Network Management Research Group C. Guo
Internet-Draft China Mobile
Intended status: Informational March 2025
Expires: 4 September 2025
Large Model based Agents for Network Operation and Maintenance
draft-chuyi-nmrg-ai-agent-network-00
Abstract
Current advancements in AI technologies, particularly large models,
have demonstrated immense potential in content generation, reasoning,
analysis and so on, providing robust technical support for network
automation and self-intelligence. However, in practical network
operations, challenges such as system isolation and fragmented data
lead to extensive manual, repetitive, and inefficient tasks, the
improvement of intelligence level is very necessary. This document
identifies typical scenarios requiring enhanced intelligence, and
explains how AI Agents and large model technologies can empower
networks to address operational pain points, reduce manual efforts,
and explore impacts on network data, system architectures, and
interfaces correspondingly. It further explores and summarizes
standardization efforts in implementation.
Requirements Language
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
document are to be interpreted as described in RFC 2119 [RFC2119].
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This Internet-Draft will expire on 2 September 2025.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
1.1. Large Models . . . . . . . . . . . . . . . . . . . . . . 2
1.2. AI Agent . . . . . . . . . . . . . . . . . . . . . . . . 3
2. Acronyms & Abbreviations . . . . . . . . . . . . . . . . . . 4
3. Use case . . . . . . . . . . . . . . . . . . . . . . . . . . 5
3.1. Scenario 1: Network Migration Operations . . . . . . . . 5
3.2. Scenario 2: Network Fault Handling . . . . . . . . . . . 5
4. Architecture and Functionality . . . . . . . . . . . . . . . 6
5. Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
6. Standardized Atomic Capabilities . . . . . . . . . . . . . . 8
7. References . . . . . . . . . . . . . . . . . . . . . . . . . 8
7.1. Informative References . . . . . . . . . . . . . . . . . 8
7.2. Normative References . . . . . . . . . . . . . . . . . . 8
Author's Address . . . . . . . . . . . . . . . . . . . . . . . . 8
1. Introduction
1.1. Large Models
Large models refer to AI systems based on deep learning techniques,
containing massive parameters (typically billions to trillions). It
is trained on large-scale datasets, and is capable of capturing
complex patterns and associations, demonstrating outstanding
abilities in natural language processing, image generation, decision-
making, and reasoning.
Recent breakthroughs in models like GPT-4 and DeepSeek have
continuously pushed technical boundaries and enhancing the
performance of models.Users can use the capabilities of large models
by accessing or deploying inference models, and combining with Fine
tuning, Prompt Learning, etc.
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The big model has been empowered in multiple vertical domains, like:
* Research: AlphaFold for protein structure prediction, Galactica
for scientific paper assistance. Industry: Generative design
(e.g., automotive/chip architecture optimization), automated code
development (GitHub Copilot).
* Finance: Risk prediction, automated report generation.
In the future, large models will also move towards embodied AI ,
embedding model capabilities into physical terminals such as robots
and autonomous driving, continuously building an open-source
developer ecosystem, opening up some model capability interfaces, and
promoting industry collaborative innovation.
1.2. AI Agent
Intelligent agent, as an important concept in the field of artificial
intelligence, refers to a system that can autonomously perceive the
environment, make decisions, and execute actions. It has basic
characteristics such as autonomy, interactivity, reactivity, and
adaptability, and can independently complete tasks in complex and
changing environments. Intelligent agents have the ability to learn
and make decisions. Through learning algorithms and data analysis,
they can extract useful information from massive amounts of data and
form their own knowledge base. In the decision-making process,
intelligent agents can comprehensively consider various factors and
use methods such as logical reasoning and probability statistics to
make the optimal decision. This ability gives intelligent agents a
significant advantage in solving complex problems.
There are four design patterns for intelligent agent workflow:
* Reflection: Let the agent review and revise the output generated
by themselves;
* Tool Use: LLM generates code, calls APIs, and performs practical
operations;
* Planning: Let the agent decompose complex tasks and execute them
according to the plan;
* Multi-agent Collaboration: Multiple agents play different roles
and collaborate to complete tasks.
At present, intelligent agents have been used in the following
scenarios:
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* Personal assistant:
- Cross platform task agent: Automatically organize emails,
schedule meetings, and manage schedules (such as Microsoft
Copilot).
- Life Butler: Adjust smart homes according to user habits and
recommend personalized health plans.
* Industry Intelligence:
- Financial advisory: Real time analysis of market data,
generation of investment portfolio recommendations, and
automatic execution of trades.
- Medical diagnosis: Provide dynamic treatment recommendations
based on the patient's medical history and real-time monitoring
data. Industrial operation and maintenance: Predicting
equipment failures and scheduling maintenance resources to
optimize production line efficiency.
* Virtual world interaction:
* - Game NPC: Intelligent characters with emotions and memories
(such as AI driven open world NPCs).
- Metaverse Guide: Help users explore virtual spaces and provide
personalized content recommendations.
* Scientific research:
* - Laboratory assistant: Automatically design experiments, analyze
data, and propose hypotheses (such as chemical synthesis
agents).
- Climate simulation: Coordinating multidimensional data models
to predict extreme weather and generate response plans.
2. Acronyms & Abbreviations
Large model: Machine learning models with large-scale parameters and
computing power are typically constructed from deep neural
networks, containing billions or even hundreds of billions of
parameters, capable of understanding text, images, speech, and
other content, and performing tasks such as text generation, image
generation, inference question answering, and scientific
prediction.
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AI Agent: An AI agent is an intelligent entity with autonomous
perception, decision-making, and execution capabilities, driven by
goals in dynamic environments.
3. Use case
3.1. Scenario 1: Network Migration Operations
The current network undergoes a large number of service migration or
device switchover every day/month, which have a high degree of
similarity in steps and processes, involving querying and filling a
large amount of data and configuration. There are two typical types
of migrations: service provisioning (for external service data
configuration) and migration change (for internal tasks such as route
publishing and network optimization). Large models naturally have
the ability to process and recognize massive amounts of data, and
intelligent agents can guide the process of each step like
experienced experts.
Automation via large models and agents can reduce errors and free
human resources. Key tasks include:
* Migration Plan Generation: Designing workflows and deployment
strategies.
* Plan Auditing: Checking configurations, compliance, and correcting
errors (e.g., typos, hallucinations).
* Automated Execution: Replacing manual configurations with AI-
generated scripts, call corresponding systems to finish tasks.
Taking the service provisioning scenario as an example, typically,
when doing migration, it was necessary to manually log in the device
configuration parameters. Now, through the interaction of the large
model, the large model generates a script to distribute the device,
also configure and audit it. The agent can call other systems, such
as digit twin platform for script testing, view the impact of the
changed parameters, and return to the assigned system to reduce
manual errors. Finally, based on the analysis of the results, it can
achieve automatic distribution when there shows no problem.
3.2. Scenario 2: Network Fault Handling
(Content to be expanded)
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4. Architecture and Functionality
Intelligent agents based on large models can automate network
operations by coordinating system scheduling and leveraging diverse
capabilities of large models. This process involves multiple
interactions with systems such as large models and network management
systems. Each agent has specialized functions, such as agents for
intent understanding or agents dedicated to fault localization and
demarcation in specific network scenarios. Current operational
systems already provide basic data support, foundational atomic
capabilities, and well-defined orchestration workflows for task
execution. However, most processes are manually connected, involve
repetitive mechanical work, and lack an intelligent coordination
"brain". See Figure 1.
Agents Network
+------------------------------------------------------+ +---------------------------+
| | | |
| +------------+ | |Network Systems & Platforms|
| | Perception | | | |
| +------+-----+ +-> AI Models |
| | | | |
| +--------v--------+ | | Atomic Capabilities |
| +----------+ | Brain | +----------+ | | |
| | Planning <+-+-+ +-+-+> Action | | | Tools |
| +----------+ | LLM | LVM | LSM | +----------+ <-+ |
| +------+--^-------+ | | Data |
| | | | | |
| +----v--+----+ | | |
| | Memory | | | |
| +------------+ | | |
+------------------------------------------------------+ +---------------------------+
Figure 1: Architecture of Large Model based Agents
Functions of Agents:
* Intent Recognition: Understand and interpret user input
intentions. Determine whether subsequent tasks require
identifying suitable agents or multi-turn dialogues to complete
intent recognition and parsing.
* Intent Classification and Analysis: Decompose tasks based on
recognized user intent.Categorize tasks according to different
functional requirements.
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* Perception: Proactively receive alarms, threshold-exceeding
notifications, or environmental change information, issuing
warnings when necessary.Accept task requests from other systems,
potentially involving multimodal data processing.
* Memory:
- Long-term memory: Stores user habits, domain-specific
processing experiences (e.g., failure/success cases,
encountered faults) in knowledge bases.
- Short-term memory: Caches temporary processing data (e.g.,
context).
* Agents perform reflection and error correction by interacting with
long-term memory and contextual information.
* Planning: Analyze and decompose intent based on task objectives
and learned knowledge. Orchestrate subtasks (e.g., breaking
complex problems into simpler ones). Identify required system
components (other agents, large models, APIs, etc.).
* Decision-Making: Finalize execution plans and match workflows to
current tasks. Generate instantiated, executable solutions by
aligning system components, data, and model strategies.
* Execution: Convert orchestrated results into network-
understandable commands. Execute tasks by mobilizing resources
and dynamically adjusting based on feedback.
* Multi-Agent Collaboration:
- Team Collaboration: Enable coordinated teamwork among multiple
agents.
- Competitive Collaboration: Manage competitive relationships to
avoid efficiency loss.
5. Data
The data that an agent can learn or perceive includes expert
knowledge in operation and maintenance processes, logs, configuration
rules, policy knowledge, case manuals, alarms, network topologies,
fault reports, and more.
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6. Standardized Atomic Capabilities
Atomic capability refers to a series of orchestrated workflows
designed to accomplish a subtask. It encapsulates various APIs,
exposes a unified interface and capabilities externally, and serves
as the minimal functional unit for achieving specific subtasks.
Atomic capabilities can be defined with standardized inputs and
outputs to facilitate cross-system communication and calls.
7. References
7.1. Informative References
[LLMbasedAgents]
Cheng, Y. Cheng., Zhang, C. Zhang., Zhang, Z. Zhang.,
Meng, X. Meng., and S. Hong. Hong, "Exploring Large
Language Model based Intelligent Agents: Definitions,
Methods, and Prospects.", January 2024.
7.2. Normative References
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119,
DOI 10.17487/RFC2119, March 1997,
<https://www.rfc-editor.org/info/rfc2119>.
Author's Address
Chuyi Guo
China Mobile
Beijing
100053
China
Email: guochuyi@chinamobile.com
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