OBJECTIVE SELECTION FOR LLM-BASED NETWORK TROUBLESHOOTING AND MONITORING AGENTS

    公开(公告)号:US20250148290A1

    公开(公告)日:2025-05-08

    申请号:US18386833

    申请日:2023-11-03

    Abstract: In one implementation, a device receives an input request for a large language model-based troubleshooting agent for a network. The device selects an optimization criterion for the large language model-based troubleshooting agent based on the input request. The device provides the optimization criterion to the large language model-based troubleshooting agent to cause the large language model-based troubleshooting agent to select a particular large language model to process the input request based on the optimization criterion. The device sends, to a user interface, an indication of a result of the particular large language model processing the input request.

    USING A CURRICULUM FOR REINFORCEMENT LEARNING TO TRAIN AN LLM-BASED NETWORK TROUBLESHOOTING AGENT

    公开(公告)号:US20250148291A1

    公开(公告)日:2025-05-08

    申请号:US18388010

    申请日:2023-11-08

    Abstract: In one implementation, a device may determine how well a large language model-based troubleshooting agent for a network was able to perform during a first test having a first difficulty. The device may update the large language model-based troubleshooting agent using reinforcement learning based on how well the large language model-based troubleshooting agent was able to perform during the first test. The device may select a second difficulty for a second test based on how well the large language model-based troubleshooting agent was able to perform during the first test. The device may initiate the second test to assess how well the large language model-based troubleshooting agent is able to perform.

    GENERATING LONG-TERM NETWORK CHANGES FROM SLA VIOLATIONS

    公开(公告)号:US20230027995A1

    公开(公告)日:2023-01-26

    申请号:US17381343

    申请日:2021-07-21

    Abstract: In one embodiment, a device obtains information regarding temporary routing patches applied to a network. Each temporary routing patch implements a routing change in the network for a specified amount of time to avoid or mitigate against a service level agreement violation. The device evaluates, using the information regarding the temporary routing patches applied to the network, a plurality of replay scenarios for the network. The device determines, based on the plurality of replay scenarios, a long-term configuration change for the network. The device provides an indication of the long-term configuration change for display.

    AUTONOMOUS SYSTEM BOTTLENECK DETECTION

    公开(公告)号:US20220376998A1

    公开(公告)日:2022-11-24

    申请号:US17328205

    申请日:2021-05-24

    Abstract: In one embodiment, a supervisory service for a network obtains quality of experience metrics for application sessions of an online application. The supervisory service maps the application sessions to paths that traverse a plurality of autonomous systems. The supervisory service identifies, based in part on the quality of experience metrics, a particular autonomous system from the plurality of autonomous systems associated with a decreased quality of experience for the online application. The supervisory service causes application traffic for the online application to avoid the particular autonomous system.

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