious code.

* Interact with Tools/APIs/LLMs without audit

When an agent interacts with an API or tool, the logs are
currently recorded as system logs, which cannot distinguish
between different agents, the logging or decision-making process
for specific agent cannot be traced and pose a risk of
repudiation.

o Multiple Agent Collaboration and Communication

* Multi-Agent Communication with Privilege Escalation

When agents communicate with each other via intent communication
and understanding, there is a cascading permission amplification
problem,e.g.,AI-generated false information disrupts the reasoning
process , leading to Privilege Escalation.

* Interaction with internal AI Agent

As a microservice, a network management AI agent can be accessed
by other services or AI agents,e.g., Performing unauthorized
operations by exploiting authentication vulnerabilities,which
poses a risk of Privilege Escalation.

10.3. Protocols between Agent and Agent/Human operator/Tools

10.3.1. High Risk Operations

"network change" in the network management field refers to the
modification, adjustment, or configuration of physical or logical
resources in the existing network. Because these operations directly
affect the continuity and stability of existing network services,
even minor unauthorized access or errors can lead to large-scale
network outages. In case of high risk operation, the following
measures should be taken into account.

o Regardless of whether the initiator of the operation is a human or
an agent, each network change request must be re-authenticated.

o For operations involving "bulk deletion" or "core route changes",
the system must require approval from a second high-privilege account
before issuing the command or enforcing the policy.

o High-risk configurations should be tested on a very small scale
first. The system should automatically monitor the indicators, and
if any abnormalities are detected, the configuration should be
automatically rolled back within milliseconds.

10.3.2. The Timeliness Requirements of Collaboration

For real-time network operation and maintenance scenarios with high
real-time requirements, such as scheduling strategy optimization and
critical fault repair, the rapid generation of network optimization
decisions is crucial. However AI Agents based on large models adopt
a "Token-based" generation and reasoning approach, which is limited
by computing power and algorithms, resulting in generally slow
reasoning speeds. In addition, the simulation and verification
process of Network Digital Twin (NDT) further increases decision
latency, which leads to long end-to-end decision-making time in
complex scenarios and is difficult to meet the real-time requirements
of services.

Also tasks such as fault diagnosis, complaint handling, and user
experience improvement often have strict time constraints. For
example, if a fault is not resolved within a set time, it can trigger
an escalation of the complaint, requiring efficient collaboration
among multiple agents. In addition, In a network management
environment, you might need agents to subscribe to real-time network
alarms or telemetry events. However Google-initiated A2A protocol
primarily follows a task-oriented "request-response" model and
doesn't support a native pub/sub or event-driven architecture.

To improve decision efficiency, continuous efforts are needed in
lightweight NDT modeling algorithms, optimizing large model reasoning
frameworks (such as quantization technology and parallel computing),
and deploying high-performance AI acceleration hardware.

10.3.3. Collaboration Reliability

Fault diagnosis and complaint handling in the network management
field are complex tasks, typically involving 10 to 20+ fields for one
single message exchange between two AI Agents and requiring a high
level of expertise. In addition, Reliable task collaboration is
incomplete and not sufficient for network management field, e.g.,
Google-initiated A2A protocol doesn't defined handling strategies for
task rejection, missing information during task collaboration, and
failure to achieve task objectives.

also In network management area, data sources can be diverse and
heterogeneous, leading to potential issues such as data
inconsistencies, missing, or outdated data. Poor-quality data may
result in inaccurate AI predictions and decisions. For example, if
incorrect or outdated network configuration data is provided, the
model may provide incorrect repair advice when diagnosing network
incidents or faults, it may suggest checking an non-existing
interface. Ensuring that data is properly cleaned, validated, and
maintained is a significant challenge in providing reliable inputs
for AI-driven network management.

10.4. Agent Observability,Intervention and Control

Network operations are increasingly autonomous with the growth of
network management Agent applications at the network level and
service level. Since AI native operations may be non-deterministic,
when network management agents misbehave or deviate from what Agents
are expected to do, Current AI safety technologies, often referred to
as "AI guardrails" are introduced to constrain the behavior of AI
agents within operational and compliance boundaries, prevent AI from
producing harmful results or taking wrong actions, e.g.,escalate a
decision to a human for a high-risk network operation, defend against
malicious attacks,e.g., prompt injection. These guardrails typically
operate at the input/output/pre-action filter level or through static
boundary alignment.

However as AI systems are increasingly integrated into autonomous
workflows and critical infrastructure, these static measures are
proving insufficient for the full operational lifecycle, they often
cannot detect, interrupt, and rollover from unanticipated behaviors.
Network operators usually lack an equivalent infrastructure for human
oversight or to provide continuous, monitoring of an AI system’s
internal logic or its long-running execution paths that match the
speed and scale of the network management Agent applications, e.g.,
network failure or security risk is hard to detect and control,
occurring at machine speed. When a violation is suspected, there are
currently no standardized protocols for intervention (e.g., immediate
task suspension) and recovery (e.g., reverting to a last known safe
state or undoing a series of autonomous actions that introduce
substantial operational risk) mechanisms. In non-deterministic
environments, the lack of human oversight and human-AI semantic
intent exchange hinder timely risk mitigation and state recovery
during boundary violations by agents.

10.5. Agent Benchmarking vs Model Benchmarking

Current AI evaluation frameworks are primarily designed for "one-
shot" Large Language Models (LLMs) that provide static responses to
isolated prompts. However, the industry is shifting toward AI
Agents: systems capable of reasoning, planning, and using tools to
execute complex, multi-step workflows.

Traditional benchmarks (e.g., HumanEval) focus on outcome-only
scoring and static knowledge. These fail to measure the core
competencies of agents, such as long-horizon planning, tool-use
proficiency, and the ability to adapt to environmental feedback.

As agents increasingly manage production-level tasks—often involving
millions of tokens and hundreds of tool calls—there is a critical
need for standardized metrics that evaluate the process of task
completion, not just the final result.

o Sequential Complexity: Unlike single-turn benchmarks that check one
answer, agents require multi-step task completion where each step
relies on the success of the previous one. This involves massive
workflows (e.g., ~90 tool calls) that traditional benchmarks don't
measure.

o Environmental Interaction: Agents must interact with live
environments. Evaluation shifts from checking if code passes a test
to whether a system can select, sequence, and execute tools in
realistic, dynamic settings.

o Adaptive Planning: Benchmarking must move beyond static knowledge
retrieval to evaluate how systems build and adapt plans over long
horizons, especially when faced with resource constraints or
unexpected disruptions.

o Process vs. Outcome: Traditional "right or wrong" scoring is
insufficient. Agentic evaluation requires process metrics such as
tool success rates, context retention, and multi-turn coherence to
determine how an agent reached its conclusion.

11. Security Considerations

The security consideration from
[I-D.irtf-nmrg-network-digital-twin-arch] apply here. In addition,
the following architectural risks need to be considered:

o Memory Poisoning：If the AI/ML models used by the network AI Agent
or Network digital twin are compromised or poisoned with malicious/
fake data,they could begin making incorrect or malicious decisions.
Robust checks and validation are necessary to ensure the integrity of
these models. Session isolation or memory access authentication is
also required to mitigate such risk.

o Misuse of Tools： When network AI Agent interacts with tools,
Deceptive prompts or commands can be introduced. Tool access
verification, tool monitoring, log tracking of AI tool usage are
required to mitigate such risk.

o Privilege Compromise：When human operators interact with Network AI
Agent or Network AI Agent interact with tools/APIs/LLM, unauthorized
actions might be performed by exploiting privilege vulnerabilities.
Fine-grained permission control, dynamic access verification, and
role change monitoring are required to mitigate such risk.

o Resource Overload：When Network AI Agent interact with tools/APIs/
LLMs, system failures might be caused by exploiting resource-
intensive features. Deployment of resource management controls to
limit high-frequency task requests from agents is required to
mitigate such risk.

o Cascading Hallucinations：In case of multi-agent collaboration or
communication, AI-generated false information might disrupt the
reasoning process.Output verification, secondary verification of AI-
generated knowledge are required to mitigate such risk.

o Intent Breaking &Goal Manipulation：When external AI Agent or human
operators interact with the network AI Agent, reasoning through agent
planning capabilities might be manipulated. Planning verification,
managing reflection processes, goal consistency protection are
required to mitigate such risk.

o Misaligned & Deceptive Behaviors：When Network AI Agent interact
with tools/APIs/LLMs, harmful operations might be performed by
exploiting reasoning vulnerabilities. Manual confirmation of high-
risk operations, logging, monitoring, and deception detection are
required to mitigate such risk.

o Repudiation & Untraceability：In case of multi-agent collaboration
or communication, the logging or decision-making process might not be
traced. Logging & cryptographic signature, cryptographic
verification are required to mitigate such risk.

o Identity Spoofing & Impersonation：When Human operators interact
with the network AI Agent or in case of multi-agent collaboration or
communication,unauthorized operations might be performed by
exploiting authentication vulnerabilities. Comprehensive identity
verification, trust boundary control, and continuous monitoring are
required to mitigate such risk.

o Overwhelming HITL(Human In The Loop)：In case of multi-agent
collaboration or communication,fatigue auditors attack might take
place. Developing advanced human-machine interaction frameworks and
adaptive trust mechanisms are required to mitigate such risk.

o Unexpected RCE & Code Attacks：When the network AI Agent interacts
with tools/APIs/LLMs, Using AI to generate execution environments and
inject malicious code might take place. Restrict AI code generation
permissions, sandbox isolation, and manual review of generated code
are required to mitigate such risk.

o Agent Communication Poisoning：In case of multi-agent collaboration
or communication, communication channels of agents might be
manipulated to influence decision-making processes. Message
authentication, communication verification, implementing multi-agent
authentication mechanisms.

o Rogue Agents in MAS：In case of multi-agent collaboration, there
might be malicious or compromised agents. Restricting the autonomy
of agents and conducting regular AI testing are required to mitigate
such risk.

o Humans Attacks on MAS：In case of multi-agent collaboration,
business operations might be manipulated using agent delegation and
authorization. Restricting the delegation mechanism, implementing AI
agent identity authentication, and isolate tasks in segments are
required to mitigate such risk.

o Human Manipulation：When Human operators interact with the network
AI Agent, AI agents might be coerced to manipulate users into
performing covert operations. Safety guardrails, content moderation,
output content detection are required to mitigate such risk.

o Insecure Inter-Agent Protocol Abuse：In case of multi-agent
collaboration, there might be target flaws in protocols like MCP or
A2A; e.g.,consent bypass, context hijacking, etc. Strong
authentication,data validation, restricting delegation to scoped
function,logging agent and tool invocations and encrypting
communications are required to mitigate such risk.

o Supply Chain Compromise：In case of multi-agent collaboration,
Vulnerable, malicious, outdated, harmful components might be included
into the agent. Digital signatures of SBOMs (AI_, Agent_),applying
version control, chaining authentication, environment isolation are
required to mitigate such risk.

o Lifecycle security: The entire management lifecycle of the network
AI agents and the network digital twin—from initial deployment and
configuration to updates and decommissioning—must be secured against
unauthorized access and manipulation.

12. IANA Considerations

This document has no requests to IANA.

13. Conclusion

The following items were felt to be good starting points for IETF
work:

* Nature Language Interaction protocol to ensure both The accuracy
and efficiency of structured data for deterministic tasks and
natural language interactions for understanding and handling
uncertain or ambiguous tasks.

* Translate Service Level YANG Data model and Network Level YANG
Data model into DSL payloads and APIs which can be consumed by
Agents and Models.

* Human and Agent Interaction to support explainable, observability
and controllable capabilities.

* Define semantic information transfer or agent prompt language
template for Agent to Agent Communication.

* Agent to Agent Protocol extensions for IP Network Agent and
Network Element Agent Collaboration.

14. References

14.1. Normative References

[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119,
DOI 10.17487/RFC2119, March 1997,
<https://www.rfc-editor.org/rfc/rfc2119>.

[RFC8174] Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC
2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174,
May 2017, <https://www.rfc-editor.org/rfc/rfc8174>.

14.2. Informative References

[A2A] "Agent2Agent (A2A) protocol", April 2025, <https://google-
a2a.github.io/A2A/#/documentation?id=agent2agent-protocol-
a2a>.

[A2A-T] "Agent to Agent Protocol for Telecoms (A2A-T)", 2025,
<https://www.tmforum.org/resources/introductory-guide/
ig1453-agent-to-agent-protocol-for-telecoms-a2a-
t-v1-0-0/>.

[Agent-skills]
"Agent Skills", 2025, <https://agentskills.io/home>.

[Google-Agents-Whitepaper]
"Agents", 2024,
<https://www.kaggle.com/whitepaper-agents>.

[I-D.hong-nmrg-agenticai-ps]
Hong, Y., Youn, J., Wu, Q., and B. Claise, "Motivations
and Problem Statement of Agentic AI for network
management", Work in Progress, Internet-Draft, draft-hong-
nmrg-agenticai-ps-01, 1 March 2026,
<https://datatracker.ietf.org/doc/html/draft-hong-nmrg-
agenticai-ps-01>.

[I-D.ietf-nmop-network-incident-yang]
Hu, T., Contreras, L. M., Wu, Q., Davis, N., and C. Feng,
"A YANG Data Model for Network Incident Management", Work
in Progress, Internet-Draft, draft-ietf-nmop-network-
incident-yang-08, 13 February 2026,
<https://datatracker.ietf.org/doc/html/draft-ietf-nmop-
network-incident-yang-08>.

[I-D.ietf-nmop-terminology]
Davis, N., Farrel, A., Graf, T., Wu, Q., and C. Yu, "Some
Key Terms for Network Fault and Problem Management", Work
in Progress, Internet-Draft, draft-ietf-nmop-terminology-
23, 18 August 2025,
<https://datatracker.ietf.org/doc/html/draft-ietf-nmop-
terminology-23>.

[I-D.irtf-nmrg-ai-challenges]
François, J., Clemm, A., Papadimitriou, D., Fernandes, S.,
and S. Schneider, "Research Challenges in Coupling
Artificial Intelligence and Network Management", Work in
Progress, Internet-Draft, draft-irtf-nmrg-ai-challenges-
05, 18 March 2025, <https://datatracker.ietf.org/doc/html/
draft-irtf-nmrg-ai-challenges-05>.

[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>.

[MCP] "Model Context Protocol", November 2024,
<https://modelcontextprotocol.io/>.

[OTel-gen-ai]
"AI Agent Observability", 2025,
<https://opentelemetry.io/docs/specs/semconv/gen-ai/>.

[RFC6241] Enns, R., Ed., Bjorklund, M., Ed., Schoenwaelder, J., Ed.,
and A. Bierman, Ed., "Network Configuration Protocol
(NETCONF)", RFC 6241, DOI 10.17487/RFC6241, June 2011,
<https://www.rfc-editor.org/rfc/rfc6241>.

[RFC7011] Claise, B., Ed., Trammell, B., Ed., and P. Aitken,
"Specification of the IP Flow Information Export (IPFIX)
Protocol for the Exchange of Flow Information", STD 77,
RFC 7011, DOI 10.17487/RFC7011, September 2013,
<https://www.rfc-editor.org/rfc/rfc7011>.

[RFC7854] Scudder, J., Ed., Fernando, R., and S. Stuart, "BGP
Monitoring Protocol (BMP)", RFC 7854,
DOI 10.17487/RFC7854, June 2016,
<https://www.rfc-editor.org/rfc/rfc7854>.

[RFC7950] Bjorklund, M., Ed., "The YANG 1.1 Data Modeling Language",
RFC 7950, DOI 10.17487/RFC7950, August 2016,
<https://www.rfc-editor.org/rfc/rfc7950>.

[RFC8040] Bierman, A., Bjorklund, M., and K. Watsen, "RESTCONF
Protocol", RFC 8040, DOI 10.17487/RFC8040, January 2017,
<https://www.rfc-editor.org/rfc/rfc8040>.

[RFC8639] Voit, E., Clemm, A., Gonzalez Prieto, A., Nilsen-Nygaard,
E., and A. Tripathy, "Subscription to YANG Notifications",
RFC 8639, DOI 10.17487/RFC8639, September 2019,
<https://www.rfc-editor.org/rfc/rfc8639>.

[RFC8641] Clemm, A. and E. Voit, "Subscription to YANG Notifications
for Datastore Updates", RFC 8641, DOI 10.17487/RFC8641,
September 2019, <https://www.rfc-editor.org/rfc/rfc8641>.

[RFC9232] Song, H., Qin, F., Martinez-Julia, P., Ciavaglia, L., and
A. Wang, "Network Telemetry Framework", RFC 9232,
DOI 10.17487/RFC9232, May 2022,
<https://www.rfc-editor.org/rfc/rfc9232>.

[RFC9315] Clemm, A., Ciavaglia, L., Granville, L. Z., and J.
Tantsura, "Intent-Based Networking - Concepts and
Definitions", RFC 9315, DOI 10.17487/RFC9315, October
2022, <https://www.rfc-editor.org/rfc/rfc9315>.

[TMF-1251D]
"AN Agent Architecture v1.0.0", May 2025,
<https://www.tmforum.org/resources/introductory-guide/
ig1251d-an-agent-architecture-v1-0-0/>.

[TMF-1258] "Autonomous Networks Glossary v1.2.0", May 2025,
<https://projects.tmforum.org/wiki/display/PUB/
IG1258+Autonomous+Networks+Glossary+v1.2.0>.

Appendix A. Network Element AI Agent and Network AI Agent Collaboration

As a supplementary deployment case, this section explores the
collaboration mechanism between network element AI Agent and Network
AI Agent.

Network devices collect information from multiple dimensions,
including flow information, configuration, events, alarms, logs,
dynamic topology and routes, and device status (including CPU,
memory, and hardware health). With large amount of data collected to
the domain controller for analysis and processing, the data accuracy
is very limited and therefore it is hard to determine the service
impact within 1 minute. In addition, it usually require multiple
step interaction, complex task management with various different data
types or data sources.

To address those challenges, the network AI Agent can delegate
massive data analysis and processing to distributed AI Agent in each
network element, e.g., a) only allow distributed AI Agent export
processed analytic data to help establish global view of network
observability. b) or export key network fault information for Network
AI Agent for further investigation the root cause of the problem.

For the former case, routing protocol specific fault data such as BGP
Status Changed, OSPF Neighbor state changes, IS-IS Adjacency Changed
data or hardware related fault data such as Optical fail, Physical
Port down can be collected and using pre-trained LLM model with
expert experience to match fault pattern and invoke corresponding
routing protocol troubleshooting MCP tools and finally root cause.
In addition, it allows network maintenance engineer using nature
language interface to look up troubleshooting information or it
allows Network AI Agent or Task Agent at the network element using
MCP interface to invoke tools from MCP server within the network
element.

+------------+ +------------+
| Network | | |
Human Operator | AI Agent | | Task Agent |
| | | | |
| |+----------+| |+----------+|
| ||MCP Client|| ||MCP Client||
| |+----------+| |+----+-----+|
Intent +------+-----+ +-----+------+
| | |
+-------+------------------+-----------------+-------+
| | +------------+ | |
| +-----V-----------------+ +--------+----+------+|
| |Protocol Fault Agent | |Protocol|Fault Agent||
| | +---------+ +------+ | | +-----V----V-+ ||
| | | ONNOX | | MCP +-+----+--> MCP | ||
| | | Model | |Client| | | | Server | ||
| | +---------+ +------+ | | +------------+ ||
| +-----------------------+ +--------------------+|
| |
+----------------------------------------------------+
Smart Network Element

Figure 10: Network Element AI Agent and Network AI Agent
Collaboration Usage Example

Appendix B. Acknowledgements

This work has benefited from the discussions of NMRG interim meeting
on Agentic AI. Thanks Laurent Ciavaglia, Chris Janz,Pedro Garcia
Parra,Brad Peters,Kevin McDonnell,Giuseppe Fioccola,Richard
Kilmurray,Dmytro Gassanov for wonderful comments and discussion on
Agentic AI for Network Operation.

Appendix C. Changes between Revisions

v03 - v04

* Add some background information for this work in the introduction;
* Agent Governance Consideration in the context of network management;
* Clarify the value of Agent added to the network management architecture;
* Add one new definition for Autonomy;
* Rewrite Challenges for Agent Benchmarking;
* Rewrite the 1st challange on making YANG AI ready;
* Add a new challenge on Agent Observability,Intervention and Control;
* Add OTel & agent skill alignment.
* Update Architecture into hybrid architecture to support hybrid agent system.

v02 - v03

* Agentic AI Architecture Update.
* Rewrite Functional Interfaces section for user to agent, agent to agent, agent to tools communication.
* Explore Relationship Between Management Characteristics and Functional Components in section 6.
* Rewrite a collection of use cases to support multi-agent collaborations.
* Rewrite Challenges section to cover Trust and Security, protocol and benchmarking.
* Add workflows for Agent Registration, Discovery, team forming.
* Add workflow for Agent to Agent Communciation Security.
* Rewrite Security Consideration Section.
* Add Conclusion Section.

v00 - v01

* Add Security Consideration Section;
* Add Acknowledge Section;
* Clarify the relation between knowlege and tools;
* Clarify the souce of knowlege;
* Clarify the key characteristics of Network AI Agent to adpat to the environment change.

Contributors

Qiufang Ma
Huawei
Email: maqiufang1@huawei.com

Zhenqiang Li
CMCC
Email: lizhenqiang@chinamobile.com

Lionel Tailhardat
Orange Research
Email: lionel.tailhardat@orange.com

Authors' Addresses

Qin Wu
Huawei
China
Email: bill.wu@huawei.com

Cheng Zhou
China Mobile
China
Email: zhouchengyjy@chinamobile.com

Luis M. Contreras
Telefonica
Email: luismiguel.contrerasmurillo@telefonica.com

Sai Han
China Unicom
China
Email: hans29@chinaunicom.cn

Yong-Geun Hong
Daejeon University
Email: yonggeun.hong@gmail.com

Document	Document type	Active Internet-Draft (individual) This document is an Internet-Draft (I-D). Anyone may submit an I-D to the IETF. This I-D is not endorsed by the IETF and has no formal standing in the IETF standards process.
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