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/
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🌐 Subscribe to GenAI Unplugged Substack → https://genaiunplugged.substack.com/
🤝 Let's connect on LinkedIn → / dheerajsharma14
📚 Browse all my courses → https://genaiunplugged.com/
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