ETSI GR ZSM 021 V1.1.1 (2026-05)

Zero-touch network and Service Management (ZSM); Study of ZSM Framework from Automation to Autonomy

DGR/ZSM-021_GRAutom2Auton

General Information

• Status: Not Published
• Technical Committee: ZSM - Zero-touch network and Service Management
• Current Stage: 12 - Citation in the OJ (auto-insert)
• Due Date: 11-Jun-2026
• Completion Date: 22-May-2026

Standards Content (Sample)

GROUP REPORT
Zero-touch network and Service Management (ZSM);
Study of ZSM Framework from Automation to Autonomy
Disclaimer
The present document has been produced and approved by the Zero-touch network and Service Management (ZSM) ETSI
Industry Specification Group (ISG) and represents the views of those members who participated in this ISG.
It does not necessarily represent the views of the entire ETSI membership.

2 ETSI GR ZSM 021 V1.1.1 (2026-05)

Reference
DGR/ZSM-021_GRAutom2Auton
Keywords
artificial intelligence, automation, autonomous,
levels
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3 ETSI GR ZSM 021 V1.1.1 (2026-05)
Contents
Intellectual Property Rights . 4
Foreword . 4
Modal verbs terminology . 4
1 Scope . 5
2 References . 5
2.1 Normative references . 5
2.2 Informative references . 5
3 Definition of terms, symbols and abbreviations . 6
3.1 Terms . 6
3.2 Symbols . 6
3.3 Abbreviations . 6
4 ZSM target to autonomy. 7
4.1 Concept of autonomy . 7
4.2 Differences between automation and autonomy. 8
4.3 High-Level Description of autonomous system . 8
4.4 The progress of Network Automation . 9
4.4.1 Review of ZSM work . 9
4.4.2 Review of industry work . 10
4.4.2.1 Introduction . 10
4.4.2.2 Standardization of the Network Automation and Autonomy . 10
4.4.3 Review Summary . 11
5 Scenarios that benefit from autonomy . 11
5.1 Scenario 1 Fault Self-Healing with Autonomy Capabilities . 11
5.2 Scenario 2 Fault Management - Signalling Storm Prevention . 11
6 ZSM architecture for autonomy . 12
6.1 Overview . 12
6.2 Architecture design objectives and principles . 12
6.3 Reference Architecture evolution . 12
6.3.1 Function View . 12
6.3.2 Deployment and interactions in ZSM . 14
6.3.2.1 Description . 14
6.3.2.2 Example of procedure flows . 15
7 Potential new ZSM Capabilities fulfil autonomy . 16
8 Potential future work based on the present document . 17
Annex A: Change history . 18
History . 19

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Intellectual Property Rights
Essential patents
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pertaining to these essential IPRs, if any, are publicly available for ETSI members and non-members, and can be
found in ETSI SR 000 314: "Intellectual Property Rights (IPRs); Essential, or potentially Essential, IPRs notified to
ETSI in respect of ETSI standards", which is available from the ETSI Secretariat. Latest updates are available on the
ETSI IPR online database.
Pursuant to the ETSI Directives including the ETSI IPR Policy, no investigation regarding the essentiality of IPRs,
including IPR searches, has been carried out by ETSI. No guarantee can be given as to the existence of other IPRs not
referenced in ETSI SR 000 314 (or the updates on the ETSI Web server) which are, or may be, or may become,
essential to the present document.
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Foreword
This Group Report (GR) has been produced by ETSI Industry Specification Group (ISG) Zero-touch network and
Service Management (ZSM).
Modal verbs terminology
In the present document "should", "should not", "may", "need not", "will", "will not", "can" and "cannot" are to be
interpreted as described in clause 3.2 of the ETSI Drafting Rules (Verbal forms for the expression of provisions).
"must" and "must not" are NOT allowed in ETSI deliverables except when used in direct citation.

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1 Scope
The present document examines the ZSM framework capabilities required to support autonomy. It highlights the
differences between automation and autonomy, and the differences among levels of autonomy, through a review of
published ZSM work and other industry work.
The study work is driven by scenarios that would benefit from autonomy, it includes an activity aimed at identifying the
ZSM framework capabilities to fulfil autonomy.
Additionally, possible service gaps are addressed. Collaboration with and reference to other SDOs (e.g. in TM Forum,
IRTF NMRG, 3GPP SA5) are recommended when appropriate.
2 References
2.1 Normative references
Normative references are not applicable in the present document.
2.2 Informative references
References are either specific (identified by date of publication and/or edition number or version number) or
non-specific. For specific references, only the cited version applies. For non-specific references, the latest version of the
referenced document (including any amendments) applies.
NOTE: While any hyperlinks included in this clause were valid at the time of publication, ETSI cannot guarantee
their long-term validity.
The following referenced documents may be useful in implementing an ETSI deliverable or add to the reader's
understanding, but are not required for conformance to the present document.
[i.1] ETSI GR ZSM 005: "Zero-touch network and Service Management (ZSM); Means of
Automation".
[i.2] ETSI GR ZSM 011: "Zero-touch network and Service Management (ZSM); Intent-driven
autonomous networks; Generic aspects".
[i.3] ETSI GR ZSM 009-3: "Zero-touch network and Service Management (ZSM); Closed-Loop
Automation; Part 3: Advanced Topics".
[i.4] TM Forum IG1258: "Autonomous Networks Glossary v1.2.0".
[i.5] ETSI GS ZSM 002: "Zero-touch network and Service Management (ZSM); Reference
Architecture".
[i.6] ETSI GS ZSM 009-1: "Zero-touch network and Service Management (ZSM); Closed-Loop
Automation; Part 1: Enablers".
[i.7] ETSI GS ZSM 016: "Zero-touch network and Service Management (ZSM); Intent-driven Closed
Loops".
[i.8] Joseph Sifakis: "Autonomous Systems - An Architectural Characterization", 2018.
[i.9] TM Forum IG1218: "Autonomous Networks Business Requirements and Framework v3.0.0".
[i.10] TM Forum IG1251A: "Autonomous Networks Reference Architecture Realizations v1.0.0".
[i.11] TM Forum IG1253: "Intent in Autonomous Networks v1.3.0".
[i.12] ETSI TS 128 312: "LTE; 5G; Management and orchestration; Intent driven management services
for mobile networks (3GPP TS 28.312 Release 19)".
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[i.13] ETSI TS 128 104: "5G; Management and orchestration; Management Data Analytics (MDA)
(3GPP TS 28.104 version 18.4.0 Release 18)".
[i.14] ETSI TS 128 105: "5G; Management and orchestration; Artificial Intelligence/ Machine Learning
(AI/ML) management (3GPP TS 28.105 version 18.4.0 Release 18)".
[i.15] ETSI TS 128 561: "5G; Management and orchestration; Management aspects of Network Digital
Twins (3GPP TS 28.561 version 19.0.0 Release 19)".
[i.16] ETSI TS 123 288: "5G; Architecture enhancements for 5G System (5GS) to support network data
analytics services (3GPP TS 23.288 version 18.7.0 Release 18)".
[i.17] IETF RFC 9315: "Intent-Based Networking - Concepts and Definitions".
[i.18] IETF RFC 8969: "A Framework for Automating Service and Network Management with YANG".
[i.19] IETF draft-ietf-nmop-network-incident-yang-08: "A YANG Data Model for Network Incident
Management".
[i.20] ETSI GR ZSM 020: "Zero-touch network and Service Management (ZSM); Study on the
Utilization of Agents in Autonomous Networks".
[i.21] ETSI GS ZSM 007: "Zero-touch network and Service Management (ZSM); Terminology for
concepts in ZSM".
3 Definition of terms, symbols and abbreviations
3.1 Terms
For the purposes of the present document, the following terms apply:
autonomous networks: network and software platforms that are capable of sensing their environment, learning from it
and adapting their behaviours accordingly, with little or no human input
single domain: logically bounded management scope that can fulfil closed-loop automation of specific network
operations
NOTE: Within the ZSM framework, a Single Domain is defined as a Management Domain instance with
autonomous capabilities.
3.2 Symbols
Void.
3.3 Abbreviations
For the purposes of the present document, the abbreviations given in ETSI GS ZSM 007 [i.21] and the following apply:
5GS 5G System
AN Autonomous Networks
AN Levels Autonomous Network Levels
AN L4 Autonomous Network Level 4
CCSA China Communications Standards Association
CN Core Network
IBN Intent-Based Networking
IDMS Intent-Driven Management Services
MDA Management Data Analytics
NDT Network Digital Twin
NWDAF Network Data Analytics Function
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OAM Operations, Administration and Maintenance
SMF Session Management Function
ToR Terms of Reference
UPF User Plane Function
YANG Yet Another Next Generation
4 ZSM target to autonomy
4.1 Concept of autonomy
"Automation is the action of making a task executable without human intervention. It is realized by introducing new
automatic functions or by replacing, modifying or augmenting manual functions with automation artifacts (e.g. a script
executing a series of commands)" (ETSI GR ZSM 005 [i.1]). A comprehensive automation provides an approach for
combining function automation (e.g. combination or chaining of the functions that collectively achieve the closed loop
automation) with process automation (e.g. operational processes such as service creation, assurance and optimization,
etc.).
Automation refers to "the use of machines and computers that can operate without needing human control", on the
other hand, autonomy implies "the capability to make decisions free from human control" (TM Forum IG1258 [i.4]).
In [i.8], autonomous agent is defined as "the capacity of an agent to achieve a set of coordinated goals by its own means
(without human intervention) adapting to environment variations".
The ZSM framework of course supports automation of network operations. Closed loops, in their most basic form,
achieve automation by linking perception to analysis, decision making and action. However, when perception,
analytical and decision-making processes are fixed, the closed loop is only able to operate within that range of contexts
and circumstances for which those fixed processes generate effective outcomes. If context or circumstances change
sufficiently, then elements of the closed loop should be redesigned in order that the closed loop can function effectively.
Such redesign may include changing elements and means of perception, of methods of analysis, of methods and bases
for decision making, or of actuation of decisions taken.
Fundamentally, the ability of systems flexibly to adapt to new, changing or unforeseen problems, contexts and
circumstances, without human intervention, lies at the heart of the distinction between autonomy and "mere"
automation. This view is widely held across the inter-linked fields of cognitive science, philosophy of mind, and AI.
Somewhat more generally, the gap between automation and autonomy is linked to cognition, a capability which is
imperfectly described across these fields, but broadly held to encompass at least two others: reasoning and learning.
Reasoning is, itself, somewhat imperfectly defined as - in essence - those capacities needed to find or design effective
solutions to new or evolving problems. Learning refers to improving reasoning outcomes through acquisition and use of
new knowledge.
Aspects of these concepts - for example, the inclusion and utility of knowledge within closed loop structures, and the
complementing of operational (automation) closed loops with cognitive closed loops supporting adaptation - have been
developed within ZSM work. However, the role and use of reasoning and learning have not been fully developed or
described within ZSM work to date, in significant part because technology supporting these capabilities - specifically
and first, generative AI - was not available and broadly understood until relatively recently. Conceptually, autonomy is
now generally linked - for good reason - to such technologies.
To summarize, within the ZSM frameworks, autonomy refers to the system's capability to achieve goals by its own
means - encompassing decision-making, service orchestration, and service lifecycle management, etc. - without human
intervention, while adapting to variations of network and its environment.
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4.2 Differences between automation and autonomy
In the context of network and service management, there are various interpretations regarding the distinction between
automation and autonomy. For instance, automation + intelligence (as the cognitive closed loop defined in ETSI
GR ZSM 009-3 [i.3], clause 5.1) may be considered as autonomy. To facilitate clearer differentiation, it is
recommended to list some of the distinctions along several dimensions, such as:
• Operator experience: automation is predictable and reliable, with clearly defined and expected outcomes.
While autonomy is adaptive but less predictable, it dynamically adjusts behaviour.
• Decision-making mechanism: automation generally is rule-based, or at least, is designed to work properly only
within a constrained set of circumstances. Those circumstances should be anticipated and reflected at the point
of design. Whereas autonomy can enable dynamic decision-making through self-learning and goal
management capabilities (herein referred to as Choice-Making).
• Environmental adaption: automated processes may be expected to perform within constrained environmental
circumstances. Autonomous systems, on the other hand, can cope with environmental change, uncertainty or
unpredictability.
4.3 High-Level Description of autonomous system
ZSM has been actively working on reducing the human intervention of network and service management automation,
and progressing toward the full automation. While the ultimate objective is to achieve autonomy, the ZSM framework
can be considered as an autonomous system, which should be designed to enhance operational capabilities by
self-awareness, autonomous decision-making and self-learning mechanisms to handle the complexity and dynamics of
networks, while minimizing the need of human intervention.
Intent management, closed-loop automation, knowledge serve as the fundamental functions in the ZSM framework. The
closed-loop mechanism is designed to fulfil individual intents and resolve issues detected from environment
automatically. ETSI GR ZSM 011 [i.2] further explores various potential intent conflicts and resolution approaches
(e.g. set priorities, set utility-goals,), however, managing multi-intent conflicts remains an ongoing challenge.
Additionally, automation system often struggles to detect sub-threshold deterioration before it escalates into faults.
Autonomy is considered to address these challenges and the dual closed-loop mechanism (shown in Figure 4.3-1) can
be a potential explanation to explore a way forward to autonomy, in which, the closed-loop (Monitor-Analysis-
Decision-Execution) automation plus knowledge reflects responses to inputs such as intents and incoming events (ETSI
GS ZSM 009-1 [i.6]), this is referred to as a Reactive Closed-loop. Meanwhile, self-awareness is used to recognize
potential risks and threats, and choice-making selects the most suitable goal. These functions enable learning and
adaptation capabilities - by leveraging and continuously updating knowledge - thereby forming a Proactive Closed-loop.

Figure 4.3-1: Closed-loops for autonomy
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A designed reactive closed loop serves as the foundational mechanism for operation automation, whereas the proactive
closed loop is triggered by changes in knowledge - beginning with new knowledge of some inadequacy of the reactive
closed loop. This drives a redesign of the reactive closed loop. For example, when a known issue "a user reports video
stuttering and performance anomalies are detected in a specific path" is detected by a violation of implemented KPIs,
the reactive CL accommodates using established designed reactions. In this case, the root cause analysis and linked
decision-making are within design bounds. However, when facing a new issue such as "a user reports video stuttering
while all KPIs appear normal", the designed reactive closed loop cannot resolve the problem. In this case, the proactive
CL would activate its reasoning capabilities to infer potential causes and subsequently update the decision-making and
action outcomes: in effect, re-designing the reactive closed loop.
4.4 The progress of Network Automation
4.4.1 Review of ZSM work
This clause reviews existing ZSM work for network and service management automation, based on which a gap
analysis between the existing automation work and target autonomy capabilities will be conducted.
As defined in ZSM ToR, the goal of ZSM is to have all operational processes (e.g. delivery, deployment, orchestration,
configuration, assurance, and optimization) executed automatically, ideally with fully automated.
ETSI GS ZSM 002 [i.5] defines the ZSM framework which is versatile and built on service-based principles offering
scalability, modularity, extensibility, and flexibility, provides capabilities to integrate AI-based functionalities and
enable closed-loop automation.
ETSI GS ZSM 009-1 [i.6] introduces a structured feedback loop between data monitoring, data analytics,
decision-making and actions, which aims to reach and preserve a set of objectives without external intervention, and
within which the automated decision-making mechanisms can be bounded by rules and policies.
ZSM framework also takes advantage of emerging technologies to increase network and services management
automation and efficiency. For example, AI/ML, Network Digital Twin can enhance analytics and decision-making to
empower the closed-loop operation. In addition, the closed-loop automation is governed by policies, rules, intents
and/or other forms of inputs that guide its behaviour.
Figure 4.4-1 shows the concepts related to service and network automation, in which intent (ETSI GR ZSM 011 [i.2],
intent-driven autonomous networks) allows human network operators to convey requirements of network (i.e. SLA).
Intent management can translate intents into internal goals, which are the inputs of service life cycle management
automation and closed loop automation, AI technology may be able to increase the "intelligence" regarding learning
based, adaptiveness of automated systems. Some stages of service life cycle management automation or closed loop
automation depend on Network Digital Twin to determine the expected outcomes, impacts and effects.

Figure 4.4-1: Concepts of ZSM automation
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4.4.2 Review of indu
...