Network Management Operations X. Zhao
Internet-Draft CAICT
Intended status: Informational M. Wang
Expires: 1 September 2025 China Mobile
D. Ceccarelli
Cisco
B. Wu
C. Yu
Huawei
28 February 2025

AI based Network Management Agent(NMA): Concepts and Architecture
draft-zhao-nmop-network-management-agent-01

Abstract

With the development of AI(Artificial Intelligence) technology, large
model have shown significant advantages and great potential in
recognition, understanding, decision-making, and generation, and can
well match the self-intelligent network management requirements for
the goal of autonomous network or Intent-based Networking, and can be
used as one of the potential driving technologies to drive high-level
autonomous networks. When introducing AI for network management, how
to integrate AI technology and deal with the relationship with the
existing network management entity (such as network controller) is
the focus of research and standardization.

This document presents the concept of AI based network management
agent(NMA), provides the basic definition and reference architecture
of NMA, discusses the relationship of NMA with traditional network
controller or other network management entity by exploring the
delpoyment mode of NMA, and proposes the comman processing flow and
typical application scenarios of NMA.

Discussion Venues

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

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

Source for this draft and an issue tracker can be found at
https://github.com/ietf-wg-nmop/draft-ietf-nmop-digital-map-concept.

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Status of This Memo

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This Internet-Draft will expire on 1 September 2025.

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Please review these documents carefully, as they describe your rights
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Table of Contents

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3
1.1. Background . . . . . . . . . . . . . . . . . . . . . . . 3
1.2. Introduction of Network Management Agent (NMA) . . . . . 4
2. Terminology . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.1. Acronyms and Abbreviations . . . . . . . . . . . . . . . 5
2.2. Definitions . . . . . . . . . . . . . . . . . . . . . . . 5
3. Reference architecture of NMA . . . . . . . . . . . . . . . . 6
3.1. Function Requirements of NMA . . . . . . . . . . . . . . 6
3.2. Reference Architecture of NMA . . . . . . . . . . . . . . 7
3.3. Related Interfaces . . . . . . . . . . . . . . . . . . . 10
4. Network Automation Architecture Based on NMAs . . . . . . . . 10
4.1. Deployment modes considerations and requirements . . . . 12
4.1.1. Single Agent Challenges . . . . . . . . . . . . . . . 12
4.1.2. Multi Agents Challenges . . . . . . . . . . . . . . . 13
5. Common processing flow of NMA . . . . . . . . . . . . . . . . 14

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6. Typical Application Scenarios after Introducing NMA . . . . . 16
7. Security Considerations . . . . . . . . . . . . . . . . . . . 17
8. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 17
9. References . . . . . . . . . . . . . . . . . . . . . . . . . 17
9.1. Normative References . . . . . . . . . . . . . . . . . . 17
9.2. Informative References . . . . . . . . . . . . . . . . . 17
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 18

1. Introduction

1.1. Background

As the types of operator services become increasingly diverse, the
complexity and difficulty of network operations and maintenance
continue to grow. On one hand, new service scenarios such as
industrial internet, vehicle-road collaboration, and 5GtoB for
vertical industries are constantly emerging, and customer services
like Extended Reality (XR), Virtual Reality (VR), and smart home are
becoming more abundant, with a continuous increase in network access
volume. On the other hand, with the popularization of 5G and gigabit
optical networks, operators' networks are facing a situation where
networks from 2G to 5G coexist. The network protocols and
characteristics vary across different network domains, leading to a
continuous increase in the difficulty and complexity of network
operations and maintenance. Relying solely on traditional manual
operations and maintenance methods can no longer meet the
increasingly complex network operations and maintenance demands. The
level of network intelligence has become a key factor directly
affecting network performance and user experience. Against this
backdrop, enhancing the level of network intelligence and creating
Autonomous Networks (AN)[TMF-IG1230] or Intent-based Networking
[RFC9315] has become a global consensus among operators

Autonomous Networks provide an architecture for the delivery of
services and capabilities with â€œZero-Xâ€ (Zero-wait, Zero-trouble,
Zero-touch) experience for the users of vertical industries and
consumers and â€œSelf-Xâ€ experience (Self-configuration, self-healing,
self-optimizing) for network operators. In particular, the AN
framework defines 6 automation levels, spanning from Level 0 (L0)
where operations and maintenance are fully manual, to Level 5 (L5)
where the network is fully automated, managed by the AI and the human
intervention is reduced to the minimum.

As of today, the industry sees quite different levels of automation
from operator to operator, but the average level is considered to be
between L2 and L3. Mainstream operators are releasing goals and
plans to achieve Level 4 (L4) autonomous networks by 2025. L4+ AN
sets higher requirement in intention, decision-making, analysis,

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perception, and execution. Artificial Intelligence (AI) large model
technology has shown significant advantages and great potential in
identification, understanding, decision-making, and generation. It
has technical features such as multimodal fusion perception
capabilities, more user-friendly human-computer interaction and
knowledge Q&A capabilities, and content generation capabilities,
which can well match the new requirements of Level 4 Autonomous
Networks and already be one of the core driving technologies to
achieve high-level autonomous networks.

While the key issues after the introduction of AI in network include:

1) The application architecture and deployment methods of AI in
network management are still unclear, that is in what form AI can
help network management?

2) The relationship between AI and the existing network controllers
is not clear.

3) New interface capability requirements after AI is introduced are
not clear either.

Therefore, it is necessary to define the general architecture and
application form of AI in network management.

1.2. Introduction of Network Management Agent (NMA)

The concept of Network Management Agent (NMA) draws inspiration from
the â€œAI Agentâ€. According to the framework proposed in the
blog[LLM-powered-autonomous-agents]by OpenAI's Lilian Weng, the
functions of an LLM-powered Agent include several key components:
planning, memory and using tools to complete actions. Following the
mainstream definition widely accepted in the industry, an AI Agent
refers to â€œan intelligent entity with the ability to perceive the
environment, make decisions, and execute actions, and can gradually
achieve set goals through independent thinking and tool invocationâ€.
In Google's latest Agent white paper[Agents], â€œa Generative AI agent
can be defined as an application that attempts to achieve a goal by
observing the world and acting upon it using the tools that it has at
its disposal. Agents are autonomous and can act independently of
human intervention, especially when provided with proper goals or
objectives they are meant to achieve.â€

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The key features of AI agents include reasoning and decision-making
abilities, goal-orientation, and autonomy. Among these, autonomy
means that once the appropriate goals are provided, it can act
independently without human intervention. As the concept of AI agent
becomes widely accepted in the industry, itâ€™s expected to become one
of the most feasible application forms of AI.

Similarly, the network management agent (NMA) which can be understood
as the AI Agent for network management, refers to a network
management entity built based on ML/AI and equipped with the
autonomous closed-loop task processing capabilities. It can
automatically carry out network status perception, task intent
interpretation, task planning, decision-making and task execution
operations based on user task intentions or preset goals, so as to
achieve closed-loop processing of scenarios-oriented network
management tasks.

This document is trying to give a standardized common architecture
for the use of AI in network management, which can be in the form of
NMA. The following chapters will propose the concept of AI-based
NMA, define the reference architecture of NMA and functional
requirements of NMA for different scenarios, clarify the relationship
of NMA with existing controller or other control systems, and discuss
the general task processing workflow and typical application
scenarios of NMA.

2. Terminology

2.1. Acronyms and Abbreviations

AI: Artificial Intelligence

LLM: Large Language Model

NMA: Network Management Agent, refers to AI based network management
agent

2.2. Definitions

The document defines the following terms:

Network Management Agent (NMA): A network management entity built

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based on ML/AI and equipped with the autonomous task processing
capabilities. It can automatically carry out network status
perception, task intent[RFC9315]interpretation, task planning,
decision-making and task execution operations based on user task
intentions or preset goals, so as to achieve closed-loop
processing of scenarios-oriented network management tasks. For
different application scenarios, NMA can be subdivided into
multiple scenario-oriented agents.

3. Reference architecture of NMA

In this section weâ€™ll analyze the functional requirements and
reference architecture of the NMA.

3.1. Function Requirements of NMA

The NMA should support the following capabilities:

1. Support receiving task requests initiated by network operators or
users through natural language. It should be noted that natural
language interaction is not the only way to use NMA, network
operators can also use GUI (Graphical User Interface) to operate
NMA. But NMA should have the capability of understanding natural
language and translate into task intents through the build-in
Large Language Models (LLMs) reasoning capability.

2. Support perception of network status through querying the data of
controller and other network management tools. Network status
include network topology, service configuration, alarms,
performance and other information needed for processing the task.

3. Support task planning and breaking down task intent into specific
operations based on the user input and network status perception.
The task planning process can also utilize the reasoning
capability of LLMs.

4. Support selecting appropriate tools and automatically invoking
corresponding tools or APIs to complete the execution of each sub
operation. The toolkit includes management functions from
existing controller as well as other standalone management tools
like Network Digital Twin (NDT)
[I-D.irtf-nmrg-network-digital-twin], etc.

5. Support generating the task execution results based on the output
of each operation and sending back to network operators or users.

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6. Support analysis and self-assessment of execution results, and
enable autonomous or human intervention optimization based on
evaluation results to continuously improve the accuracy of task
execution.

7. Supporting collaboration among multiple intelligent agents to
complete complex tasks.

3.2. Reference Architecture of NMA

In order to achieve above capabilities, by referring to the common AI
agent framework, this document presents the reference functional
architecture of NMA as shown in Figure 1.

+--------------------------------------------+
| Network Management Agent (NMA) |
| +---------------------+ +----------------+ |
| | Intent Management | | Memory | |
| +---------------------+ | +------------+ | |
| +---------------------+ | | Long-term | | |
| | Network Paerception | | +------------+ | |
| +---------------------+ | +------------+ | |
Tool | +---------------------+ | | Short-term | | |
invocation | | Task Planning | | +------------+ | |
| +---------------------+ +----------------+ |
Controller<---+ | +---------------------+ +----------------+ |
| | | Orchestration and | | | |
NDT<---+----+-> Execution | | | |
| | +---------------------+ | Multi-agents | |
Other <---+ | +---------------------+ | Collaboration | |
external tools | | Reflection and | | | |
| | Self-optimization | | | |
| +---------------------+ +----------------+ |
+----------------------^---------------------+
|
+----------------------v---------------------+
| Common AI Service Layer |
| +----------------++------------++--------+ |
| | Large language || Multimodal || Small | |
| | Models(LLMs) || Models || Models | |
| +----------------++------------++--------+ |
| +----------------------------------------+ |
| | Knowledge Base | |
| +----------------------------------------+ |
+--------------------------------------------+

Figure 1: Reference function architecture of NMA

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The main function components of NMA include:

Intent Management: Basic capability provided by AI models,
responsible for collecting the input task information and
translate into intents through AI model reasoning.

Network Perception: Achieve real-time query for network status
information related to the task intent. Network status
information is not limited to network topology, service
configurations, device status, alarms, performances, etc. The
query source can be controller, ENO, etc.

Task Planning: Based on the reasoning ability of AI models, break
down the task intention into multiple sub operations.

Orchestration and execution: Select the appropriate tools based on
the specific operation, and automatically call the relevant tools
or interfaces to perform the operation. After each sub operation
is completed, the execution results of each operation are formed
into task execution results.

Reflection and self-optimization: Select the appropriate tools based
on the specific operation, and automatically call the relevant
tools or interfaces to perform the operation. After each sub
operation is completed, the execution results of each operation
are formed into task execution results.

Additionally, artificial evaluation methods can be integrated to
further optimize the NMA's performance through human supervision,
enhancing the NMA's intention understanding and task execution
capabilities.

Memory: Responsible for storing and processing various types of
information during the operation of NMA, including long-term
memory (LTM) and short-term memory (STM). STM stores information
that NMA is currently aware of and needed to carry out complex
cognitive tasks such as learning and reasoning. LTM can store
information for a remarkably long time, ranging from a few days to
months or years. To summarize, STM is for in-context learning
which is short and finite, as it is restricted by the finite
context window length of Transformer. LTM is for the external
vector store that the NMA can attend to query time, accessible via
fast retrieval.

Multi-agents collaboration Responsible for completing collaboration
between multiple NMAs at different levels or in different
application scenarios. The specific collaboration mechanism needs
further research.

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In addition, there is a common AI service layer, including various
large language models (LLMs), multimodal models, small models, and
knowledge base. Among them, AI models provide public interactive
intelligence capabilities as unified agent engine, to simplify NMA
development. Knowledge base provides unified search for multi-type
knowledge bases including vector knowledge base, system online help,
operation and maintenance data logs), combines AI models to complete
knowledge fusion and extraction, and improves the accuracy of NMA
task execution.

Various NMAs can be constructed based on the common AI service layer.
During the operation of NMA, it leverages the model reasoning
capabilities and knowledge base provided by the AI service layer to
achieve functions such as intent parsing and task planning. It
should be noted that, depending on the actual deployment
requirements, the AI basic service can also be deployed within the
NMA.

For different application scenarios, there can be multiple scenario-
oriented agents (like apps in the phone). Aimed at the network
planning, construction, maintenance, optimization, and operation
scenarios, the main NMAs could include:

* Network Fault Handling Agent: This agent can be created by pre-
training specific AI model based on the network troubleshooting
guidance documents, network equipment product documents, and other
materials. The agent can solidify the fault handling experience
of experts, and realize fault impact analysis, root cause self-
diagnosis, and self-repair of network faults by orchestrating and
calling models or network control APIs. It also interfaces with
the work order dispatching system to achieve automated closed-loop
processing of work orders, etc.

* Network Planning Agent: Makes use of the capabilities of AI large
model to understand the network planning intent (user intent,
business development goals, network construction plans, etc.), and
analyzes and forecasts the current network resource usage
(traffic, performance, user scale, resource utilization, etc.) to
output planning schemes.

* Network Optimization Agent: Understands the network optimization
goal through natural language, converts the optimization intent
into network optimization constraint rules, such as network load
thresholds, service route optimization strategies, etc. The
instance can use traffic prediction models to predict the future
traffic and bandwidth utilization of the entire network,
automatically generate resource, hidden danger, performance,
traffic, and other prediction results, and can automatically

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generate optimization strategies based on the prediction results
to perform traffic pre-diversion, autonomous decision-making, and
automatic execution to achieve dynamic energy saving of equipment
and optimal traffic of the entire network, etc.

* Intelligent Assistant Agent: This instance can have open Q&A
capability based on LLM, providing a dialogue Q&A style operation
and maintenance. Users can "one-click" input fault descriptions
or resource names in natural language, and the instance will
automatically perform intent recognition and query to
significantly improve the efficiency of knowledge questioning,
fault reporting, and maintenance support.

3.3. Related Interfaces

To be discussed in the later version.

4. Network Automation Architecture Based on NMAs

When deploying an NMA based management/control architecture, it is
possible to consider two different deployment models, where the NMA
can be part of an existing network controller, or can be an
independent system deployed separately and interacting both with the
controller and the network. The two deployment modes can be called:
Independent deployment mode and Integrated deployment mode and are
shown in Figure 2.

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+-----------------------------+ +--------------------+
| | | |
| Network <--C_A_I--> Network Management |
| Controller | | Agent(NMA) |
| | | |
+--------------^--------------+ +----------^---------+
| |
Southbound Interface(SBI) Intelligent SBI(I_SBI)
| |
+--------------v-----------------------------------v---------+
| Physical Network |
+------------------------------------------------------------+
(a)

+------------------------------------------------------------+
| Network Controller |
| |
| +--------------------+ +--------------------+ |
| | Original Function <--Internal-> Network management | |
| | Modules | Interface | Agent(NMA) | |
| +--------------------+ (I_I) +--------------------+ |
| |
+------------------------------^-----------------------------+
|
Extended SBI(E_SBI)
|
+------------------------------v-----------------------------+
| Physical Network |
+------------------------------------------------------------+
(b)

Figure 2: Deployment mode of network management agent (NMA)

Independent deployment mode: As shown in Figure 2(a), NMA is
independently deployed from the original network controller. NMA
and controller are independent systems. A new east-west interface
needs to be added between the NMA and the controller to achieve
capability calling and result feedback operations. This interface
can be called â€œC_A_Iâ€. In this deployment mode, controller uses
southbound interface (SBI) to interact with physical network,
while an intelligent southbound interface (abbreviated as â€œI_SBIâ€)
needs to be added between NMA and the underlying physical network.

Integrated deployment mode: As shown in Figure 2(b), NMA is

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integrated and deployed with the original network controller, and
the NMA serves as a function of the controller. NMA interacts
with original function modules through internal interface
(abbreviated as â€œI_Iâ€). The enhanced controller interacts with
the underlay physical network through extended SBI (abbreviated as
â€œE_SBIâ€).

The specific functional requirements and information model
definition of interfaces mentioned above will be discussed in the
following version.

4.1. Deployment modes considerations and requirements

While the integrated deployment mode is relatively simple, due to an
internal communication between the NMA and the controller, the
independent deployment mode introduces several challenges to be
analyzed, that can be grouped into â€œsingle agentâ€ and â€œmulti agentâ€
challenges.

4.1.1. Single Agent Challenges

Starting from and architecture with a single NMA, like the one shown
in Figure 3 below, the challenges that we need to address are:

* NMA APIs: Agents use descriptions of APIs and tools in order to
use them. A gap analysis against existing tools needs to be
carried out to understand if the NMA API requirements can be met
and if we can find an optimal or common way to describe network
APIs for LLMs.

* NMA triggers: Agents need to be triggered with an input, which can
be â€œjustâ€ a natural language input or something with a more
structured format. Is the trigger going to be initiated by a
controller or is it â€justâ€ a human readable string?

* NMA interaction with existing controller: A wide variety of
protocol and models exist today to interact with different
components of existing controller. A gap analysis needs to be run
to understand if those protocols and models are enough or
extensions are needed in order to interact no longer with humans/
UIs and higher order orchestrators/controllers but also by NMAs.

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Figure 3: Network management architecture with single agent

4.1.2. Multi Agents Challenges

Things get a bit more complex when multiple NMAs are deployed and, in
addition to interacting with existing controller, they need to
interact with other NMAs as shown in Figure 4. In this case the
challenges to consider are:

* Inter NMA communication: It is just a natural language â€œstringâ€ or
we need a more structured format/protocol? How can we ensure
agents have a common understanding of context and can interwork?

* NMA discovery: How do agents know about each other? They need to
advertise their existence and capabilities to other NMAs? How do
we describe their capabilities? How do we do it in a way that
they can discover each other?

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Figure 4: Network management architecture with multi agents

5. Common processing flow of NMA

The embedded AI model within NMA serves as the interface for user
information input, and NMA instance uses the large model as the
interface to clarify problems through multiple rounds, analyze
positioning, generate plans, invoke interfaces/tools to handle
problems, and complete closed-loop processing of problems, so as to
build end-to-end problem processing assistance capabilities.

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Figure 5: Common processing flow of NMA

The common processing flow of NMA instance are shown in Figure 3.
The processing steps include:

1. User/Network Management Task Input: Input the userâ€™s task
information Through multiple rounds of natural language
interaction.

2. Intent Analysis: Analysis user task intent through AI model
reasoning provided by the AI based basic services within NMA.

3. Task Decomposition: Split the task into detailed operations to be
performed based on the analyzed intent of the task.

4. Tool/API Invocation: Call the corresponding tool or function API
to complete the execution of each operation listed in step 3).
The toolkit refers to the collection of all tools that can be
used directly to manage and operate physical networks, which can
include management functions from existing controller, EMS, or

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standalone other management tools. The toolkit can include
service configuration API/Tool, alarm monitor API/Tool,
performance monitor API/Tool, network optimization API/Tool,
topology management API/Tool, etc.

5. Process Encapsulation: Encapsulate each execution step.
According to the order or dependency of all the operations,
package the individual operation results into the execution
result of the entire task.

6. Executive result analysis: Analyze the task processing results
and return to the user.

Through above processing flow, NMA can achieve closed-loop automated
processing of tasks and constructing end-to-end intelligent network
maintenance assistance capabilities. For example, in the intelligent
troubleshooting scenario, NMA can identify the cause of the fault and
call the corresponding interfaces to handle it, such as creating a
troubleshooting order, automatically initiating rerouting/optical
power optimization, and other troubleshooting operations, and
automatically verifying the progress of the order execution, with
feedback on the troubleshooting results after the job order is
completed.

The introduction of NMA can effectively improve the level of
intelligent operation and maintenance of network, thus promoting the
continuous evolution of communication network towards higher-level
self-intelligence.

6. Typical Application Scenarios after Introducing NMA

Typical applications of NMA in networks can cover network operation
and maintenance and operation processes:

Network management and maintenance scenarios, including:
* Intelligent planning and construction: such as broadband
installation, resource/capacity planning, intelligent
acceptance, site selection, etc.

* Intelligent maintenance: such as intelligent fault diagnosis,
quality analysis, operation and maintenance/cutting assistant,
broadband maintenance assistant, etc.

* Intelligent optimization: such as route optimization, coverage
optimization, topology optimization, and intelligent energy
saving, etc.

Network operation scenarios: including intelligent question and

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answer, customer service assistant, automatic classification of
user complaints, customer retention, product recommendation,
automatic flow of work orders, anti-fraud monitoring and
identification, intelligent marketing and other value-added
services. This part is outside the scope of this document.

The starting point for the application of NMA in the live network
should comprehensively consider the scenarios with strong demand,
feasible technology, and good input-output ratio, and at the same
time meet the requirements of sufficient data for AI pre-training
during the construction of NMA instance, perfect data annotations,
and high fault tolerance rate. Based on above considerations, the
broadband installation and maintenance assistant, fault diagnosis,
operation and maintenance assistant may become the first application
scenarios.

7. Security Considerations

TBD.

8. IANA Considerations

This document has no requests for IANA action.

9. References

9.1. Normative References

9.2. Informative References

[Agents] Wiesinger, J., Marlow, P., and V. Vuskovic, "Google
Whitepaper: Agents", 10 September 2024.

[I-D.irtf-nmrg-ai-challenges]
FranÃ§ois, 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-
03, 4 March 2024, <https://datatracker.ietf.org/doc/html/
draft-irtf-nmrg-ai-challenges-03>.

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[I-D.irtf-nmrg-network-digital-twin]
Zhou, C., Yang, H., Duan, X., Lopez, D., Paster, A., Wu,
Q., Bouncadair, M., and C. Jacquenet, "Network Digital
Twin: Concepts and Reference Architecture", Work in
Progress, Internet-Draft, draft-irtf-nmrg-network-digital-
twin-arch-09, 24 January 2025,
<https://datatracker.ietf.org/doc/html/draft-irtf-nmrg-
network-digital-twin-arch-09>.

[I-D.kdj-nmrg-ibn-usecases]
Yao, K., Chen, D., Jeong, J., Wu, Q., Yang, C., and L.
Contreras, "Use Cases and Practices for Intent-Based
Networking", Work in Progress, Internet-Draft, draft-kdj-
nmrg-ibn-usecases-01, 8 July 2024,
<https://datatracker.ietf.org/doc/html/draft-kdj-nmrg-ibn-
usecases-01>.

[LLM-powered-autonomous-agents]
Weng, L., "LLM Powered Autonomous Agents", 23 June 2023.

[RFC7575] Behringer, M., Pritikin, M., Bjarnason, S., Clemm, A.,
Carpenter, B., Jiang, S., and L. Ciavaglia, "Autonomic
Networking: Definitions and Design Goals", RFC 7575,
DOI 10.17487/RFC7575, June 2015,
<https://www.rfc-editor.org/rfc/rfc7575>.

[RFC7576] Jiang, S., Carpenter, B., and M. Behringer, "General Gap
Analysis for Autonomic Networking", RFC 7576,
DOI 10.17487/RFC7576, June 2015,
<https://www.rfc-editor.org/rfc/rfc7576>.

[RFC9222] Carpenter, B. E., Ciavaglia, L., Jiang, S., and P. Peloso,
"Guidelines for Autonomic Service Agents", RFC 9222,
DOI 10.17487/RFC9222, March 2022,
<https://www.rfc-editor.org/rfc/rfc9222>.

[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-IG1230]
McDonnell, K., Machwe, A., Milham, D., Oâ€™Sullivan, J.,
Clemm, A., and J. NiemÃ¶ller, "Autonomous Networks
Technical Architecture", TMF IG1230, December 2022.

Authors' Addresses

Zhao, et al. Expires 1 September 2025 [Page 18]

Internet-Draft Network Management Agent Concept February 2025

Xing Zhao
CAICT
Beijing
China
Email: zhaoxing@caict.ac.cn

Minxue Wang
China Mobile
Beijing
China
Email: wangminxue@chinamobile.com

Daniele Ceccarelli
Cisco
Email: dceccare@cisco.com

Bo Wu
Huawei
China
Email: lana.wubo@huawei.com

Chaode Yu
Huawei
China
Email: yuchaode@huawei.com

Zhao, et al. Expires 1 September 2025 [Page 19]