Build a 3-Agent AI Content Team in Claude Code (Nobody Shows You This) | Claude Code Full Course

Build a 3-agent AI content team using Claude Code subagents that research, write, and review articles automatically. This is Lesson 6 of the Claude Code full course series.

In this lesson you will build a complete content pipeline where three specialized claude agents work in sequence. The Researcher gathers context, the Writer produces a draft, and the Reviewer runs a 25-point quality checklist before surfacing feedback. You will also see parallel agent execution in action in Claude Code, where multiple research agents run simultaneously to save time and credits.

🎯 What You Will Learn

- How Claude subagents prevent context window exhaustion by isolating work into dedicated jobs
- The full anatomy of a Claude subagent: name, model, tools, memory, and permissions
- How to structure a 3-agent AI content team with a Researcher, Writer, and Reviewer
- When to run AI agents sequentially vs. in parallel for maximum efficiency
- How the Claude AI agent memory layer helps each agent get sharper over time
- How to wire brand voice guidelines and content strategy context into your agents
- Live demo: all 3 agents run end-to-end and produce a reviewed draft with a real quality score

📋 Prerequisites

- Completion of Claude Code Lessons 1 through 5 (or basic Claude Code familiarity)
- Visual Studio Code installed
- An active Claude Code subscription

🛠️ Tools and Concepts Covered

- Claude Code subagents and the .claude/agents folder structure
- Claude Agent anatomy: name, model, tools list, memory file, and permission scopes
- MCP servers as the external intelligence layer for agents
- Brand voice guidelines injected as agent context
- Content calendar cross-referencing inside the Researcher agent
- Model selection per agent role: Claude Opus for orchestration, Claude Sonnet for specialized tasks
- Sequential vs. parallel agent orchestration patterns

💰 Real Cost and Speed Breakdown

The full Researcher plus Writer plus Reviewer pipeline on Claude Sonnet runs approximately 12 minutes per article. You can batch 3 articles in roughly the same window depending on task complexity. Model selection details for Sonnet vs. Opus are shown live in the demo.

📊 Live Demo Highlights

- Full VS Code walkthrough of the .claude folder and agent files
- Researcher agent pulling content strategy and content calendar context
- Writer agent producing a complete article draft
- Reviewer agent scoring the draft 74 out of 100 with specific revision notes
- Post-run memory update so agents improve accuracy on every subsequent run

📺 CHAPTERS:
0:00 The Context Window Problem
1:06 Lesson 6 Overview
2:10 Why Claude Subagents Exist
5:18 Anatomy of a Claude Subagent
10:24 Claude AI Agent Memory Layer
15:33 Setting Up in VS Code
27:55 Inside the Claude Agent Files
34:11 Sequential vs Parallel AI Agents
40:25 Live Demo: Full Content Automation Pipeline Run
43:29 Results, Costs & Wrap-Up

📥 Helpful Links:
🛠️ Check my AI Systems store → https://store.genaiunplugged.com/
▶️ Watch the Full n8n Zero to Hero Course Playlist → https://tinyurl.com/3rw3x6hy
🌐 Subscribe to GenAI Unplugged Substack → https://genaiunplugged.substack.com/
🤝 Let's connect on LinkedIn → https://www.linkedin.com/in/dheerajsharma14/
📚 Browse all my courses → https://genaiunplugged.com/

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Видео Build a 3-Agent AI Content Team in Claude Code (Nobody Shows You This) | Claude Code Full Course канала GenAI Unplugged