How We Built an Auto-Generating AI Content System with Cursor Agents

The Idea: Let AI Build the Blog

What if your blog system could write itself? Not just the content — the entire infrastructure. The rendering pipeline, the SEO optimization, the interactive components, even the article you're reading right now.

That's exactly what we did. We used Cursor's cloud agents to build GroupChat's blog system from scratch. One prompt, one agent, one complete content platform.

What We Asked For

The prompt was straightforward. We needed a blog content system with:

• MDX content with custom component blocks (callouts, demos, author cards)
• Interactive previews that showcase product features inline
• SEO optimization that ranks well, works on mobile, and generates social cards
• Auto-generated meta images from post content when no image is uploaded
• Fast rendering with server-side generation and static params

The agent took it from there.

The Architecture

Here's what the agent built — and how each piece fits together.

Content Pipeline

Blog posts live as .mdx files in content/blog/. Each file starts with YAML frontmatter that defines metadata: title, description, author, tags, co-authors (including AI agents), and publication status.

---
title: "My Post"
description: "A summary for SEO and social cards"
date: "2026-03-24"
author:
  name: "GroupChat Team"
  avatar: "/assets/group-chat-logo.png"
coAuthors:
  - name: "Cursor Agent"
    isAgent: true
tags: ["ai", "cursor"]
published: true
---

At build time, gray-matter parses the frontmatter, reading-time computes the read duration, and next-mdx-remote compiles the MDX body into React components on the server. No client-side MDX compilation, no bundle bloat.

Custom MDX Components

The MDX renderer maps special tags to React components. The agent created a component registry pattern where each custom block is a named export:

• <Callout> — styled alert blocks with info, tip, warning, and danger variants
• <Demo> — interactive component previews loaded from a registry
• Headings — auto-slugified with anchor links for deep linking
• Code blocks — syntax highlighted with sugar-high, zero client JS
• Links — smart detection of internal vs. external, proper rel attributes
• Tables — responsive with horizontal scroll on mobile

This means content authors can mix prose with interactive UI without touching React code.

SEO Stack

The agent didn't just build a renderer — it built a complete SEO pipeline:

The OG image generator is particularly clever — when no custom image is provided, it generates a branded card from the post's title, description, tags, and author. The card includes an "AI co-authored" badge when the post has an agent contributor.

What the Agent Got Right

A few things impressed us about the agent's output:

Static Generation

The agent used generateStaticParams to pre-render all blog posts at build time. This means zero server compute at request time — pages serve from the CDN edge.

export async function generateStaticParams() {
  return getAllPostSlugs().map((slug) => ({ slug }));
}

Component Isolation

Interactive demos run as client components inside server-rendered MDX. The agent used a registry pattern to lazily load demos, keeping the base bundle small:

const DEMO_REGISTRY: Record<string, () => ReactNode> = {
  TaskCreationDemo: () => <TaskCreationDemo />,
  ContentPipelineDemo: () => <ContentPipelineDemo />,
};

Accessible Defaults

Callout blocks include role="note" and aria-label. Social share buttons have proper aria-label attributes. The table of contents uses a <nav> element with aria-label="Table of contents". These weren't requested — the agent added them by default.

What We Had to Fix

No AI output is perfect. Here's what needed human intervention:

1. Edge runtime conflict — the OG image generator was set to runtime = "edge", but it imported fs for reading MDX files. Edge runtimes don't support Node.js built-in modules. We removed the edge directive to let it run on the Node.js runtime instead.

2. Reading time parsing — the JSON-LD schema tried to compute wordCount from the readingTime string (e.g., "5 min read") using parseInt. This works coincidentally but is fragile.

These were caught in minutes. The agent saved hours of scaffolding work, and the fixes were trivial.

The Meta Part

Here's where it gets recursive. This article:

• Was written by a Cursor agent to describe the system it built
• Is rendered by the MDX pipeline that same agent created
• Contains an interactive demo (the pipeline visualizer above) also built by the agent
• Has its OG image auto-generated by the image route the agent wrote
• Appears in the sitemap the agent configured
• Shows an "AI co-authored" badge using the co-author system the agent designed

The agent built the factory, then used the factory to build the product description.

Takeaways for Your Team

If you're considering AI-assisted development, here's what we learned:

1. Start with clear requirements — the agent works best when you describe what you need, not how to build it
2. Review the output like a PR — treat agent code as a pull request from a junior engineer. The architecture is usually solid; the edge cases need human eyes
3. Iterate fast — agents work in minutes. If the first pass misses something, adjust the prompt and run again
4. Let AI handle the boilerplate — SEO metadata, structured data, sitemap generation. These are tedious but important. Agents get them right consistently

The future of content isn't just AI-generated text. It's AI-generated infrastructure — systems that build themselves, document themselves, and improve themselves.

Built with Cursor agents and the GroupChat blog platform. View the source on GitHub.