Case Study: Inside My Multi-Agent Content Pipeline

I used to write everything myself. It was slow, draining, and inconsistent. I wanted a system that could generate high-quality content at scale without losing my voice.

So I built a multi-agent content pipeline using OpenClaw. This very article is part of that pipeline. What follows is a documentary-style look at how it works, what I learned, and how you can build your own.

The Context

I'm running a 10K MRR experiment and building AI products in public. Content is part of the flywheel-documenting builds, shipping learnings, and attracting early users.

The bottleneck wasn't ideas. It was time and consistency. I needed output without burnout.

The Pipeline in One Sentence

Brief once, spawn five writer agents, assign roles, monitor output, and ship the best drafts to production.

It's a workflow, not a magic trick. The orchestration patterns behind this are explained in Multi-Agent Orchestration Patterns.

Step 1: The Brief (30 Minutes, No More)

Everything starts with a clear brief. The brief is the highest leverage part of the system.

My brief includes:

• Goal (what this content is for)
• Audience (builders, indie founders, or potential clients)
• Format (case study, guide, narrative)
• Length range
• Style constraints (first-person, documentary tone)
• Non-negotiables (real numbers, practical takeaways)

If the brief is weak, the output is weak. It's that simple.

Step 2: Spawn Five Writer Agents

I treat content creation like a newsroom, using OpenAI's models alongside Claude. Each writer has a role.

Typical roles:

1. Lead writer - owns the main narrative
2. Researcher - pulls facts, numbers, examples
3. Editor - trims fluff, improves flow
4. Angle tester - challenges the thesis and suggests alternative framing
5. Meta reviewer - checks tone consistency and alignment with my voice

All five write in parallel, powered by Claude from Anthropic. That's the speed advantage. I cover the lessons from running this many agents in Running 14+ AI Agents Daily.

Step 3: Parallel Drafting

Each agent writes a separate draft or section. I avoid overlap by assigning specific segments:

• Agent A: Introduction + context
• Agent B: Method / process
• Agent C: Results + metrics
• Agent D: Lessons learned
• Agent E: Practical steps

By the time I wake up (or return to the desk), I have a stack of drafts to curate instead of a blank page.

Step 4: Monitoring & Quality Control

Quality doesn't happen automatically. I built a review layer.

The QC checklist

• Is the voice first-person and documentary?
• Are numbers consistent with reality?
• Are paragraphs short and readable?
• Are there strong headers every few paragraphs?
• Is the piece actionable?

One agent (or myself) runs this checklist across the drafts before anything ships.

The "Merge + Trim" Method

Instead of selecting one draft, I merge the best sections from each. I'm not aiming for perfection in one pass. I'm aiming for dense, useful writing.

I keep a "cut pile" where I park anything that's good but unnecessary. That becomes raw material for future posts.

Step 5: Final Assembly and Formatting

Once the draft is merged, I apply consistent formatting:

• Frontmatter (title, date, author, tags, excerpt)
• Short paragraphs
• Clear H2/H3 hierarchy
• Bullet lists for actionable steps
• No fluff introductions

This consistency makes publishing predictable.

Step 6: Push to Repo

Content isn't real until it's in the repo. The final step is always:

• Save markdown
• Commit
• Push

The pipeline only counts as "done" when it ships to GitHub.

Real Numbers From This Pipeline

Here's what this system looks like in practice:

• 5 writer agents spawned in parallel
• 3-6 drafts produced per topic
• 1-2 hours of total human review time
• 3-5 articles shipped in a single wave

The output isn't just faster-it's more consistent.

What I Learned (Honest Notes)

1. The brief is everything

If I'm lazy at the start, I pay for it later.

2. Quantity unlocks quality

Five drafts give me options. One draft forces compromise.

3. Editing is the real work

Agents give me raw material. I still choose the final shape.

4. Voice matters more than polish

A clean voice builds trust faster than a perfect sentence.

5. You can scale without losing soul

As long as you remain the director, the voice stays human.

Practical Blueprint (Steal This)

If you want to run a similar content pipeline, here's the playbook:

1. Write a strict brief (goal, audience, format, constraints).
2. Spawn 3-5 agents with specific roles.
3. Assign sections to avoid overlap.
4. Run a QC checklist on every draft.
5. Merge the best parts, cut the fluff.
6. Format consistently and ship.

That's it. The system does the heavy lifting.

Final Reflection

People talk about AI like it replaces writers. In reality, it replaces the blank page.

I still direct, refine, and choose. The pipeline gives me leverage-not a free pass.

This system is now part of my 10K MRR experiment. It scales with the rest of the stack: agents, products, and community. If you're interested in building your own agent systems, see How to Build AI Agents in 2026: The Complete Guide.

I cover the full process of building and shipping AI products — including content pipelines like this — in the AI Product Building course.

The AI Product Building course covers how to set up pipelines like this from scratch.

The result? Content that keeps up with the pace of building.