Closed-Loop Network Change Agent: An O&M Innovation

In the digital era, networks are the backbone of enterprise operation and social development. As services expand and technologies evolve, network changes have become crucial for efficient network operation and service assurance. Traditional network change O&M models can't cope with complicated network environments and service demands. The rise of AI large models has introduced new solutions, enabling a closed-loop network change agent poised to transform traditional O&M.

Traditional Network Change O&M Pain Points

The traditional network change O&M system has many pain points that significantly restrict the efficiency and stability of enterprise network management.

• High threshold for O&M skills: Traditional network change O&M system relies heavily on O&M personnel for solution design and review. This requires deep knowledge of network topology, device configuration and management, and a precise understanding of network demands in different service scenarios. The shortage of qualified O&M professionals increases recruitment pressure and drives up labor costs.
• Lagging automation: In traditional network change O&M, core processes, such as pre-change inspection and configuration generation, rely almost entirely on manual operations, which are inefficient and also prone to human error. Without automated tools, it is difficult to fully and accurately evaluate the change impacts or identify potential risks in advance, increasing uncertainty during network changes.
• High risk of service interruption: In traditional O&M, the testing and verification phase, involves numerous complex steps, and manual verification is highly inefficient. If an issue is found during the test and rollback is required, manual operations may exceed the preset operation window, causing large-scale service interruptions, economic losses, and reputational damage to the enterprise. Manual verification is also subjective and limited in scope, making it difficult to detect subtle yet potentially critical issues that may affect long-term network stability.   

Closed-Loop Network Change Agent Solution

The closed-loop network change agent, powered by AI large models, generates network change solutions based on the agent architecture, makes trustworthy decisions based on digital twins, and completes the closed-loop process—pre-change inspection, change simulation, change execution, change verification, and post-change monitoring—through natural language concatenation.

• Automatic/semi-automatic change solution generation: With powerful data analysis and processing capabilities, the AI large model intelligently orchestrates network-related APIs to generate dedicated tools for pre-change inspection and post-change verification. These tools automatically detect network status, collect key data, and compare the data with preset standards to quickly identify potential problems, providing reliable early-stage evaluation and later-stage verification. The solution also supports natural language interaction, allowing O&M personnel to easily communicate with the agent and input network change demands and instructions. The agent converts these inputs into executable steps, seamlessly connects all steps, and automates the entire process, significantly reducing operational difficulty and communication costs for O&M personnel.
• Trustworthy decision-making mechanism: Leveraging digital twin technologies, the agent simulates network changes in a virtual environment (Fig. 1). It identifies potential issues such as network congestion and device compatibility conflicts, providing a scientific and reliable basis for making network change decisions and ensuring the security and reliability of the change operation from the outset.

• Reliable implementation guarantee: AI large models construct an execution chain of thought, integrating key steps such as pre-change checks, configuration changes, and post-change verification. Based on this chain, the agent can automatically and systematically perform operations according to preset standards and procedures, avoiding confusion and errors that may occur in manual operations.
• Atomic operation mechanism: The solution encapsulates the entire change execution process as an atomic operation. All operations are either completed in full or automatically rolled back to their pre-change state if a fault occurs, preventing network faults and data corruption caused by partial execution, and greatly improving execution stability and reliability.

Highlights of Network Change Agent

• Improvement in fault prevention, control, and efficiency: The closed-loop network change agent delivers breakthroughs in fault prevention, control and efficiency. Through automated and intelligent operations, it shortens traditional O&M change time from over 14.5 hours to just 4 hours, improving efficiency by more than 70%. This enables enterprises to complete network changes faster to meet rapid service development needs while minimizing service interruption windows, lowering risks, and improving both enterprise network service quality and user experience.
• Trustworthy decision-making and steady execution: Powered by a digital twin base, the agent ensures trustworthy decision-making. By simulating network changes in a virtual environment, the agent identifies obvious potential network faults and subtle performance impacts in advance, providing reference for decision-making. Robust change execution is guaranteed through an atomic operation mechanism that eliminates risks of major faults from operational errors or partial execution. In case of incidents, configurations can be quickly rolled back to restore the network to a stable state, reducing risks and losses from network changes.

The closed-loop network change agent represents a major shift in network O&M. It addresses long-standing challenges in traditional network change O&M through an intelligent and automated approach, greatly improving efficiency, reducing risks, and enhancing management quality. As AI technologies advance, the agent will expand into broader domains and scenarios, deeply integrate with IoT and cloud computing, and drive further innovations and breakthroughs in network O&M.