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    Network Lifecycle Management Agent

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    Sold by: XenonStack 
    The Network Lifecycle Management Multi-Agent System is an AI-driven platform for telecom operators that automates the end-to-end network lifecycle—from planning and deployment to real-time monitoring and optimization. Built on AWS, it uses collaborating AI agents to analyze network data, forecast demand, enable zero-touch provisioning, detect anomalies, and optimize performance across mobile, fixed, and cloud-native networks. The solution integrates with existing OSS/BSS systems to reduce operational effort, minimize outages, and improve network quality through scalable, secure, and autonomous operations. Built on NexaStack, XenonStack's physical AI and edge platform.

    Overview

    Built on NexaStack

    Network Lifecycle Management Agent runs on NexaStack, XenonStack's platform for physical AI and sovereign, edge deployment. NexaStack runs the planning, deployment and assurance agents under one policy layer, so operators move toward zero-touch networks while keeping control of every automated action.

    Telecom Network Lifecycle Management Challenge

    Modern telecom networks are expanding rapidly with 5G, IoT, cloud-native network functions, and edge deployments. Operators must manage planning, deployment, monitoring, and optimization across highly distributed, multi-vendor environments. Traditional OSS tools and manual processes struggle to keep pace, leading to slow capacity planning, error-prone deployments, reactive incident handling, inefficient resource utilization, and higher operational costs. As network data volumes grow beyond human-scale analysis, operators face increased outages, delayed service launches, and inconsistent quality of experience.

    Our Solution: AI-Driven Network Lifecycle Automation The Network Lifecycle Management Multi-Agent System is an AWS-native AI platform that automates the full telecom network lifecycle—from planning and deployment to real-time operations and optimization. Built on Amazon EKS, it uses specialized AI agents to enable zero-touch deployment, continuous monitoring, anomaly detection, and performance optimization. Leveraging services such as Amazon Bedrock, S3, CloudWatch, and IAM, the system analyzes network telemetry, forecasts demand, and executes closed-loop actions while integrating seamlessly with existing OSS/BSS and multi-vendor network environments.

    Key Benefits & Business Outcomes

    1. End-to-end automation across planning, deployment, operations, and optimization, reducing manual effort and operational complexity

    2. Faster network rollouts and service launches through zero-touch provisioning and automated configuration management

    3. Proactive operations with AI-driven monitoring, anomaly detection, and predictive maintenance to reduce outages and MTTR

    4. Continuous performance optimization and efficient resource utilization, lowering OPEX and improving ROI

    5. Secure, scalable, cloud-native architecture on AWS with built-in access control, encryption, and compliance readiness

    Ideal Users / Organizations Mobile and fixed-line telecom operators, network operations teams, NOC engineers, network planners, DevOps and automation teams, and service quality managers seeking to modernize network operations, reduce operational costs, improve reliability, and move toward zero-touch, autonomous network management on AWS.

    Part of XenonStack Decision Infrastructure: ElixirData (Context OS), ElixirClaw (Agentic OS), AgentGRC (governance, risk and compliance for AI agents) and NexaStack (physical and edge AI). Every decision follows one governed path: signal, context, policy, action and record, so risk and compliance teams can approve it for production.

    Highlights

    • AI-powered multi-agent architecture that automates the entire telecom network lifecycle with closed-loop, zero-touch operations
    • AWS-native, scalable, and secure design with seamless integration across OSS/BSS and multi-vendor network environments
    • Proactive assurance and optimization using real-time telemetry, anomaly detection, and predictive intelligence

    Details

    Delivery method

    Deployed on AWS
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