Agentic AI on Kubernetes: Advanced Orchestration, Deployment, and Scaling Strategies for Autonomous AI Systems

Agentic AI represents the next evolution in artificial intelligence, where autonomous agents can reason, plan, and execute complex tasks independently. Deploying these sophisticated AI systems at scale requires robust orchestration platforms, and Kubernetes has emerged as the de facto standard for managing containerized Agentic AI workloads. This comprehensive technical guide explores advanced deployment patterns, optimization strategies, and production-ready implementations for Agentic AI systems on Kubernetes.

Understanding Agentic AI Architecture and Kubernetes Integration

Agentic AI systems differ fundamentally from traditional machine learning models by incorporating decision-making capabilities, memory management, tool usage, and autonomous task execution. These systems require sophisticated orchestration to manage multiple interacting components, dynamic resource allocation, and complex communication patterns.

Core Components of Agentic AI Systems

Agent Runtime Environment:
Agentic AI systems typically consist of multiple interconnected components that must be orchestrated effectively:

• Reasoning Engine: LLM-based decision making and planning
• Memory Systems: Vector databases and episodic memory storage
• Tool Integration: External API and service interactions
• Task Orchestration: Workflow management and execution coordination
• Monitoring and Observability: Real-time performance tracking

# Agentic AI system namespace configuration
apiVersion: v1
kind: Namespace
metadata:
  name: agentic-ai
  labels:
    purpose: ai-agents
    security.kubernetes.io/enforce: restricted
    pod-security.kubernetes.io/enforce: restricted
---
apiVersion: v1
kind: ResourceQuota
metadata:
  name: agentic-ai-quota
  namespace: agentic-ai
spec:
  hard:
    requests.cpu: "50"
    requests.memory: 200Gi
    requests.nvidia.com/gpu: "8"
    limits.cpu: "100"
    limits.memory: 400Gi
    limits.nvidia.com/gpu: "8"
    persistentvolumeclaims: "20"

Advanced Agent Deployment Patterns

Multi-Agent System Architecture:

# Agent coordinator deployment
apiVersion: apps/v1
kind: Deployment
metadata:
  name: agent-coordinator
  namespace: agentic-ai
  labels:
    component: coordinator
    tier: control-plane
spec:
  replicas: 3
  strategy:
    type: RollingUpdate
    rollingUpdate:
      maxSurge: 1
      maxUnavailable: 0
  selector:
    matchLabels:
      app: agent-coordinator
  template:
    metadata:
      labels:
        app: agent-coordinator
        component: coordinator
      annotations:
        prometheus.io/scrape: "true"
        prometheus.io/port: "8080"
    spec:
      serviceAccountName: agent-coordinator
      securityContext:
        runAsNonRoot: true
        runAsUser: 1000
        fsGroup: 1000
      containers:
      - name: coordinator
        image: agentic-ai/coordinator:v2.1.0
        ports:
        - containerPort: 8080
          name: http-metrics
        - containerPort: 9090
          name: grpc
        env:
        - name: AGENT_POOL_SIZE
          value: "10"
        - name: MAX_CONCURRENT_TASKS
          value: "50"
        - name: REDIS_URL
          valueFrom:
            secretKeyRef:
              name: redis-credentials
              key: url
        - name: VECTOR_DB_ENDPOINT
          valueFrom:
            configMapKeyRef:
              name: agent-config
              key: vector_db_endpoint
        resources:
          requests:
            cpu: 2000m
            memory: 4Gi
          limits:
            cpu: 4000m
            memory: 8Gi
        readinessProbe:
          httpGet:
            path: /health/ready
            port: 8080
          initialDelaySeconds: 10
          periodSeconds: 5
        livenessProbe:
          httpGet:
            path: /health/live
            port: 8080
          initialDelaySeconds: 30
          periodSeconds: 10
        volumeMounts:
        - name: agent-config
          mountPath: /etc/agent
          readOnly: true
        - name: tls-certs
          mountPath: /etc/tls
          readOnly: true
      volumes:
      - name: agent-config
        configMap:
          name: agent-config
      - name: tls-certs
        secret:
          secretName: agent-tls

GPU-Accelerated Agent Workloads

LLM-Powered Agent Deployment:

# GPU-accelerated reasoning agent
apiVersion: apps/v1
kind: Deployment
metadata:
  name: reasoning-agent
  namespace: agentic-ai
spec:
  replicas: 2
  selector:
    matchLabels:
      app: reasoning-agent
  template:
    metadata:
      labels:
        app: reasoning-agent
        component: reasoning
    spec:
      nodeSelector:
        accelerator: nvidia-tesla-v100
      tolerations:
      - key: nvidia.com/gpu
        operator: Equal
        value: "true"
        effect: NoSchedule
      containers:
      - name: reasoning-engine
        image: agentic-ai/reasoning-engine:v1.5.0
        ports:
        - containerPort: 8000
          name: inference
        env:
        - name: MODEL_PATH
          value: "/models/llama-2-70b-chat"
        - name: CUDA_VISIBLE_DEVICES
          value: "0,1"
        - name: TENSOR_PARALLEL_SIZE
          value: "2"
        - name: MAX_NUM_SEQS
          value: "128"
        - name: GPU_MEMORY_UTILIZATION
          value: "0.9"
        resources:
          requests:
            nvidia.com/gpu: 2
            cpu: 8000m
            memory: 32Gi
          limits:
            nvidia.com/gpu: 2
            cpu: 16000m
            memory: 64Gi
        volumeMounts:
        - name: model-storage
          mountPath: /models
          readOnly: true
        - name: cache-volume
          mountPath: /cache
      volumes:
      - name: model-storage
        persistentVolumeClaim:
          claimName: model-pvc
      - name: cache-volume
        emptyDir:
          sizeLimit: 50Gi
---
apiVersion: v1
kind: Service
metadata:
  name: reasoning-agent-service
  namespace: agentic-ai
spec:
  selector:
    app: reasoning-agent
  ports:
  - port: 8000
    targetPort: 8000
    name: inference
  type: ClusterIP

Vector Database and Memory Management

High-Performance Vector Database Deployment:

# Qdrant vector database for agent memory
apiVersion: apps/v1
kind: StatefulSet
metadata:
  name: qdrant-cluster
  namespace: agentic-ai
spec:
  serviceName: qdrant-headless
  replicas: 3
  selector:
    matchLabels:
      app: qdrant
  template:
    metadata:
      labels:
        app: qdrant
    spec:
      containers:
      - name: qdrant
        image: qdrant/qdrant:v1.7.0
        ports:
        - containerPort: 6333
          name: http
        - containerPort: 6334
          name: grpc
        env:
        - name: QDRANT__CLUSTER__ENABLED
          value: "true"
        - name: QDRANT__CLUSTER__P2P__PORT
          value: "6335"
        - name: QDRANT__STORAGE__STORAGE_PATH
          value: "/qdrant/storage"
        resources:
          requests:
            cpu: 2000m
            memory: 8Gi
          limits:
            cpu: 4000m
            memory: 16Gi
        volumeMounts:
        - name: qdrant-storage
          mountPath: /qdrant/storage
        readinessProbe:
          httpGet:
            path: /readiness
            port: 6333
          initialDelaySeconds: 10
          periodSeconds: 5
  volumeClaimTemplates:
  - metadata:
      name: qdrant-storage
    spec:
      accessModes: ["ReadWriteOnce"]
      storageClassName: fast-ssd
      resources:
        requests:
          storage: 100Gi

Redis-Based Agent State Management:

# Redis cluster for agent state and caching
apiVersion: apps/v1
kind: StatefulSet
metadata:
  name: redis-cluster
  namespace: agentic-ai
spec:
  serviceName: redis-headless
  replicas: 6
  selector:
    matchLabels:
      app: redis-cluster
  template:
    metadata:
      labels:
        app: redis-cluster
    spec:
      containers:
      - name: redis
        image: redis:7.2-alpine
        command:
        - redis-server
        - /etc/redis/redis.conf
        - --cluster-enabled
        - "yes"
        - --cluster-config-file
        - /data/nodes.conf
        - --cluster-node-timeout
        - "5000"
        - --appendonly
        - "yes"
        ports:
        - containerPort: 6379
          name: client
        - containerPort: 16379
          name: gossip
        resources:
          requests:
            cpu: 1000m
            memory: 2Gi
          limits:
            cpu: 2000m
            memory: 4Gi
        volumeMounts:
        - name: redis-data
          mountPath: /data
        - name: redis-config
          mountPath: /etc/redis
      volumes:
      - name: redis-config
        configMap:
          name: redis-config
  volumeClaimTemplates:
  - metadata:
      name: redis-data
    spec:
      accessModes: ["ReadWriteOnce"]
      storageClassName: fast-ssd
      resources:
        requests:
          storage: 20Gi

Advanced Agent Communication Patterns

Event-Driven Agent Communication:

# Apache Kafka for agent event streaming
apiVersion: apps/v1
kind: StatefulSet
metadata:
  name: kafka-cluster
  namespace: agentic-ai
spec:
  serviceName: kafka-headless
  replicas: 3
  selector:
    matchLabels:
      app: kafka
  template:
    metadata:
      labels:
        app: kafka
    spec:
      containers:
      - name: kafka
        image: confluentinc/cp-kafka:7.4.0
        ports:
        - containerPort: 9092
          name: kafka
        - containerPort: 9093
          name: kafka-external
        env:
        - name: KAFKA_BROKER_ID
          valueFrom:
            fieldRef:
              fieldPath: metadata.annotations['kafka.apache.org/broker-id']
        - name: KAFKA_ZOOKEEPER_CONNECT
          value: "zookeeper-service:2181"
        - name: KAFKA_LISTENERS
          value: "INTERNAL://0.0.0.0:9092,EXTERNAL://0.0.0.0:9093"
        - name: KAFKA_ADVERTISED_LISTENERS
          value: "INTERNAL://$(POD_NAME).kafka-headless:9092,EXTERNAL://$(POD_IP):9093"
        - name: KAFKA_LISTENER_SECURITY_PROTOCOL_MAP
          value: "INTERNAL:PLAINTEXT,EXTERNAL:PLAINTEXT"
        - name: KAFKA_INTER_BROKER_LISTENER_NAME
          value: "INTERNAL"
        - name: KAFKA_OFFSETS_TOPIC_REPLICATION_FACTOR
          value: "3"
        - name: KAFKA_TRANSACTION_STATE_LOG_REPLICATION_FACTOR
          value: "3"
        - name: KAFKA_LOG_RETENTION_HOURS
          value: "168"
        - name: KAFKA_LOG_SEGMENT_BYTES
          value: "1073741824"
        - name: POD_NAME
          valueFrom:
            fieldRef:
              fieldPath: metadata.name
        - name: POD_IP
          valueFrom:
            fieldRef:
              fieldPath: status.podIP
        resources:
          requests:
            cpu: 1000m
            memory: 2Gi
          limits:
            cpu: 2000m
            memory: 4Gi
        volumeMounts:
        - name: kafka-data
          mountPath: /var/lib/kafka/data
  volumeClaimTemplates:
  - metadata:
      name: kafka-data
    spec:
      accessModes: ["ReadWriteOnce"]
      storageClassName: fast-ssd
      resources:
        requests:
          storage: 50Gi

Agent Workflow Orchestration with Argo Workflows

Complex Agent Task Pipeline:

# Argo Workflow for multi-step agent tasks
apiVersion: argoproj.io/v1alpha1
kind: WorkflowTemplate
metadata:
  name: agent-task-pipeline
  namespace: agentic-ai
spec:
  entrypoint: agent-workflow
  serviceAccountName: argo-workflow
  templates:
  - name: agent-workflow
    dag:
      tasks:
      - name: data-ingestion
        template: data-ingest
      - name: reasoning-phase
        template: reasoning-agent
        dependencies: [data-ingestion]
        arguments:
          parameters:
          - name: input-data
            value: "{{tasks.data-ingestion.outputs.parameters.processed-data}}"
      - name: tool-execution
        template: tool-agent
        dependencies: [reasoning-phase]
        arguments:
          parameters:
          - name: action-plan
            value: "{{tasks.reasoning-phase.outputs.parameters.action-plan}}"
      - name: result-synthesis
        template: synthesis-agent
        dependencies: [tool-execution]
        arguments:
          parameters:
          - name: execution-results
            value: "{{tasks.tool-execution.outputs.parameters.results}}"

  - name: data-ingest
    container:
      image: agentic-ai/data-processor:v1.0.0
      command: [python, -c]
      args: ["import data_processor; result = data_processor.process(); print(f'::output::{result}')"]
      resources:
        requests:
          cpu: 500m
          memory: 1Gi
        limits:
          cpu: 1000m
          memory: 2Gi
    outputs:
      parameters:
      - name: processed-data
        valueFrom:
          path: /tmp/processed_data.json

  - name: reasoning-agent
    inputs:
      parameters:
      - name: input-data
    container:
      image: agentic-ai/reasoning-agent:v2.0.0
      command: [python, -c]
      args: ["import reasoning; plan = reasoning.create_plan('{{inputs.parameters.input-data}}'); print(f'::output::{plan}')"]
      resources:
        requests:
          nvidia.com/gpu: 1
          cpu: 2000m
          memory: 8Gi
        limits:
          nvidia.com/gpu: 1
          cpu: 4000m
          memory: 16Gi
    outputs:
      parameters:
      - name: action-plan
        valueFrom:
          path: /tmp/action_plan.json

  - name: tool-agent
    inputs:
      parameters:
      - name: action-plan
    container:
      image: agentic-ai/tool-executor:v1.5.0
      command: [python, -c]
      args: ["import tool_executor; results = tool_executor.execute('{{inputs.parameters.action-plan}}'); print(f'::output::{results}')"]
      resources:
        requests:
          cpu: 1000m
          memory: 2Gi
        limits:
          cpu: 2000m
          memory: 4Gi
    outputs:
      parameters:
      - name: results
        valueFrom:
          path: /tmp/execution_results.json

  - name: synthesis-agent
    inputs:
      parameters:
      - name: execution-results
    container:
      image: agentic-ai/synthesizer:v1.0.0
      command: [python, -c]
      args: ["import synthesizer; final = synthesizer.synthesize('{{inputs.parameters.execution-results}}'); print(f'Final Result: {final}')"]
      resources:
        requests:
          cpu: 500m
          memory: 1Gi
        limits:
          cpu: 1000m
          memory: 2Gi

Auto-Scaling and Resource Optimization

Horizontal Pod Autoscaler for AI Agents:

# Custom metrics-based HPA for reasoning agents
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: reasoning-agent-hpa
  namespace: agentic-ai
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: reasoning-agent
  minReplicas: 2
  maxReplicas: 20
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70
  - type: Resource
    resource:
      name: nvidia.com/gpu
      target:
        type: Utilization
        averageUtilization: 80
  - type: Pods
    pods:
      metric:
        name: inference_queue_length
      target:
        type: AverageValue
        averageValue: "30"
  - type: Pods
    pods:
      metric:
        name: response_time_p95
      target:
        type: AverageValue
        averageValue: "2000m"
  behavior:
    scaleUp:
      stabilizationWindowSeconds: 60
      policies:
      - type: Percent
        value: 50
        periodSeconds: 60
      - type: Pods
        value: 2
        periodSeconds: 60
      selectPolicy: Max
    scaleDown:
      stabilizationWindowSeconds: 300
      policies:
      - type: Percent
        value: 10
        periodSeconds: 60
      selectPolicy: Min

Vertical Pod Autoscaler for Memory-Intensive Workloads:

# VPA for vector database optimization
apiVersion: autoscaling.k8s.io/v1
kind: VerticalPodAutoscaler
metadata:
  name: qdrant-vpa
  namespace: agentic-ai
spec:
  targetRef:
    apiVersion: apps/v1
    kind: StatefulSet
    name: qdrant-cluster
  updatePolicy:
    updateMode: "Auto"
  resourcePolicy:
    containerPolicies:
    - containerName: qdrant
      minAllowed:
        cpu: 1000m
        memory: 4Gi
      maxAllowed:
        cpu: 8000m
        memory: 32Gi
      controlledResources: ["cpu", "memory"]
      controlledValues: RequestsAndLimits

Advanced Security and RBAC for AI Workloads

Comprehensive RBAC Configuration:

# Service account for agent operations
apiVersion: v1
kind: ServiceAccount
metadata:
  name: agent-operator
  namespace: agentic-ai
  annotations:
    eks.amazonaws.com/role-arn: arn:aws:iam::ACCOUNT:role/agent-operator-role
---
# Role for agent management
apiVersion: rbac.authorization.k8s.io/v1
kind: Role
metadata:
  namespace: agentic-ai
  name: agent-manager
rules:
- apiGroups: [""]
  resources: ["pods", "services", "configmaps", "secrets"]
  verbs: ["get", "list", "watch", "create", "update", "patch", "delete"]
- apiGroups: ["apps"]
  resources: ["deployments", "statefulsets", "daemonsets"]
  verbs: ["get", "list", "watch", "create", "update", "patch", "delete"]
- apiGroups: ["autoscaling"]
  resources: ["horizontalpodautoscalers"]
  verbs: ["get", "list", "watch", "create", "update", "patch", "delete"]
- apiGroups: ["metrics.k8s.io"]
  resources: ["pods", "nodes"]
  verbs: ["get", "list"]
---
# Role binding
apiVersion: rbac.authorization.k8s.io/v1
kind: RoleBinding
metadata:
  name: agent-manager-binding
  namespace: agentic-ai
subjects:
- kind: ServiceAccount
  name: agent-operator
  namespace: agentic-ai
roleRef:
  kind: Role
  name: agent-manager
  apiGroup: rbac.authorization.k8s.io

Pod Security Standards and Network Policies:

# Network policy for agent isolation
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: agent-network-policy
  namespace: agentic-ai
spec:
  podSelector:
    matchLabels:
      tier: agent
  policyTypes:
  - Ingress
  - Egress
  ingress:
  - from:
    - namespaceSelector:
        matchLabels:
          name: agentic-ai
    - podSelector:
        matchLabels:
          role: coordinator
    ports:
    - protocol: TCP
      port: 8080
    - protocol: TCP
      port: 9090
  egress:
  - to:
    - namespaceSelector:
        matchLabels:
          name: agentic-ai
    ports:
    - protocol: TCP
      port: 6379  # Redis
    - protocol: TCP
      port: 6333  # Qdrant
    - protocol: TCP
      port: 9092  # Kafka
  - to: []  # Allow external API calls
    ports:
    - protocol: TCP
      port: 443
    - protocol: TCP
      port: 80

Monitoring and Observability for Agent Systems

Prometheus Monitoring Configuration:

# ServiceMonitor for agent metrics
apiVersion: monitoring.coreos.com/v1
kind: ServiceMonitor
metadata:
  name: agent-metrics
  namespace: agentic-ai
  labels:
    app: agentic-ai
spec:
  selector:
    matchLabels:
      monitoring: enabled
  endpoints:
  - port: metrics
    interval: 30s
    path: /metrics
    honorLabels: true
  - port: health
    interval: 60s
    path: /health/metrics
---
# PrometheusRule for agent alerts
apiVersion: monitoring.coreos.com/v1
kind: PrometheusRule
metadata:
  name: agent-alerts
  namespace: agentic-ai
spec:
  groups:
  - name: agent.rules
    interval: 30s
    rules:
    - alert: AgentHighResponseTime
      expr: histogram_quantile(0.95, rate(agent_request_duration_seconds_bucket[5m])) > 2
      for: 2m
      labels:
        severity: warning
      annotations:
        summary: "Agent response time is high"
        description: "Agent {{ $labels.agent_id }} has 95th percentile response time of {{ $value }}s"

    - alert: AgentGPUUtilizationHigh
      expr: nvidia_gpu_utilization > 90
      for: 5m
      labels:
        severity: critical
      annotations:
        summary: "GPU utilization is critically high"
        description: "GPU utilization on {{ $labels.instance }} is {{ $value }}%"

    - alert: VectorDatabaseConnectionFailed
      expr: up{job="qdrant"} == 0
      for: 1m
      labels:
        severity: critical
      annotations:
        summary: "Vector database is down"
        description: "Qdrant instance {{ $labels.instance }} is unreachable"

Custom Metrics for Agent Performance:

# ConfigMap for agent metrics configuration
apiVersion: v1
kind: ConfigMap
metadata:
  name: agent-metrics-config
  namespace: agentic-ai
data:
  metrics.yaml: |
    metrics:
      - name: agent_task_completion_time
        type: histogram
        description: Time taken to complete agent tasks
        buckets: [0.1, 0.5, 1.0, 2.0, 5.0, 10.0, 30.0, 60.0]

      - name: agent_memory_usage_bytes
        type: gauge
        description: Current memory usage of agent processes

      - name: agent_inference_requests_total
        type: counter
        description: Total number of inference requests processed
        labels: [agent_type, model_version]

      - name: agent_tool_execution_duration
        type: histogram
        description: Time taken for tool execution
        buckets: [0.01, 0.1, 0.5, 1.0, 5.0, 10.0]

      - name: agent_queue_depth
        type: gauge
        description: Current depth of agent task queue

      - name: vector_search_latency
        type: histogram
        description: Vector similarity search latency
        buckets: [0.001, 0.01, 0.1, 0.5, 1.0]

Advanced Agent Deployment Strategies

Canary Deployment for Agent Updates:

# Flagger canary analysis for agent deployments
apiVersion: flagger.app/v1beta1
kind: Canary
metadata:
  name: reasoning-agent-canary
  namespace: agentic-ai
spec:
  targetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: reasoning-agent
  progressDeadlineSeconds: 60
  service:
    port: 8000
    targetPort: 8000
    gateways:
    - reasoning-agent-gateway
    hosts:
    - reasoning-agent.local
  analysis:
    interval: 1m
    threshold: 5
    maxWeight: 50
    stepWeight: 10
    metrics:
    - name: request-success-rate
      thresholdRange:
        min: 99
      interval: 1m
    - name: request-duration
      thresholdRange:
        max: 500
      interval: 1m
    - name: gpu-utilization
      thresholdRange:
        max: 85
      interval: 1m
    webhooks:
    - name: load-test
      url: http://load-tester.agentic-ai/
      timeout: 5s
      metadata:
        cmd: "hey -z 1m -q 10 -c 2 http://reasoning-agent-canary.agentic-ai:8000/health"

Blue-Green Deployment with Agent State Migration:

# Agent state migration job
apiVersion: batch/v1
kind: Job
metadata:
  name: agent-state-migration
  namespace: agentic-ai
spec:
  template:
    spec:
      serviceAccountName: agent-operator
      containers:
      - name: state-migrator
        image: agentic-ai/state-migrator:v1.0.0
        command: ["/bin/sh", "-c"]
        args:
        - |
          echo "Starting agent state migration..."

          # Export current agent states
          kubectl exec -n agentic-ai sts/redis-cluster-0 -- redis-cli --cluster call 127.0.0.1:6379 BGSAVE

          # Wait for backup completion
          while [ "$(kubectl exec -n agentic-ai sts/redis-cluster-0 -- redis-cli LASTSAVE)" == "$(kubectl exec -n agentic-ai sts/redis-cluster-0 -- redis-cli LASTSAVE)" ]; do
            sleep 5
          done

          # Create blue environment
          kubectl apply -f /manifests/blue-deployment.yaml

          # Wait for blue environment readiness
          kubectl rollout status deployment/reasoning-agent-blue -n agentic-ai

          # Migrate vector embeddings
          python /scripts/migrate_vectors.py --source qdrant-green --target qdrant-blue

          # Switch traffic to blue
          kubectl patch service reasoning-agent-service -p '{"spec":{"selector":{"version":"blue"}}}'

          echo "Migration completed successfully"
        volumeMounts:
        - name: migration-scripts
          mountPath: /scripts
        - name: manifests
          mountPath: /manifests
        env:
        - name: KUBECONFIG
          value: /var/run/secrets/kubernetes.io/serviceaccount
      volumes:
      - name: migration-scripts
        configMap:
          name: migration-scripts
      - name: manifests
        configMap:
          name: blue-green-manifests
      restartPolicy: OnFailure
  backoffLimit: 3

Performance Optimization and Resource Management

GPU Memory Optimization:

# Multi-instance GPU configuration
apiVersion: v1
kind: ConfigMap
metadata:
  name: mig-config
  namespace: agentic-ai
data:
  mig.conf: |
    version: v1
    mig-configs:
      all-1g.5gb:
        - devices: all
          mig-enabled: true
          mig-devices:
            1g.5gb: 7
      all-2g.10gb:
        - devices: all
          mig-enabled: true
          mig-devices:
            2g.10gb: 3
      all-3g.20gb:
        - devices: all
          mig-enabled: true
          mig-devices:
            3g.20gb: 2
---
# MIG-enabled agent deployment
apiVersion: apps/v1
kind: Deployment
metadata:
  name: mig-reasoning-agent
  namespace: agentic-ai
spec:
  replicas: 7
  selector:
    matchLabels:
      app: mig-reasoning-agent
  template:
    metadata:
      labels:
        app: mig-reasoning-agent
    spec:
      nodeSelector:
        nvidia.com/mig.config: all-1g.5gb
      containers:
      - name: reasoning-engine
        image: agentic-ai/reasoning-engine:v1.5.0-mig
        resources:
          requests:
            nvidia.com/mig-1g.5gb: 1
            cpu: 2000m
            memory: 8Gi
          limits:
            nvidia.com/mig-1g.5gb: 1
            cpu: 4000m
            memory: 16Gi
        env:
        - name: CUDA_MPS_PERCENTAGE
          value: "100"
        - name: MODEL_PARALLEL_SIZE
          value: "1"

Intelligent Agent Scheduling:

# Priority class for critical agents
apiVersion: scheduling.k8s.io/v1
kind: PriorityClass
metadata:
  name: critical-agent
value: 1000
globalDefault: false
description: "Priority class for critical AI agents"
---
# Scheduler configuration for agent placement
apiVersion: v1
kind: ConfigMap
metadata:
  name: scheduler-config
  namespace: kube-system
data:
  config.yaml: |
    apiVersion: kubescheduler.config.k8s.io/v1beta3
    kind: KubeSchedulerConfiguration
    profiles:
    - schedulerName: agent-scheduler
      plugins:
        filter:
          enabled:
          - name: NodeResourcesFit
          - name: NodeAffinity
        score:
          enabled:
          - name: NodeResourcesFit
            weight: 5
          - name: NodeAffinity
            weight: 3
          - name: InterPodAffinity
            weight: 2
      pluginConfig:
      - name: NodeResourcesFit
        args:
          scoringStrategy:
            type: LeastAllocated
            requestedToCapacityRatio:
              shape:
              - utilization: 0
                score: 10
              - utilization: 100
                score: 0
              resources:
              - name: nvidia.com/gpu
                weight: 100
              - name: cpu
                weight: 1
              - name: memory
                weight: 1

Production Best Practices and Troubleshooting

Health Check and Circuit Breaker Implementation:

# Agent health monitoring deployment
apiVersion: apps/v1
kind: Deployment
metadata:
  name: agent-health-monitor
  namespace: agentic-ai
spec:
  replicas: 1
  selector:
    matchLabels:
      app: agent-health-monitor
  template:
    metadata:
      labels:
        app: agent-health-monitor
    spec:
      containers:
      - name: health-monitor
        image: agentic-ai/health-monitor:v1.0.0
        ports:
        - containerPort: 8080
        env:
        - name: AGENT_ENDPOINTS
          value: "reasoning-agent:8000,tool-agent:8001,synthesis-agent:8002"
        - name: CHECK_INTERVAL
          value: "30s"
        - name: CIRCUIT_BREAKER_THRESHOLD
          value: "5"
        - name: CIRCUIT_BREAKER_TIMEOUT
          value: "60s"
        resources:
          requests:
            cpu: 100m
            memory: 128Mi
          limits:
            cpu: 200m
            memory: 256Mi
        livenessProbe:
          httpGet:
            path: /health
            port: 8080
          initialDelaySeconds: 10
          periodSeconds: 30
        readinessProbe:
          httpGet:
            path: /ready
            port: 8080
          initialDelaySeconds: 5
          periodSeconds: 10

Comprehensive Logging and Audit Trail:

# Fluent Bit configuration for agent logs
apiVersion: v1
kind: ConfigMap
metadata:
  name: fluent-bit-config
  namespace: agentic-ai
data:
  fluent-bit.conf: |
    [SERVICE]
        Flush         1
        Log_Level     info
        Daemon        off
        Parsers_File  parsers.conf
        HTTP_Server   On
        HTTP_Listen   0.0.0.0
        HTTP_Port     2020

    [INPUT]
        Name              tail
        Path              /var/log/containers/*agentic-ai*.log
        Parser            cri
        Tag               agent.*
        Refresh_Interval  5
        Mem_Buf_Limit     50MB
        Skip_Long_Lines   On

    [FILTER]
        Name                kubernetes
        Match               agent.*
        Kube_URL            https://kubernetes.default.svc:443
        Kube_CA_File        /var/run/secrets/kubernetes.io/serviceaccount/ca.crt
        Kube_Token_File     /var/run/secrets/kubernetes.io/serviceaccount/token
        Kube_Tag_Prefix     agent.var.log.containers.
        Merge_Log           On
        Keep_Log            Off
        K8S-Logging.Parser  On
        K8S-Logging.Exclude On
        Annotations         Off
        Labels              On

    [FILTER]
        Name    grep
        Match   agent.*
        Regex   log (ERROR|WARN|INFO|DEBUG)

    [OUTPUT]
        Name  es
        Match agent.*
        Host  elasticsearch.logging.svc.cluster.local
        Port  9200
        Index agent-logs
        Type  _doc
        Logstash_Format On
        Logstash_Prefix agent
        Time_Key @timestamp

Future-Proofing and Ecosystem Integration

GitOps Integration with ArgoCD:

# ArgoCD application for agent deployments
apiVersion: argoproj.io/v1alpha1
kind: Application
metadata:
  name: agentic-ai-platform
  namespace: argocd
spec:
  project: default
  source:
    repoURL: https://github.com/company/agentic-ai-k8s
    targetRevision: HEAD
    path: manifests/
    helm:
      valueFiles:
      - values.yaml
      - values-production.yaml
  destination:
    server: https://kubernetes.default.svc
    namespace: agentic-ai
  syncPolicy:
    automated:
      prune: true
      selfHeal: true
      allowEmpty: false
    syncOptions:
    - CreateNamespace=true
    - PruneLast=true
    retry:
      limit: 5
      backoff:
        duration: 5s
        factor: 2
        maxDuration: 3m
  revisionHistoryLimit: 10

The deployment and orchestration of Agentic AI systems on Kubernetes represents a significant advancement in autonomous artificial intelligence infrastructure. By leveraging Kubernetes’ powerful orchestration capabilities, organizations can build resilient, scalable, and intelligent systems that adapt to changing workloads while maintaining high availability and performance.

The key to successful Agentic AI deployment lies in understanding the unique requirements of autonomous agents, implementing robust monitoring and observability, and designing for horizontal scalability. As these systems continue to evolve, Kubernetes provides the foundation for managing increasingly sophisticated AI workloads that will define the next generation of intelligent applications.

Through careful implementation of the patterns and practices outlined in this guide, teams can build production-ready Agentic AI systems that harness the full potential of autonomous artificial intelligence while maintaining operational excellence and security standards required for enterprise environments.