AI Agent Use Cases and Requirements in 6G Network
draft-yu-dmsc-ai-agent-use-cases-in-6g-01
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| Document | Type | Active Internet-Draft (individual) | |
|---|---|---|---|
| Authors | Menghan Yu , Aijun Wang , Jinyan Li , Zhen Li | ||
| Last updated | 2026-01-11 | ||
| Replaces | draft-yu-ai-agent-use-cases-in-6g | ||
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draft-yu-dmsc-ai-agent-use-cases-in-6g-01
Network Working Group M. Yu
Internet-Draft A. Wang
Intended status: Informational J. Li
Expires: 16 July 2026 Z. Li
China Telecom
12 January 2026
AI Agent Use Cases and Requirements in 6G Network
draft-yu-dmsc-ai-agent-use-cases-in-6g-01
Abstract
This draft introduces use cases related to AI Agents in 6G networks,
primarily referencing the technical report of 3GPP SA1 R20 Study on
6G Use Cases and Service Requirements (TR 22.870). It also
elaborates on some of the requirements for introducing AI Agents into
6G networks from the perspective of operators.
Status of This Memo
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This Internet-Draft will expire on 16 July 2026.
Copyright Notice
Copyright (c) 2026 IETF Trust and the persons identified as the
document authors. All rights reserved.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
2. Use Cases . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2.1. Intent-based 6G Services Enabled by Network AI Agents . . 4
2.2. Device-Network Collaboration . . . . . . . . . . . . . . 5
2.3. Multiple Devices Collaboration . . . . . . . . . . . . . 5
2.4. Network-Application Collaboration . . . . . . . . . . . . 6
3. Potential Requirements for 6G Network . . . . . . . . . . . . 7
3.1. The Identity of AI Agents . . . . . . . . . . . . . . . . 7
3.2. Efficient Collaboration . . . . . . . . . . . . . . . . . 7
3.3. Cross-Domain Collaboration . . . . . . . . . . . . . . . 7
3.4. Registration and Discovery . . . . . . . . . . . . . . . 8
3.5. Service and Data Exposure . . . . . . . . . . . . . . . . 8
3.6. Reliability Assurance . . . . . . . . . . . . . . . . . . 8
3.7. High-performance Communication . . . . . . . . . . . . . 8
3.8. Security . . . . . . . . . . . . . . . . . . . . . . . . 8
3.9. Energy Efficiency . . . . . . . . . . . . . . . . . . . . 8
4. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . 9
5. Informative References . . . . . . . . . . . . . . . . . . . 9
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 9
1. Introduction
Currently, with breakthroughs in large language models and multi-
modal technologies, AI Agent has emerged as a major research focus in
the industry. Equipped with capabilities such as intent
understanding, action planning, decision-making, task execution, and
self-awareness, AI Agents can integrate environmental perception,
memory, tool invocation, and multi-agent collaboration to accomplish
complex tasks. They have already demonstrated significant value in
key fields like autonomous driving, intelligent customer service, and
smart home systems. In the 6G era, the introduction of AI Agent
technology will enable operators to fully leverage the potential of
mobile communication networks, significantly improving network
operational efficiency and user experience. As a result, AI Agents
are expected to become a key research focus in future 6G networks,
leading to deep integration between 6G and AI Agent technologies.
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In the 3GPP R20 standardization research for 6G, AI Agent has been
one of the most discussed and debated topics, whether in SA1's study
on 6G scenarios and requirements or SA2's research on network
architecture. In the SA1#109 meeting, 19 contributions related to AI
Agents were submitted, which include 16 new use cases, with 4 use
cases ultimately agreed. And a preliminary definition of AI Agent
from a capability perspective was adopted: "an automated intelligent
entity capable of e.g interacting with its environment, acquiring
contextual information, reasoning, self-learning, decision-making,
executing tasks (autonomously or in collaboration with other Al
Agents) to achieve a specific goal." In the SA1#110 meeting, more
than 30 contributions related to AI Agents were submitted, which
include 22 new use cases, with 7 ultimately agreed. In the SA2#172
meeting, how to achieve deep integration of 6G network with AI
technology (e.g. AI agent) has been explicitly included in the scope
of 6G architecture study.
This draft summarizes and categorizes the AI Agent-related use cases
in 6G networks, with a brief introduction provided in Section 2. In
Section 3, from an operator's perspective, we elaborate on the
potential requirements for introducing AI Agents into 6G networks,
which should be considered when designing the agent communication
related protocol in mobile communication network. In Section 4, we
conclude this draft.
2. Use Cases
AI Agents can be deployed at various locations within the 6G system.
Depending on their deployment positions, AI Agents in 6G can be
classified into On-device AI Agents, application AI Agents, network
AI Agents, operation management AI Agents, etc. For instance, on-
device AI Agents refer to those implemented on end-user devices,
while network AI Agents are those embedded within the 6G network.
This section summarizes and categorizes AI Agent-related use cases in
6G networks. Unlike AI Agents in the Internet domain, use cases
involving AI Agents in mobile communication networks place greater
emphasis on how network AI Agents can deliver 6G services to users,
as well as how different AI Agents within the 6G system coordinate
with each other.
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2.1. Intent-based 6G Services Enabled by Network AI Agents
By deploying AI Agents within 6G network, the 6G network can provide
users with intent-based services. These intelligent services may
represent combinations of multiple network capabilities, such as
communication, sensing, AI/ML, computing, data and more. Users only
need to express their intent to the 6G network, without requiring
specialized technical knowledge to decompose the intent into
technical requirements. In this context, 3GPP SA1 has formally
defined network intent as: Expectations including requirements, goals
and constraints without specifying how to achieve them.
Use Case A: Network-Wide Intent Fulfilment
The 6G system interprets high-level service intents and translate
such intent into concrete actions, including resource selection,
configuration, and coordination across communication, sensing,
computing, AI, and data capabilities utilizing network AI Agents.
Use Case B: Dynamic Service Customization and Optimization
The 6G network AI Agents dynamically adapt and optimize service
behavior based on real-time context information, user requirements,
and operational conditions in order to maintain desired service
objectives.
+-------------+
| User Intent |
+------+------+
|
+--------v----------+
| Network AI Agents |
+--------+----------+
|
+--------------v----------------+
| Service Orchestration & |
| Resource Optimization |
+-------------------------------+
Figure 2.1 Intent-Based Network Control
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2.2. Device-Network Collaboration
With the rapid advancement of technologies like smart phones and
lightweight large-scale AI models, capabilities of user devices have
significantly expanded, enabling autonomous execution of certain AI
tasks and independent decision-making. However, due to inherent
device limitations - including constrained computational resources
and battery capacity - deploying complex AI agents or performing
sophisticated AI tasks locally on devices remains challenging.
Consequently, investigating optimal collaboration mechanisms between
UE-based AI agents and network-based AI agents to accomplish complex
tasks represents a critical research direction for 6G networks.
Use Case A: Enhanced UE Intelligence via Network Support
The 6G system enables collaboration between on-device AI Agents and
Network AI Agents such that devices can leverage network-provided
context, analytics, and capabilities to improve local decision-making
and task execution.
Use Case B: Joint Device and Network Coordination Services
On-device AI Agents and Network AI Agents jointly support
coordination services in which adaptation decisions are distributed
across device-side and network-side components.
+-------------------+ +-----------------------+
| On-Device AI | <--------------> | 6G Network AI Agents |
| Agent (UE) | | and Network Support |
+-------------------+ +-----------------------+
| |
| |
+---------v---------+ +-----------v----------+
| Local Decisions | | Network Capabilities |
+-------------------+ +----------------------+
Figure 2.2 Device-Network AI Collaboration
2.3. Multiple Devices Collaboration
Under the powerful communication capabilities of 6G network, multiple
on-device AI Agents can collaborate with each other to accomplish
complex AI tasks. These AI Agents may from either the same
application or different applications.
Use Case A: Cross-Device Intelligent Coordination
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The 6G system securely exchanges information among multiple on-device
AI Agents to enable coordinated task execution across devices.
Use Case B: Group AI Agent Collaboration Domains
The 6G system dynamically establishes the collaboration domains that
allow authorized AI Agents to participate in group-based task
execution under defined security and policy constraints.
+-------------------+ +-------------------+ +-------------------+
| On-Device AI | | On-Device AI | | On-Device AI |
| Agent (Device 1) |<-->| Agent (Device 2) |<-->| Agent (Device N) |
+-------------------+ +-------------------+ +-------------------+
\ | /
\ | /
+--------------------------------------------------------+
| Dynamic Multi-Agent Secured Collaboration(DMSC) Domain |
+--------------------------------------------------------+
Figure 2.3 Multiple On-device AI Agents Collaboration
2.4. Network-Application Collaboration
The 6G network AI Agents and application AI Agents can fully
collaborate to accomplish network tasks. On one hand, AI agents
within the 6G network can invoke appropriate application AI Agents
based on service characteristics. On the other hand, the network AI
Agents can share network data and domain expertise with application
AI Agents, providing crucial data support for application AI Agents.
Use Case A: Integrated Service Orchestration
The 6G system enables cooperation between Network AI Agents and
application-layer AI Agents to support end-to-end service
orchestration through context sharing and coordinated control
actions.
Use Case B: Knowledge-Driven AI Enhancements
The 6G system provides mechanisms for exposing network-generated
knowledge, such as sensing data and telemetry, to authorized AI
Agents in support of advanced reasoning and service optimization.
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+---------------------+ +-----------------------+
| Application | | 6G Network AI Agents |
| AI Agents |<-------------->| |
+----------+----------+ +----------+------------+
| |
+-----------v---------+ +-----------v------------------+
| External Services | | Network Knowledge |
| and Applications | | (Sensing, AI, Computing,etc) |
+---------------------+ +------------------------------+
Figure 2.4 Network-Application AI Collaboration
3. Potential Requirements for 6G Network
In this section, we present potential requirements to 6G network that
may arise from the introduction of AI Agents in 6G mobile
communication network from an operator's perspective. Some of these
potential requirements have already been agreed by 3GPP, while others
have not yet been adopted by 3GPP.
3.1. The Identity of AI Agents
The 6G network shall support secure authentication, authorization,
and management mechanisms for AI Agents' digital identities. These
AI Agents include on-device AI Agents, 3rd party AI Agents, Network
AI Agents, etc. A robust identity management mechanism is the
prerequisite for interactions between users and AI Agents, as well as
between different AI Agents.
3.2. Efficient Collaboration
The 6G network shall support efficient collaboration between
different AI Agents and between AI Agents and the tools. This
include: developing agent communication protocols better suited for
6G network characteristics, supporting multi-modal data (such as
text, audio, video, etc.) interactions, enabling rapid transmission
of massive data volumes, etc.
3.3. Cross-Domain Collaboration
Future AI agents will be ubiquitous, forming a device-network-
industry end-to-end ecosystem. 6G network shall support the cross-
domain collaboration of AI agents, including the device domain, RAN
domain, core network domain, operation and management domain,
application domain, etc.
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3.4. Registration and Discovery
The 6G network shall support mechanisms for on-device AI Agents, 3rd
party AI Agent, Network AI Agents and tools to register their
attributes to 6G network, which enables efficient, cross-platforms
and cross-domain AI Agents and tools discovery. This may different
from the discovery mechanism in existing agent communication related
protocol (e.g. NRF discovery mechanism).
3.5. Service and Data Exposure
The 6G network shall support secure mechanisms to expose the 6G
services (e.g. sensing service, computing service, AI/ML service,
etc.) and network data (e.g. sensing data, positioning data, etc.) to
3rd party AI Agents.
3.6. Reliability Assurance
The 6G network shall be able to provide mechanisms (e.g. network
digital twin) to ensure the reliability and the validity of the
decisions made by the AI Agents. The decisions made by the AI Agents
in 6G network may directly change the network status, parameters,
configurations. Only decisions that have been verified for
reliability can be executed to change the network environment.
3.7. High-performance Communication
The 6G network shall enable high-performance communication, which may
include low latency, high band-width, ultra-high data rate, etc.
This is crucial for numerous scenarios such as device-network
collaboration, network-application collaboration.
3.8. Security
The security of AI Agents communication in 6G includes the data
protection and user consent. Data privacy means the 6G network shall
support end-to-end encryption for the interactions between AI Agents
to ensure robust data protection and privacy security for sensitive
information. Besides, 6G network shall be able to provide mechanisms
to collect the user consent for the local data collection.
3.9. Energy Efficiency
The 6G network shall be able to provide mechanisms to optimize the
communication between AI Agents (especially for the on-device AI
Agents) to reduce energy consumption.
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4. Conclusion
AI Agents are expected to represent a critical innovation vector for
6G. This draft explores the transformative potential of AI Agents in
6G network, outlining key use cases and operational requirements from
an operator's perspective. When designing agent communication
related protocols for 6G network, the aforementioned requirements
should be thoroughly considered and incorporated into the protocol
architecture.
5. Informative References
[TR_22.870]
"3GPP TR 22.870, "Study on 6G Use Cases and Service
Requirements", 2025.".
Authors' Addresses
Menghan Yu
China Telecom
Beiqijia Town, Changping District
Beijing
Beijing, 102209
China
Email: yumh1@chinatelecom.cn
Aijun Wang
China Telecom
Beiqijia Town, Changping District
Beijing
Beijing, 102209
China
Email: wangaj3@chinatelecom.cn
Jinyan Li
China Telecom
Beiqijia Town, Changping District
Beijing
Beijing, 102209
China
Email: lijinyan@chinatelecom.cn
Zhen Li
China Telecom
Beiqijia Town, Changping District
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Beijing
Beijing, 102209
China
Email: liz779@chinatelecom.cn
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