One AI agent is useful. Six agents working together is something else.
I've spent the last three months building a system where multiple Claude agents coordinate on complex tasks. One does research. One writes. One reviews. One publishes. They pass work between themselves.
This isn't science fiction. It runs on my laptop. Here's how.
Why Multiple Agents?
Single agent problem: Context limits.
Claude has a context window. When you dump a 10,000-line codebase plus instructions plus conversation history, things get fuzzy. The agent forgets what you told it 20 minutes ago.
Multiple agents solve this differently:
Agent 1 (Researcher): Deep expertise in finding information. Small, focused context.
Agent 2 (Writer): Receives summary from Researcher. Writes content. Doesn't need to hold research context.
Agent 3 (Editor): Receives draft from Writer. Edits without the original research context cluttering things.
Each agent is sharp at one thing. They hand off summaries, not full context.
My 6-Agent Setup
Agent 1: Researcher
Job: Find information on any topic
Context: Web search tools, documentation, memory of past research
Output: 500-word brief with sources
Agent 2: Content Writer
Job: Write articles, emails, posts
Context: My writing style examples, brand guidelines
Input: Brief from Researcher
Output: Draft content
Agent 3: Editor
Job: Fix grammar, improve flow, remove AI-isms
Context: Humanizer guidelines, style rules
Input: Draft from Writer
Output: Polished content
Agent 4: Publisher
Job: Post content to platforms
Context: API keys, platform formatting rules
Input: Final content from Editor
Output: Live posts with links
Agent 5: Monitor
Job: Watch for events that need response
Context: Email inbox, social mentions, error logs
Output: Alerts and task assignments to other agents
Agent 6: Coordinator
Job: Route tasks between agents
Context: Current queue, agent capabilities
Output: Task assignments
How They Talk
Simple message passing. Agent finishes work → writes to shared file → next agent picks up.
# coordinator.py (simplified)
def route_task(task):
if task.type == "research":
return researcher.execute(task)
elif task.type == "write":
brief = task.dependencies["research"]
return writer.execute(task, context=brief)
elif task.type == "edit":
draft = task.dependencies["write"]
return editor.execute(task, context=draft)
# etc.
def main():
while True:
task = queue.get_next()
if task:
result = route_task(task)
queue.mark_complete(task, result)
sleep(10)
Each agent has its own CLAUDE.md file with specific instructions. The Coordinator knows which agent handles what.
Real Example: Blog Post Pipeline
Input: "Write a blog post about Telegram bots for restaurants"
Researcher (5 min):
- Searches for restaurant Telegram bot examples
- Finds features that work well
- Compiles brief with 3 case studies
Writer (3 min):
- Receives brief
- Writes 1,200-word article
- Includes practical examples
Editor (2 min):
- Removes AI-sounding phrases
- Fixes flow issues
- Adds personality
Publisher (1 min):
- Formats for Medium
- Posts to draft
- Notifies me for final review
Total: 11 minutes. Article ready for my review. I spend 5 minutes reading, make small tweaks, hit publish.
The Hard Parts
1. Context Handoff
Agents can't share everything. The Writer doesn't need raw search results — just the summary.
I spent weeks tuning what each agent outputs. Too little context and the next agent is lost. Too much and you're back to the single-agent problem.
2. Error Handling
What happens when the Writer produces garbage? The Editor flags it. Coordinator routes it back to Writer with feedback.
Loops happen. I cap retries at 3, then flag for human review.
3. Maintaining Consistency
Six agents can develop inconsistent styles. The Coordinator maintains shared context about brand voice, current projects, recent decisions.
This shared context is small — maybe 500 tokens — and every agent sees it.
Cost
Running 6 agents isn't cheap if you're using the API directly.
My setup:
- Claude Pro subscription for the main agents: $20/month
- API calls for automated agents: ~$50/month (varies with volume)
For a content operation producing 30+ articles/month, the math works. Human writer at that volume would cost $1,500+.
Where This Is Going
Right now, I review everything before publishing. The agents are good but not infallible.
Next step: Agent that learns from my edits. When I change something, it updates the Editor's rules.
Eventually: Truly autonomous content operation. Human reviews weekly instead of daily.
We're not there yet. But we're closer than most people realize.
Get Started
You don't need six agents. Start with two:
- Research agent (finds info)
- Writer agent (uses info to create)
Add more as you find bottlenecks.
The coordination isn't hard. Simple file-based messaging works. Fancy frameworks optional.
I cover the full architecture — every CLAUDE.md file, the coordinator logic, error handling, and scaling tips — in AI Automation Blueprint 2026. $29 for the complete system.
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