Deploying Agentic AI with Docker

Most AI books teach concepts.
Very few teach you how to actually build production AI systems from the ground up.
You can connect a model to an application.
You can even make an AI demo work locally.
But building a real AI service is different.
Real systems crash. Containers fail. Agents loop endlessly. Memory grows uncontrollably. Infrastructure becomes unstable under load. Suddenly, the challenge is no longer the model itself, it is the architecture surrounding it.
This book was written for that reality.
Deploying Agentic AI with Docker is a deeply practical, project-focused guide to building and scaling modern AI systems using Docker and Kubernetes. Instead of isolated snippets and disconnected examples, you will work through complete real-world projects designed to reflect how intelligent services are actually engineered and deployed in production environments.
Throughout this book, you will build:

• Stateful AI agents
• Containerized inference services
• Multi-service AI architectures
• Persistent memory systems
• Tool-using autonomous workflows
• Scalable API layers
• Distributed orchestration pipelines
• Production-ready AI deployments with Docker and Kubernetes

• Why most AI deployments become unstable at scale
• How Docker transforms AI deployment workflows
• How Kubernetes manages orchestration and resilience
• How autonomous agents handle memory, tools, and execution loops
• How production AI systems deal with failures, queues, scaling, and observability
• How real engineering decisions impact long-term system reliability