Use cases for Autonomous Networks
This technical specification analyses use cases for autonomous networks and provides a basic set of requirements for each use case. It is produced by the ITU-T Focus Group on Autonomous Networks to study core autonomous behaviors, applications, and architectural guidance. The document outlines scenarios such as knowledge management, simulation configuration, automation loops, and machine learning integration.
Summary and content fields are drawn from the document text and are pending editorial review.
Who issued it
Who it governs
Network architects, researchers, and engineers working on telecommunication standards, artificial intelligence, and autonomous network systems should read this to understand use cases and requirements.
What the document contains
The technical specification covers forty distinct use cases related to autonomous networks, detailing their contexts, descriptions, open issues, and specific requirements classified into critical, expected, and added value categories.
The document does not provide binding legal regulations or enforcement mechanisms, focusing instead on technical specifications and architectural guidance for telecommunication standardization.
- The document provides a comprehensive analysis of use cases for autonomous networks, covering core autonomous behaviors and applications.
- It establishes basic requirements classified by criticality for each identified use case.
- It highlights the importance of knowledge bases, sandboxes, closed loops, and intent-based management in future networks.
- It addresses specific domain applications such as radio access networks, data centers, edge computing, and financial services.
1 SCOPE 2 REFERENCES 3 TERMS AND DEFINITIONS 4 ABBREVIATIONS 5 CONVENTIONS 6 INTRODUCTION 7 USE CASES BIBLIOGRAPHY
How it scores
Each score is backed by a quote verified against the document text. Editorial review has not yet confirmed them.
Accountability
2/5Clear responsibility for AI outcomes
To satisfy the key concepts of autonomous networks (evolution, experimentation and adaptation) while minimizing human intervention requires knowledge.
7.1
screening score · 2026-10-23
Fairness
0/5Non-discrimination and equitable treatment
n/a
screening score · 2026-10-23
Human Dignity
0/5Respect for the inherent worth of persons
n/a
screening score · 2026-10-23
Human Oversight
4/5Humans remain in control of consequential decisions
AN-UC03-REQ-001: It is critical that autonomous networks (AN) components enable synchronous or asynchronous, interoperable exchange of feedback or information from peers regarding the decisions and choices related to AN behaviour.
7.3.1
screening score · 2026-10-23
Privacy
2/5Protection of personal data and private life
By making closed loops in edge domain autonomous, we also enable lesser orchestration delay, better privacy and flexibility for verticals
7.9
screening score · 2026-10-23
Transparency
4/5Openness about how AI systems work and are used
AN-UC01-REQ-003: It is critical that AN enable creation of reports on the use of knowledge bases, for consumption of humans and machines.
7.1.1
screening score · 2026-10-23
Enforcement
0/5Consequences and compliance mechanisms with real force
n/a
screening score · 2026-10-23
Impact Assessment
4/5Required assessment of an AI system's risks/impacts before or during use
AN-UC10-REQ-003: It is critical that, AN validates any changes to the application and network services in a sandbox environment before applying it in the network.
7.10.1
screening score · 2026-10-23