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Module 17 · Page 18 of 20 · 9 sections

Deployment and Scalability of Agentic AI

Docker, Kubernetes, scaling strategies, observability and CI/CD for agent applications.

11Deployment and MLOps for Agentic AI12Security and Safety in Agentic AI09Building Real-World Projects with Agentic AI
17.1

What is Deployment in Agentic AI?

  • Deployment means making the agent application available in a real-world environment so that users can interact with it.
  • It includes packaging, testing, monitoring, scaling and maintaining the system.
  • A properly deployed agent should be reliable, scalable and secure.
Related11Deployment and MLOps for Agentic AI
17.2

Deployment Architecture (High Level)

UserWeb / mobile
Load BalancerNginx / cloud LB
API GatewayREST / GraphQL
Agent ServiceLLM + tools
Tool ServicesAPIs, DB, search
Memory ServiceVector DB
Monitoring & LoggingMetrics, alerts, traces
17.3

Containerization using Docker

  • Docker helps to package the application with all dependencies.
  • It provides a consistent environment across development, testing and production.
  • Useful for deploying agent services, tools and databases.
DockerfileInstructions
Docker ImageApplication + dependencies
Docker ContainerRunning app
Docker architecture (simplified).
shell
docker build -t agent-app .
docker run -p 8000:8000 agent-app

# common commands
docker build    # build an image
docker run      # start a container
docker ps       # list running containers
docker logs     # read container logs
docker stop     # stop a container
17.4

Orchestration with Kubernetes

  • Kubernetes helps in deploying, scaling and managing containerized applications.
  • It automatically handles load balancing, self-healing and scaling.
  • Useful for production-grade agentic AI systems.
Clientkubectl
Control plane
API Server
etcdCluster data
Scheduler
Worker NodesContainers
Kubernetes architecture (simplified).
17.5

Scaling Strategies

1
Horizontal Scaling
Add more instances of agent services to handle high traffic.
2
Vertical Scaling
Increase CPU / memory of existing instances.
3
Auto Scaling
Automatically add or remove instances based on load.
4
Load Balancing
Distribute traffic across multiple instances.
17.6

Monitoring and Observability

  • Monitoring helps to track the health and performance of the agent system.
  • It is important to monitor response time, error rate, resource usage and tool performance.
  • Common tools: Prometheus, Grafana, ELK Stack, OpenTelemetry.
ApplicationAgent services
MetricsPrometheus
LogsELK / Loki
TracingOpenTelemetry
VisualizationGrafana
Observability stack.
17.7

Security Considerations

  • Use authentication and authorization for user access.
  • Validate and sanitize user input.
  • Secure API keys and secrets using environment variables or secret managers.
  • Implement rate limiting to prevent misuse.
  • Monitor for unusual or harmful agent behaviour.
Related12Security and Safety in Agentic AI
17.8

CI/CD Pipeline for Agentic AI

  • CI/CD helps in automating build, test and deployment.
  • It ensures faster and more reliable delivery.
  • Useful tools: GitHub Actions, GitLab CI, Jenkins.
CodeRepo
BuildDocker image
TestUnit + integration
DeployKubernetes
CI/CD pipeline.
17.9

Key Takeaways

  1. 1Use Docker for packaging agent applications.
  2. 2Use Kubernetes for scalable deployment.
  3. 3Implement monitoring and logging for reliability.
  4. 4Follow security best practices.
  5. 5Use CI/CD for faster and automated delivery.