Enterprise 5G Deployment Example

This comprehensive example demonstrates deploying a complete private 5G network with O-RAN components for an enterprise manufacturing facility using the Nephio O-RAN Claude Agents system.

🎯 Scenario Overview

Enterprise: Global Manufacturing Corp
Location: Manufacturing facility in Detroit, Michigan
Requirements:

• Ultra-low latency for industrial automation (< 5ms)
• High reliability (99.99% uptime)
• Private 5G network with 1000+ connected devices
• Integration with existing MES and ERP systems
• WG11 security compliance
• Energy efficiency target: > 0.6 Gbps/W

🏗️ Architecture

🚀 Deployment Walkthrough

Phase 1: Environment Preparation

Step 1: Infrastructure Validation

# Validate the enterprise environment
claude-agent dependency-doctor-agent "check dependencies"

# Expected environment:
# - 3 physical servers (32 cores, 128GB RAM each)
# - 10Gbps networking with SR-IOV support
# - GPU acceleration (NVIDIA A100)
# - Enterprise security requirements

Validation Output:

✓ Go 1.24.6 with FIPS 140-3 support
✓ Kubernetes 1.30+ (v1.30.2)
✓ Hardware acceleration (SR-IOV, DPDK)
✓ Network connectivity (10Gbps)
✓ Security compliance tools available
✓ 384 CPU cores total, 384GB RAM
✓ GPU acceleration available (NVIDIA A100)

Step 2: Security Baseline

# Establish security baseline for manufacturing environment
claude-agent security-compliance-agent "enforce_fips_mode"
claude-agent security-compliance-agent "apply_wg11_policies"

# Configure OT/IT network segmentation
claude-agent security-compliance-agent "apply_zero_trust_policies"

Phase 2: Kubernetes Infrastructure

Step 3: Cluster Deployment

# Create management cluster
claude-agent infrastructure-agent "create cluster"

# Deploy Nephio R5 components
claude-agent infrastructure-agent "install nephio"

# Configure enterprise storage
claude-agent infrastructure-agent "setup storage"

Cluster Configuration:

apiVersion: kind.x-k8s.io/v1alpha4
kind: Cluster
metadata:
  name: enterprise-manufacturing
nodes:
  - role: control-plane
    labels:
      node-type: control-plane
    extraMounts:
      - hostPath: /var/lib/manufacturing-data
        containerPath: /data
  - role: worker
    labels:
      node-type: edge-computing
      hardware: gpu-accelerated
    extraMounts:
      - hostPath: /dev/sriov
        containerPath: /dev/sriov
  - role: worker
    labels:
      node-type: ran-functions
      hardware: dpdk-enabled
  - role: worker
    labels:
      node-type: ot-integration
      security-zone: manufacturing-floor

Step 4: Network Configuration

# Setup SR-IOV and DPDK for high-performance networking
claude-agent config-management-agent "setup network"

# Configure network attachments for manufacturing
cat > manufacturing-network-attachments.yaml <<EOF
apiVersion: k8s.cni.cncf.io/v1
kind: NetworkAttachmentDefinition
metadata:
  name: manufacturing-fronthaul
  namespace: oran
spec:
  config: |
    {
      "cniVersion": "1.0.0",
      "type": "sriov",
      "name": "manufacturing-fronthaul",
      "vlan": 100,
      "spoofchk": "off",
      "trust": "on",
      "capabilities": {
        "ips": true,
        "mac": true
      },
      "ipam": {
        "type": "whereabouts",
        "range": "10.100.1.0/24"
      }
    }
---
apiVersion: k8s.cni.cncf.io/v1
kind: NetworkAttachmentDefinition
metadata:
  name: manufacturing-midhaul
  namespace: oran
spec:
  config: |
    {
      "cniVersion": "1.0.0",
      "type": "macvlan",
      "master": "eth1",
      "mode": "bridge",
      "ipam": {
        "type": "whereabouts",
        "range": "10.100.2.0/24"
      }
    }
EOF

kubectl apply -f manufacturing-network-attachments.yaml

Phase 3: O-RAN Network Functions Deployment

Step 5: Near-RT RIC Platform

# Deploy Near-RT RIC optimized for manufacturing
claude-agent network-functions-agent "deploy ric"

# Configure manufacturing-specific xApps
claude-agent network-functions-agent "deploy xapp"

Manufacturing-Optimized RIC Configuration:

apiVersion: v1
kind: ConfigMap
metadata:
  name: manufacturing-ric-config
  namespace: ricplt
data:
  ric-config.yaml: |
    ricPlatform:
      realTimeConstraints:
        maxLatency: 1ms
        jitterTolerance: 100us
      manufacturingIntegration:
        opcuaEnabled: true
        mesIntegration: true
        scadaIntegration: true
      xApps:
      - name: traffic-steering-manufacturing
        priority: high
        cpuLimit: "2"
        memoryLimit: "4Gi"
      - name: qos-management-urllc
        priority: critical
        cpuLimit: "4"
        memoryLimit: "8Gi"
        nodeSelector:
          hardware: dpdk-enabled

Step 6: Network Slice Deployment

# Deploy URLLC slice for critical manufacturing operations
claude-agent orchestrator-agent "deploy network slice urllc"

# Deploy eMBB slice for general connectivity
claude-agent orchestrator-agent "deploy network slice embb"

URLLC Network Slice Configuration:

apiVersion: nephio.org/v1alpha1
kind: NetworkSlice
metadata:
  name: manufacturing-urllc
  namespace: manufacturing
spec:
  sliceType: ultra-reliable-low-latency
  sliceId: '100001'
  priority: critical
  sites:
    - name: manufacturing-floor
      cu: 1
      du: 2
      ru: 8
  requirements:
    latency: 1ms
    reliability: 99.999
    availability: 99.99
    bandwidth: 100Mbps
  qosPolicy:
    priorityLevel: 1
    trafficClass: 'conversational'
    allocationRetentionPriority: 1
    guaranteedBitRate: 50Mbps
  manufacturingIntegration:
    protocols: ['OPC-UA', 'PROFINET', 'Modbus-TCP']
    deterministic: true
    timeSync: ieee1588v2

Step 7: O-RAN Components

# Deploy O-RAN CU with manufacturing optimizations
claude-agent network-functions-agent "deploy cu"

# Deploy O-RAN DU with DPDK acceleration
claude-agent network-functions-agent "deploy du"

# Configure O-RU connections (8 radio units)
for i in {1..8}; do
  claude-agent network-functions-agent "configure ru $i"
done

Phase 4: SMO and AI/ML Integration

Step 8: Non-RT RIC / SMO Deployment

# Deploy SMO with manufacturing analytics
claude-agent network-functions-agent "deploy smo"

# Deploy manufacturing-specific rApps
claude-agent network-functions-agent "deploy rapp"

Manufacturing Analytics rApp:

#!/usr/bin/env python3
"""
Manufacturing Analytics rApp for Predictive Maintenance
Integrates with MES and provides real-time equipment health monitoring
"""

import asyncio
import json
from datetime import datetime
from typing import Dict, List
import numpy as np
from sklearn.ensemble import IsolationForest
import requests
import paho.mqtt.client as mqtt

class ManufacturingAnalyticsRApp:
    def __init__(self):
        self.policy_client = PolicyManagementClient()
        self.mes_client = MESIntegrationClient()
        self.anomaly_detector = IsolationForest(contamination=0.1)
        self.equipment_data = {}

    async def process_equipment_data(self, data: Dict):
        """Process real-time equipment telemetry"""
        equipment_id = data.get('equipment_id')
        metrics = data.get('metrics', {})

        # Store historical data
        if equipment_id not in self.equipment_data:
            self.equipment_data[equipment_id] = []

        self.equipment_data[equipment_id].append({
            'timestamp': datetime.utcnow(),
            'vibration': metrics.get('vibration', 0),
            'temperature': metrics.get('temperature', 0),
            'pressure': metrics.get('pressure', 0),
            'current': metrics.get('current', 0)
        })

        # Anomaly detection
        if len(self.equipment_data[equipment_id]) > 100:
            recent_data = self.equipment_data[equipment_id][-100:]
            features = np.array([[d['vibration'], d['temperature'],
                                 d['pressure'], d['current']]
                                for d in recent_data])

            anomaly_score = self.anomaly_detector.fit_predict(features)

            if anomaly_score[-1] == -1:  # Anomaly detected
                await self.handle_equipment_anomaly(equipment_id, metrics)

    async def handle_equipment_anomaly(self, equipment_id: str, metrics: Dict):
        """Handle detected equipment anomalies"""
        # Create A1 policy for traffic prioritization
        policy = {
            "policy_id": f"emergency-{equipment_id}-{datetime.utcnow().timestamp()}",
            "policy_type": "TrafficPrioritization",
            "policy_data": {
                "equipment_id": equipment_id,
                "priority": "critical",
                "guaranteed_bandwidth": "10Mbps",
                "max_latency": "500us"
            }
        }

        await self.policy_client.create_policy(policy)

        # Notify MES system
        await self.mes_client.send_maintenance_alert(
            equipment_id=equipment_id,
            severity="high",
            predicted_failure_time="2 hours",
            recommended_action="Schedule predictive maintenance"
        )

        print(f"Anomaly detected for equipment {equipment_id}")
        print(f"Policy created: {policy['policy_id']}")

if __name__ == "__main__":
    rapp = ManufacturingAnalyticsRApp()
    asyncio.run(rapp.start())

Step 9: Edge AI/ML Deployment

# Deploy Kubeflow for edge AI/ML
claude-agent data-analytics-agent "setup ml pipeline"

# Deploy manufacturing-specific AI models
claude-agent performance-optimization-agent "deploy_optimized_ai_models"

Edge AI Deployment Configuration:

apiVersion: serving.kserve.io/v1beta1
kind: InferenceService
metadata:
  name: predictive-maintenance-model
  namespace: manufacturing
spec:
  predictor:
    model:
      modelFormat:
        name: onnx
      runtime: kserve-onnxruntime-gpu
      storageUri: 's3://manufacturing-models/predictive-maintenance/v2.0'
      resources:
        requests:
          cpu: '4'
          memory: '8Gi'
          nvidia.com/gpu: '1'
        limits:
          cpu: '8'
          memory: '16Gi'
          nvidia.com/gpu: '1'
    minReplicas: 2
    maxReplicas: 10
    scaleTarget: 10 # 10ms target latency
    scaleMetric: latency
  transformer:
    containers:
      - name: feature-extractor
        image: manufacturing/feature-extractor:v1.0
        env:
          - name: FEATURE_WINDOW
            value: '30s'
          - name: SAMPLING_RATE
            value: '1000Hz'

Phase 5: Monitoring and Analytics

Step 10: Comprehensive Monitoring

# Deploy monitoring stack optimized for manufacturing
claude-agent monitoring-analytics-agent "setup monitoring"

# Configure manufacturing-specific dashboards
claude-agent monitoring-analytics-agent "import dashboards"

Manufacturing KPI Dashboard Configuration:

{
  "dashboard": {
    "title": "Manufacturing 5G Performance Dashboard",
    "uid": "manufacturing-5g-kpis",
    "tags": ["manufacturing", "5g", "o-ran"],
    "panels": [
      {
        "title": "URLLC Slice Latency",
        "type": "graph",
        "targets": [
          {
            "expr": "histogram_quantile(0.99, oran_urllc_latency_histogram)",
            "legendFormat": "P99 Latency (URLLC)"
          }
        ],
        "yAxes": [
          {
            "unit": "ms",
            "max": 5
          }
        ],
        "alert": {
          "conditions": [
            {
              "query": { "queryType": "" },
              "reducer": { "params": [], "type": "last" },
              "evaluator": { "params": [1], "type": "gt" }
            }
          ],
          "executionErrorState": "alerting",
          "noDataState": "no_data",
          "frequency": "10s",
          "handler": 1,
          "name": "Manufacturing URLLC Latency Alert",
          "message": "URLLC latency exceeds 1ms threshold"
        }
      },
      {
        "title": "Equipment Connectivity",
        "type": "stat",
        "targets": [
          {
            "expr": "count(up{job=\"manufacturing-equipment\"} == 1)",
            "legendFormat": "Connected Devices"
          }
        ],
        "fieldConfig": {
          "defaults": {
            "thresholds": {
              "steps": [
                { "color": "red", "value": 0 },
                { "color": "yellow", "value": 950 },
                { "color": "green", "value": 1000 }
              ]
            }
          }
        }
      },
      {
        "title": "Energy Efficiency",
        "type": "graph",
        "targets": [
          {
            "expr": "sum(rate(network_transmit_bytes_total[5m])*8/1e9) / sum(node_power_watts)",
            "legendFormat": "Gbps/Watt"
          }
        ],
        "yAxes": [
          {
            "unit": "gbps/watt",
            "min": 0.6
          }
        ]
      },
      {
        "title": "Predictive Maintenance Alerts",
        "type": "table",
        "targets": [
          {
            "expr": "manufacturing_equipment_anomaly_score > 0.8",
            "format": "table",
            "instant": true
          }
        ]
      }
    ]
  }
}

Step 11: Data Analytics Pipeline

# Setup manufacturing data pipeline
claude-agent data-analytics-agent "setup kafka"
claude-agent data-analytics-agent "deploy kpi calculator"

# Configure MES integration
kubectl apply -f - <<EOF
apiVersion: v1
kind: ConfigMap
metadata:
  name: mes-integration-config
  namespace: manufacturing
data:
  integration.yaml: |
    mes:
      endpoint: "https://mes.manufacturing.corp/api/v1"
      authentication:
        type: oauth2
        clientId: "5g-network-integration"
        tokenEndpoint: "https://auth.manufacturing.corp/oauth/token"
      dataMapping:
        equipmentStatus: "/equipment/{id}/status"
        productionMetrics: "/production/metrics"
        qualityData: "/quality/measurements"
    realTimeSync:
      enabled: true
      syncInterval: 1s
      batchSize: 100
EOF

Phase 6: Testing and Validation

Step 12: End-to-End Testing

# Run comprehensive manufacturing-specific tests
claude-agent testing-validation-agent "run_complete_test_suite"

# Test URLLC slice performance
claude-agent testing-validation-agent "test urllc latency requirements"

# Validate equipment connectivity
claude-agent testing-validation-agent "test equipment connectivity 1000 devices"

Custom Manufacturing Tests:

#!/bin/bash
# Manufacturing-specific validation tests

echo "=== Manufacturing 5G Network Validation ==="

# Test 1: URLLC Latency Requirements
echo "Testing URLLC latency requirements (<1ms)..."
LATENCY=$(kubectl exec -n manufacturing test-pod -- ping -c 100 urllc-gateway.manufacturing | \
  grep "min/avg/max" | awk -F'/' '{print $5}')

if (( $(echo "$LATENCY < 1" | bc -l) )); then
  echo "✓ URLLC latency: ${LATENCY}ms (requirement: &lt;1ms)"
else
  echo "✗ URLLC latency: ${LATENCY}ms exceeds requirement"
  exit 1
fi

# Test 2: Device Connectivity
echo "Testing device connectivity (target: 1000 devices)..."
CONNECTED_DEVICES=$(kubectl get pods -n manufacturing -l type=manufacturing-device | \
  grep Running | wc -l)

if [ $CONNECTED_DEVICES -ge 1000 ]; then
  echo "✓ Connected devices: ${CONNECTED_DEVICES}/1000"
else
  echo "✗ Connected devices: ${CONNECTED_DEVICES}/1000 - below target"
fi

# Test 3: Energy Efficiency
echo "Testing energy efficiency (target: >0.6 Gbps/W)..."
EFFICIENCY=$(kubectl exec -n monitoring prometheus-0 -- \
  promtool query instant 'sum(rate(network_transmit_bytes_total[5m])*8/1e9) / sum(node_power_watts)' | \
  grep -oE '[0-9]+\.[0-9]+' | head -1)

if (( $(echo "$EFFICIENCY > 0.6" | bc -l) )); then
  echo "✓ Energy efficiency: ${EFFICIENCY} Gbps/W (requirement: >0.6)"
else
  echo "✗ Energy efficiency: ${EFFICIENCY} Gbps/W below requirement"
fi

# Test 4: AI/ML Inference Performance
echo "Testing AI/ML inference latency (target: &lt;10ms P99)..."
AI_LATENCY=$(kubectl logs -n manufacturing predictive-maintenance-model --tail=1000 | \
  grep "inference_latency_p99" | tail -1 | awk '{print $3}')

if (( $(echo "$AI_LATENCY < 10" | bc -l) )); then
  echo "✓ AI/ML inference P99: ${AI_LATENCY}ms (requirement: &lt;10ms)"
else
  echo "✗ AI/ML inference P99: ${AI_LATENCY}ms exceeds requirement"
fi

echo "=== Manufacturing Validation Complete ==="

Step 13: Security Compliance Validation

# Final security audit
claude-agent security-compliance-agent "full_security_audit"

# Validate OT/IT network segmentation
claude-agent security-compliance-agent "validate network segmentation"

# Check manufacturing-specific compliance
kubectl apply -f - <<EOF
apiVersion: v1
kind: ConfigMap
metadata:
  name: manufacturing-compliance-checklist
  namespace: security
data:
  checklist.yaml: |
    compliance:
      standards:
      - name: "IEC 62443"
        description: "Industrial communication networks security"
        status: "compliant"
        validatedAt: "2025-08-22T10:30:00Z"
      - name: "NIST Cybersecurity Framework"
        description: "Manufacturing sector cybersecurity"
        status: "compliant"
        validatedAt: "2025-08-22T10:30:00Z"
      - name: "O-RAN WG11"
        description: "O-RAN security specifications"
        status: "compliant"
        validatedAt: "2025-08-22T10:30:00Z"
      networkSecurity:
        otItSegmentation: true
        zeroTrustArchitecture: true
        encryptionInTransit: true
        encryptionAtRest: true
      accessControl:
        rbacEnabled: true
        multiFactorAuth: true
        privilegedAccessManagement: true
EOF

📊 Deployment Results

Performance Metrics Achieved

Metric	Requirement	Achieved	Status
URLLC Latency (P99)	<1ms	0.8ms	✅
Network Reliability	99.99%	99.997%	✅
Energy Efficiency	>0.6 Gbps/W	0.73 Gbps/W	✅
Device Connectivity	1000 devices	1000 devices	✅
AI/ML Inference (P99)	<10ms	7.2ms	✅
Deployment Time	<4 hours	2.5 hours	✅

Resource Utilization

Component	CPU Usage	Memory Usage	Storage Usage
O-RAN Components	60% (192/320 cores)	70% (268GB/384GB)	45% (45TB/100TB)
Edge AI/ML	25% (80/320 cores)	20% (76GB/384GB)	15% (15TB/100TB)
Monitoring & Analytics	10% (32/320 cores)	8% (30GB/384GB)	25% (25TB/100TB)
System Overhead	5% (16/320 cores)	2% (10GB/384GB)	15% (15TB/100TB)

Cost Analysis (Monthly)

Category	Cost
Infrastructure (Hardware amortization)	$8,500
Software Licenses (Nephio, O-RAN SC)	$0 (Open Source)
Network Connectivity	$1,200
Operations & Maintenance	$3,500
Energy Consumption	$2,800
Total Monthly Cost	$16,000
Cost per Connected Device	$16/device/month

🔧 Operational Procedures

Daily Operations Checklist

#!/bin/bash
# Daily operations checklist for manufacturing 5G network

echo "=== Daily Manufacturing 5G Network Health Check ==="

# Check critical services
kubectl get pods -n oran | grep -v Running && echo "⚠️ O-RAN services issue" || echo "✅ O-RAN services healthy"
kubectl get pods -n ricplt | grep -v Running && echo "⚠️ RIC services issue" || echo "✅ RIC services healthy"
kubectl get pods -n manufacturing | grep -v Running && echo "⚠️ Manufacturing services issue" || echo "✅ Manufacturing services healthy"

# Check device connectivity
CONNECTED_DEVICES=$(kubectl get pods -n manufacturing -l type=manufacturing-device | grep Running | wc -l)
echo "📱 Connected devices: $CONNECTED_DEVICES/1000"

# Check network slice performance
URLLC_LATENCY=$(kubectl exec -n monitoring prometheus-0 -- promtool query instant 'histogram_quantile(0.99, oran_urllc_latency_histogram)' | grep -oE '[0-9]+\.[0-9]+')
echo "⚡ URLLC P99 latency: ${URLLC_LATENCY}ms"

# Check energy efficiency
EFFICIENCY=$(kubectl exec -n monitoring prometheus-0 -- promtool query instant 'sum(rate(network_transmit_bytes_total[5m])*8/1e9) / sum(node_power_watts)' | grep -oE '[0-9]+\.[0-9]+')
echo "🔋 Energy efficiency: ${EFFICIENCY} Gbps/W"

# Check security status
kubectl get networkpolicies -A | wc -l | xargs echo "🔒 Network policies active:"

echo "=== Daily Check Complete ==="

Maintenance Procedures

Weekly Maintenance

• Update Grafana dashboards with latest KPIs
• Review and rotate certificates
• Analyze energy consumption trends
• Update AI/ML models with latest training data
• Review security audit logs

Monthly Maintenance

• Kubernetes cluster updates and patches
• O-RAN component updates
• Performance optimization review
• Capacity planning analysis
• Disaster recovery testing

Quarterly Maintenance

• Major version updates (Nephio R5, O-RAN L Release)
• Security compliance re-certification
• Hardware refresh planning
• Network expansion planning
• Cost optimization review

🚨 Troubleshooting Guide

Common Issues and Solutions

Issue: High URLLC Latency

Symptoms: Latency >1ms affecting manufacturing operations Diagnosis:

# Check network path latency
kubectl exec -n manufacturing test-pod -- traceroute urllc-gateway
# Check CPU throttling on RAN components
kubectl top pods -n oran
# Check for network congestion
kubectl exec -n monitoring prometheus-0 -- promtool query instant 'rate(network_receive_bytes_total[1m])'

Resolution:

# Scale up RAN components
kubectl scale deployment oran-du --replicas=4 -n oran
# Apply traffic shaping
claude-agent performance-optimization-agent "optimize urllc traffic"
# Enable DPDK if not already enabled
kubectl patch deployment oran-du -n oran -p '{"spec":{"template":{"spec":{"containers":[{"name":"du","env":[{"name":"DPDK_ENABLED","value":"true"}]}]}}}}'

Issue: Device Connectivity Problems

Symptoms: Manufacturing devices losing connection Diagnosis:

# Check RU status
kubectl get pods -n oran -l component=ru
# Check SR-IOV configuration
kubectl get sriovnetworknodepolicies -A
# Check network attachment definitions
kubectl get network-attachment-definitions -n manufacturing

Resolution:

# Restart affected RUs
kubectl rollout restart daemonset/oran-ru -n oran
# Reconfigure network attachments
claude-agent config-management-agent "setup network"

Emergency Procedures

Production Line Outage

1. Immediate Response (0-5 minutes)

   • Check manufacturing pod status
   • Verify URLLC slice connectivity
   • Enable backup connectivity if available

2. Short-term Mitigation (5-15 minutes)

   • Scale critical services
   • Reroute traffic through backup paths
   • Notify MES system of degraded performance

3. Resolution (15-60 minutes)

   • Identify root cause
   • Apply permanent fix
   • Validate full functionality restoration

📈 Success Metrics & ROI

Operational Improvements

• Reduced Downtime: 45% reduction in unplanned downtime
• Improved Efficiency: 12% increase in overall equipment effectiveness (OEE)
• Energy Savings: 18% reduction in energy consumption per unit produced
• Maintenance Optimization: 35% reduction in maintenance costs through predictive maintenance

ROI Analysis (Annual)

• Implementation Cost: $350,000 (hardware, software, deployment)
• Operational Savings: $420,000/year
• Productivity Gains: $280,000/year
• Energy Savings: $85,000/year
• Maintenance Savings: $125,000/year
• Total Annual Benefit: $910,000/year
• ROI: 160% in first year

Business Impact

• Production Capacity: Increased by 15% through optimized automation
• Quality Improvement: 25% reduction in defect rates
• Time to Market: 20% faster product development cycles
• Competitive Advantage: First in industry with private 5G manufacturing

This comprehensive enterprise example demonstrates how the Nephio O-RAN Claude Agents system can deliver significant business value through intelligent automation, advanced O-RAN technologies, and seamless enterprise integration.