# JIT Chapter 84: CI/CD Pipelines & GitOps with ArgoCD (/docs/deploying-agent-factories-in-the-cloud/84-cicd-gitops-argocd/overview)

---
type: Document
title: "JIT Chapter 84: CI/CD Pipelines & GitOps with ArgoCD"
description: "Build your GitOps skill first, then learn to understand and refine it into a production-ready asset"
status: stable
order: 8400
generated:
  by: ag-archive/0.1.0
  at: 2026-08-26T00:00:00Z
ksor:
  audience: [public]
  approval:
    by: ag-archive/0.1.0
    at: 2026-08-26T00:00:00Z
trust_tier: unverified
build_id: sha256:c8912c3008ae345a7fdc33ace656531ad74f37c065177057c390c7d759f1e970
source_commit: dad4758bb59919c7b9130444778ade062a2dce3e
ksor_version: 0.0.47
---

You build the `gitops-deployment` skill first, then refine it across CI/CD foundations and ArgoCD GitOps patterns until you have a production pipeline for your agent services.

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## Goals

- Design CI/CD pipelines in GitHub Actions (build, test, quality gates)
- Apply GitOps principles with ArgoCD as the declarative source of truth
- Configure ArgoCD apps, sync strategies/waves/hooks
- Scale with ApplicationSets; secure with Projects/RBAC and secrets management
- Run progressive delivery (canary/blue-green) with health/notification hooks
- Operate multi-cluster GitOps and capture patterns in a reusable skill

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## Lesson Progression

| # | Lesson | Focus |
|---|--------|-------|
| **0** | Build Your GitOps Skill | Scaffold from docs |
| 1-4 | CI/CD Fundamentals | Concepts, Actions workflows, Docker builds, tests |
| 5-9 | GitOps & ArgoCD Core | Principles, architecture, first app, sync strategies/waves |
| 10-15 | Production Patterns | ApplicationSets, RBAC/Projects, health/notifications, progressive delivery, secrets, multi-cluster |
| 16 | AI-Assisted GitOps | Use AI to author/review pipelines |
| **17** | Capstone: End-to-End Pipeline | Commit-to-cluster production pipeline |
| 18 | Finalize GitOps Skill | Package prompts and patterns |

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

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## Outcome & Method

You finish with an automated pipeline that builds, tests, and deploys your Kubernetes agent via ArgoCD, plus a GitOps skill for future projects. The flow moves from foundations to ArgoCD production patterns, then AI-assisted authoring, ending in a spec-driven capstone.

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## Prerequisites

- Chapters 79-81 (Docker, Kubernetes, Helm)
- Context from Chapters 82-83 (Kafka, Dapr) for services you deploy