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Module 11 · Page 12 of 20 · 9 sections

Deployment and MLOps for Agentic AI

Taking agents to production: the MLOps lifecycle, containers, Kubernetes, monitoring and good practice.

11.1

What is Deployment for Agentic AI?

  • Deployment means making the agent available in real-world environments for end users.
  • It includes hosting the model, tools, APIs, agent logic and required infrastructure.
  • It should be scalable, secure and reliable.
  • Deployment can be on cloud, on-premise or hybrid based on use case.
11.2

Why MLOps is Important?

  • Helps in managing the complete lifecycle of agentic AI systems.
  • Ensures continuous development, testing, deployment and monitoring.
  • Useful for handling model updates, tool changes and new agent capabilities.
  • Improves reliability, performance and cost efficiency.
11.3

MLOps Lifecycle for Agentic AI

01
Data ManagementCollect, clean and store data
e.g. user queries, documents, logs
02
DevelopmentBuild and test agent logic
LLM, tools, prompts, orchestration
03
EvaluationTest for quality, safety, accuracy and reliability
automatic + human evaluation
04
DeploymentDeploy agent in production
Docker, Kubernetes, cloud services
05
MonitoringTrack performance and usage
logs, metrics, feedback
06
Continuous ImprovementUpdate model, tools and prompts
retrain, re-evaluate, redeploy
11.4

Deployment Architecture (High Level)

Web
Mobile
API GatewayNginx / cloud
Agent Service (Docker container)
Agent Orchestrator
LLMOpenAI / Llama / etc.
Tools / APIs
MemoryVector DB
DatabasePostgreSQL / MongoDB
Vector StorePinecone / Qdrant
External APIsWeather, search, custom APIs
Prometheusmetrics
Grafanavisualization
ELK / CloudWatchlogs
11.5

Containerization using Docker

  • Docker helps package the agent, its dependencies and tools into a single container.
  • Ensures consistency across development, testing and production environments.
  • Makes deployment easy and reproducible.
dockerfile
FROM python:3.10-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .
CMD ["python", "app.py"]
11.6

Kubernetes Deployment

  • Kubernetes helps in running and scaling agent services in production.
  • Automatically manages containers, scaling, load balancing and self-healing.
  • Useful for handling multiple agent instances and high traffic.
Load Balancer
Kubernetes Cluster
Agent Pod 1
Agent Pod 2
Agent Pod 3
Auto ScalingHPA
Simplified Kubernetes architecture.
11.7

Monitoring and Logging

  • Monitoring helps track agent performance, latency, errors and user satisfaction.
  • Logging is useful for debugging and audit.
  • Common tools: Prometheus, Grafana, ELK, CloudWatch.

What to monitor

  1. 1API latency and response time.
  2. 2Tool call success / failure rate.
  3. 3Model token usage and cost.
  4. 4Memory usage and database performance.
  5. 5User feedback and error logs.
11.8

Best Practices

  1. 1Use Docker and Kubernetes for scalable deployment.
  2. 2Implement monitoring and alerting.
  3. 3Use environment variables for secrets and API keys.
  4. 4Keep logs and traces for debugging.
  5. 5Implement automatic and human evaluation.
  6. 6Continuously update models, tools and prompts.
11.9

Key Takeaways

  • Deployment and MLOps are essential for real-world agentic AI.
  • Use containers, orchestration and monitoring tools.
  • Follow best practices for security, scalability and reliability.
  • A proper MLOps pipeline helps in continuous improvement and better user experience.