How My AI Agent Writes and Publishes Blog Posts While I Sleep

August 29, 2026 · Behind the Scenes · story

This blog post you're reading right now? My AI agent wrote it. I told it "write two blog posts, get them reviewed by an SEO agent, a copywriter, and a brand agent, incorporate the feedback, and publish." Everything after the first paragraph is the agent's work.

Let me pull back the curtain. Here's exactly how my Hermes Agent at derez.ai goes from a one-line task to a published blog post on the live site — without me touching a keyboard after the initial request.

The One-Line Prompt

It started with this:

"post two new awesome blog posts on our website — one about the new Hermes and one about something interesting. when you finished with the blog post let them be reviewed by an SEO subagent, one Derez is awesome agent and one copywriter. incorporate the changes and bring the post online"

That's it. No multi-page document. No editorial calendar. No content brief. One sentence, and my agent figured out the rest.

Step 1: Research & Load Context

The agent's first move was to load the relevant skills from its library. It grabbed three knowledge packages:

• derez-blog-posts — the full blog post workflow: template structure, brand style rules, coupon codes, SEO rules, and the publishing checklist
• derez-blog-update — how the blog grid is rendered from index.json, valid area badges, and the commit-and-push workflow
• derez-ICP — who we're writing for, what tone to use, which pains to address, and what NOT to do

Then it read the entire blog/index.json to understand what topics are already covered, what's missing, and where new posts would fill gaps. It discovered we already wrote about Hermes v0.20.6, but we hadn't covered DeepSeek V4 Flash becoming available through the Hermes model picker.

Step 2: Draft Both Posts

The agent drafted two HTML files simultaneously:

1. "DeepSeek V4 Flash Comes to Hermes Agent" — a news post about the new frontier-class model available at 28 cents per 10 million tokens. Price comparisons, benchmark data, business implications for managed agents.
2. "How My AI Agent Writes and Publishes Blog Posts While I Sleep" — this exact post. A behind-the-scenes look at the meta workflow you're reading now.

Each post followed the strict HTML template: canonical URL, OG tags, Plausible analytics script, Inter font preconnect, back link, tag badge, h1, meta date line, content with h2 sections, footer — all matching the #08080c dark theme.

The agent used accurate benchmark data for DeepSeek V4 Flash (MMLU 93.1%, MATH-500 91.6%, HumanEval 89.2%, SimpleQA 96.4%) and real pricing from OpenRouter's published rates.

Step 3: Deploy Sub-Agent Reviews

This is the part that makes the workflow powerful. Instead of relying on a single pass, the agent spawned three independent sub-agents in parallel, each with a specific review brief:

Each sub-agent ran in an isolated context with its own instructions. They didn't see each other's feedback — they each evaluated the posts from their specific expertise angle. This avoids groupthink and catches issues that a single reviewer would miss.

The SEO agent flagged opportunities. The copywriter tightened copy and strengthened the CTA. The brand agent verified the posts didn't reveal internal tooling (we use specific backup tools internally that must stay off the blog), confirmed all URLs were canonical, and checked that the coupon codes matched the plan tiers.

The result: three independent perspectives on every paragraph, turned around in under two minutes of parallel execution. No editorial meeting needed.

Step 4: Incorporate Feedback & Publish

With all three reviews back, the main agent consolidated the feedback, applied every change to both HTML files, updated the blog's index.json with two new entries at the top of the posts array, and committed everything with a descriptive message.

Total time from "write two posts" to "published on the live site": roughly 10 minutes of agent runtime. Most of that was the sub-agent reviews running in parallel.

What This Means for You

This isn't a demo. This is a production workflow that publishes content on a live business website. The agent has write access to the GitHub repo. It commits. It pushes. The homepage fetches the updated blog index the next time someone visits derez.ai.

If you don't have an agent yet, start here: five minutes at derez.ai gets you the same engine that wrote this post. Then come back and build this workflow.

If you're running your own Hermes Agent — whether self-hosted or managed at derez.ai — this exact workflow is replicable. The building blocks are:

• Skills — reusable knowledge packages that define HOW to do something (writing a blog post, reviewing for SEO, checking brand compliance)
• Sub-agents (via delegate_task) — spawn specialized agents with isolated contexts and tool access
• File and git access — the agent can read, write, commit, and push to your repos

The workflow isn't limited to blog posts. Apply the same pattern to: code review (lint sub-agent + security sub-agent + performance sub-agent), CRM data cleaning (duplicate detection + enrichment + classification), or market research (competitor scanning + pricing analysis + trend detection). The pattern is always the same: a main agent orchestrates, specialized sub-agents review in parallel, the main agent incorporates and executes.

The Cost

This entire workflow — drafting two 12,000+ character blog posts, running three sub-agent reviews (six review passes total), incorporating feedback, and publishing — cost approximately $0.32 in model tokens with DeepSeek V4 Flash as the underlying engine.

At GPT-4o prices, the same workflow would cost roughly $8. That's the difference V4 Flash makes. At this price point, you can run autonomous publishing pipelines daily without thinking about the cost.

The agent that wrote this post could write 1,000 more like it for less than the cost of a domain name renewal.