| Internet-Draft | AI Agent Use Cases and Requirements in 6 | January 2026 |
| Yu, et al. | Expires 9 July 2026 | [Page] |
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.¶
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Currently, with breakthroughs in large language models and multimodal 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.¶
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.¶
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.¶
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 (deployed on user devices), application AI Agents, network AI Agents (deployed within the future 6G network), operation management AI Agents, etc. For instance, terminal 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.¶
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 services, sensing services, AI/ML services, computing services, 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.¶
User Harry owns a smart robot named Ron and has a lovely pet dog called Bob. Bob needs to be walked twice daily. While away on a business trip, Harry sends his request through an operator portal (which could be an app, a mobile webpage, etc.) to the 6G network's AI Agent, expressing his intention for robot Ron to ensure Bob's safety during walks. The network AI Agent processes this request, determines that the task requires perception services and QoS-guaranteed services, and then distributes these services to the relevant network entities.¶
The network delivers AI Agents enabled intelligent calling services that revolutionize traditional voice communications. By integrating recognition and perception capabilities of AI Agents, it offers two key functionalities: 24/7 Intelligent Answering (handling calls during unreachability, e.g., flight/power-off modes with contextual responses) and Intelligent Answering Machine (managing calls during user unavailability, e.g., meetings, with call logging). These services operate under strict user authorization, allowing customization of voice tones, trigger conditions (e.g., flight mode activation), and data permissions (call records/summaries). For instance, when a subscriber enables the service, the network autonomously answers calls based on predefined preferences and provides post-call analytics.¶
When a disaster strikes, unpredictable challenges such as collapsed buildings, deformed roads, and communication outages make the rescue extremely complex. By leveraging 6G network AI Agents for rescue planning, the rescue efficiency can be significantly improved, maximizing the protection of victims‘ lives and personal property. In this case, the intent may be “execute the rescue mission with multiple rescue robots in a certain area”. Upon receiving the intent, the network AI agents initiate the rescue planning and decompose the rescue into multiple operations and other standardized 3GPP service. This may specifically include: road obstacle sensing (sensing service), multi-robot rescue route planning (AI inference service), training obstacle avoidance models (AI training service), real-time optimal route computation for rescue robots (computing service) and communication resource allocation for disaster zones (communication service).¶
AI agents are artificial entities that can perceive environments, make decisions, and act. The AI Agents have evolved to LLM-based versions, leveraging LLMs’ strengths in knowledge acquisition, reasoning, and planning to decompose complex tasks into collaborative sub-tasks via perception, intent understanding, and plan reflection (with feedback and human interaction for robustness). In 6G network, multi-agent systems address strict network demands of big events (e.g., national games with millions of participants over 15 days), where Operator A deploys AI agents for performance assurance. The workflow involves organizers submitting intent-based requirements (e.g., bandwidth, VIP service), AI agents decomposing tasks into network configuration, resource allocation, and real-time monitoring, service agents creating and refining action plans through reflection, and action agents executing via tools. During the event, agents collaborate to ensure VIP QoE, monitor KPIs, and auto-adjust networks upon warnings. This multi-agent collaboration fulfills 6G’s big-event needs while reducing labor, surpassing 5G’s limitations in real-time dynamic planning, frequent KPI collection, and plan reflection.¶
With telecom industries prioritizing personalized services, AI agents integrate with 6G network to boost efficiency and innovation. This use case involves Bob (a 6G user), who needs high-quality 6G network support for a 2 pm online meeting during his tomorrow’s Beijing-Chengdu train trip (departing 9 am). Assume that Operator A’s 6G-deployed AI agent enabling intent-based services, user-agent interaction, and third-party resource access via tool invocations. Bob sends his intent; the AI agent validates the intent, and fetches third-party data (e.g., train schedules) if needed, identifies possible routes and covering base stations, predicts meeting QoE, and pushes fee-included assurance packages. After Bob’s selection, the agent pre-configures the network, ensuring his optimal meeting experience during the journey.¶
The rapidly growing market for AI-driven traffic navigation/assistance (e.g., ADAS, autonomous vehicles) presents significant opportunities for 3GPP operators. 3GPP networks offer unique advantages: access to exclusive wide-area environmental/network data, distributed AI capabilities, low latency via edge computing, and the integration of communication-AI-sensing. They provide three service categories: Category 1 (local inferencing with vehicle/network data, low cost), Category 2 (added network sensing, moderate cost), and Category 3 (external data integration, comprehensive assistance). Core components include the AI Toolbox (pre-trained models/algorithms), network-based intelligent assistant (AI Agent interpreting intents and orchestrating services), and UE-side Intelligent Assistance Application Entity. The service flow involves UE registration, subscriber intent submission (e.g., safe navigation), AI Agent recommending customized services, subscriber selection, and real-time network service activation/monitoring to fulfill the intent (e.g., safe travel to destination).¶
A telecom operator integrates an AI-powered smart call assistance service into 6G network, leveraging in-network AI Agents to dynamically optimize voice/video call quality based on real-time network conditions, user intent, and historical data. Assume that the network AI capabilities (e.g., AI Agents), UE (smartphones/VoIP devices) with AI for real-time call condition/QoE monitoring, privacy-compliant user data sharing, and pre-trained AI models are deployed. The service flow starts with a user initiating a call; the UE’s AI monitors metrics like jitter and packet loss, requesting network adjustments if quality degrades. The 6G AI Agent generates optimizations (e.g., codec adjustments, bandwidth allocation) and validates effects (e.g., via digital twin). The UE provides QoE feedback, and the AI Agent continuously analyzes aggregated data, updating models if persistent issues (affecting single/multiple users) arise.¶
With the rapid advancement of technologies like smartphones 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.¶
AI-powered devices can interact with their environment—collecting data, making autonomous decisions, and executing actions. The 6G system will enhance AI agents by providing supplementary environmental data (e.g., real-time sensing for traffic awareness) and dynamic QoS updates for adaptive decision-making.Additionally, 6G must support secure AI agent authentication and inter-agent communication, as traditional identifiers like SUPI/IMSI may not suffice for dynamic AI functionalities. The rise of AI agents will also increase "horizontal traffic" between devices, enabling collaboration within agent groups and with third-party applications.¶
6G system could help to keep the family daily care and security, requiring advanced automation and management capabilities to maintain a comfortable and efficient living space. There will be more AI related applications and intelligent devices (e.g. robots, UAVs, autonomous vehicles) in the 6G era. Users will be able to express their requirements through natural language to convey their needs. In certain scenarios, multiple devices will need to collaborate to complete complex tasks. The 6G system can dynamically coordinate devices based on user's supply and demand requirements.¶
Lily's smartwatch AI agent continuously tracks her vital signs (heart rate, body temperature) during school hours. When detecting abnormal readings (elevated heart rate and temperature), the system automatically escalates monitoring frequency and initiates an emergency protocol by: (1) verifying authorization through the network, (2) selecting the optimal emergency contact (mother Emma, based on real-time proximity and availability data), and (3) coordinating with Emma's AI agent by sharing Lily's health metrics, location data, and environmental conditions. The network facilitates this process by providing positioning services, environmental sensing data, and secure data transmission between authorized AI agents. Emma's AI agent then calculates the fastest route to Lily's location while receiving continuous health updates, enabling prompt medical intervention. This scenario showcases the seamless integration of UE-based and network-based AI capabilities, including cross-domain data analysis, dynamic service invocation, and privacy-preserving emergency response mechanisms, ultimately delivering timely healthcare intervention while maintaining strict data security protocols.¶
6G aims to support diverse terminals (cars, AR glasses, etc.) with advanced services beyond connectivity, but current service interaction faces fragmentation and reliance on user pre-knowledge of available services. To address this, Operator O deploys AI agents in its 6G network for generic UE-network coordination. When user A drives into city X, the service access AI Agent proactively recommends a regional sensing service to enhance driving safety, which A accepts—receiving beyond-line-of-sight sensing data. After checking into a hotel, A’s connected AR glasses are notified of a regional computing service; with A’s permission, the AI agent coordinates application offloading/acceleration. The AI agent dynamically adjusts: warning of potential downgrades in poor network areas (advising local app execution) and providing communication quality maps/path recommendations in crowded spots, plus optional VIP QoS prioritization.¶
This use case presents a network-hosted personal safety AI agent in 6G network, dedicated to proactively safeguarding users by integrating real-time data (location, wearable biometrics like heart rate/accelerometer, calendar) and environmental data (e.g., area crime statistics) to build user risk profiles. Assume that Alex has subscribed to the service, granting explicit data access consent, configuring safety policies (emergency contacts, distress triggers), and 6G ensuring secure, low-latency agent hosting. When Alex walks through an unfamiliar, high-crime area after dark, the agent monitors his data, detects a sudden spike in heart rate and sprinting, and activates a high-alert state. It sends Alex a safety confirmation prompt and alerts his emergency contact Chloe. Unresponsive after 30 seconds, the agent auto-contacts emergency services with Alex’s real-time location and context.¶
A future shared embodied AI agent model will emerge, with entities like humanoid robots, robot dogs, and Automated Guided Vehicles (AGVs) deployed across cities for rental. This boosts their utilization and makes AI tech more accessible, requiring 6G’s high-speed, low-latency ne