Building a Blog With AI Agents

Why start over?

I had a blog. React frontend, Contentful CMS, Azure Cosmos DB backend, 300+ unit tests, deployment scripts for Azure — the whole enterprise stack. It sat untouched for 7 months.

The problem wasn't the code. It was the friction. Writing a post meant logging into Contentful, fighting with their rich text editor, and hoping the frontend (which was never actually built — the template zip was still unextracted) would eventually render it.

So I scrapped it. All of it.

The new stack

Here's what replaced it:

• Next.js 15 with App Router and MDX for content
• PostgreSQL for comments, ratings, and feedback
• Tailwind CSS for styling
• Docker Compose for deployment
• Hetzner VPS — the same box running curate-me.ai

Total infrastructure cost: included in the $5/month I'm already paying.

The real play: agents

This blog isn't just a blog. It's a reference application for curate-me.ai, the AI agent governance platform I'm building.

The content pipeline runs on OpenClaw agents managed through the platform:

1. blog-researcher — a web-profile runner that scans HN, Reddit, and arxiv daily for AI news
2. blog-writer — a base-profile runner that turns research briefs into MDX drafts
3. blog-moderator — a locked-profile runner that watches comments for spam
4. blog-promoter — a web-profile runner that creates social posts when I publish
5. blog-analyst — a locked-profile runner that tracks what readers care about

Every agent runs through the curate-me.ai gateway. Every LLM call is cost-tracked, PII-scanned, and logged. Drafts go through a human-in-the-loop approval queue before they publish.

Why this matters

Most AI agent demos are toy examples. "Look, my agent can search the web and write a summary!" Cool. Now run 6 of them in production with cost caps, audit trails, and approval workflows.

That's what curate-me.ai does. And this blog proves it works by using it every day.

Every post you read here will show:

• Which agents contributed
• What it cost in AI
• How the human-AI collaboration worked

Radical transparency. Because if you're going to build trust in AI agents, you have to show the work.

What's next

This is post #1. The agents are warming up. Next, I'll write about:

• Setting up the OpenClaw research agent and its SOUL.md configuration
• How the curate-me.ai gateway tracks costs across a fleet of agents
• The comment moderation pipeline — from spam detection to HITL approval
• Time-travel debugging: replaying what an agent did step by step

If you're interested in running AI agents in production with proper governance, check out curate-me.ai. Or just keep reading — every post here is a case study.