# JIT Chapter 79: Docker for AI Services (/docs/deploying-agent-factories-in-the-cloud/79-docker-for-ai-services/overview)

---
type: Document
title: "JIT Chapter 79: Docker for AI Services"
description: "Build your Docker skill first, then learn to understand and refine it into a production-ready asset"
status: stable
order: 7900
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  by: ag-archive/0.1.0
  at: 2026-08-26T00:00:00Z
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    by: ag-archive/0.1.0
    at: 2026-08-26T00:00:00Z
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---

You build the `docker-deployment` skill first, then use each lesson to test and refine it. By the end you own a production-ready Docker capability for your agent services.

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## Goals

- Understand container fundamentals (images, layers, runtime)
- Write production Dockerfiles with multi-stage builds and optimization
- Debug containers (logs, exec, inspect, port conflicts, restart policies)
- Harden images (env vars, health checks, non-root users)
- Apply spec-driven workflows and turn the patterns into a reusable skill

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## Lesson Progression

| # | Lesson | Focus |
|---|--------|-------|
| **0** | Build Your Docker Skill | Scaffold from official docs |
| 1 | Installation & Setup | Validate prerequisites |
| 2 | Container Fundamentals | Images vs. containers |
| 3 | First Dockerfile | Build and run images |
| 4 | Lifecycle & Debugging | Exec, logs, inspect, restarts |
| 5 | Multi-Stage Builds | Size and cache optimization |
| 6 | Production Hardening | Health checks, users, envs |
| 7 | Docker Image Builder Skill | Encode patterns and prompts |
| **8** | Capstone: Containerize Your API | Production-ready image |

Each lesson ends with a skill reflection: test, find gaps, and improve the skill.

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## Outcome & Method

You finish with a hardened image for the Chapter 70 Task API (in-memory and SQLModel variants) pushed to a registry, plus a reusable Docker skill. The chapter uses the 4-Layer method: foundations → optimization → skill design → spec-driven capstone.

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## Prerequisites

- Chapter 70 Task API ready to containerize
- Part 6 fundamentals (FastAPI, Python)
- Terminal comfort; Docker experience not required (Lesson 1 installs)