Page 220 - AI for Good-Innovate for Impact Final Report 2024

AI for Good-Innovate for Impact

51�2�2� Future Work

To further enhance this use case, future endeavors include:

• Data Collection: Gathering more comprehensive datasets to refine AI models and
enhance operational accuracy.
• Model Development: Evolving the LLM technology to better understand and execute
complex network maintenance tasks.
• Extension and Variation: Expanding the application of AIChatOps to new operational
scenarios and use cases.
• Standards Development: Establishing industry standards for AI-powered network
maintenance practices.

51�3� Use case requirements
• REQ-Q1: It is critical to enable real-time intent recognition and scheduling capabilities
for network maintenance tasks.
• Note: Ensure the system can interpret natural language instructions swiftly to initiate and
schedule tasks without manual intervention.
• REQ-Q2: It is critical to implement mobile-compatible features for querying and executing
network tasks via natural language instructions.
• Note: This functionality empowers frontline personnel with on-the-go access to critical
operational data and actions, enhancing operational efficiency and flexibility.
• REQ-Q3: It is critical to integrate ChatOps
• Note: Enhance human-robot interaction by leveraging LLM's intent recognition to match
user queries with appropriate automation capabilities, optimizing operational workflows.
• REQ-Q4: It is critical to develop a multi-agent system to distribute and execute network
maintenance tasks autonomously.
• Note: This system efficiently handles complex tasks by assigning and coordinating
multiple agents.

51�4� Sequence diagram

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