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Gina Martiny
Gina Martiny

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Building an Autonomous Content Pipeline with Hermes Agent + Colony

#hermesagentchallenge #devchallenge #agents #ai

Hermes Agent Challenge Submission: Build With Hermes Agent

What I Built

An autonomous content pipeline that uses Hermes Agent to research trending topics, generate SEO-optimized outlines, and write publish-ready blog articles — all without human intervention.

The pipeline plugs into Colony, a Clojure daemon I built to orchestrate autonomous AI workers for passive income projects. Hermes Agent replaces the previous claude -p subprocess as the "brain" that does the actual research and writing work.

How It Works 

Colony Daemon (Clojure)
  └── ROI Task Queue (SQLite)
       └── hermes-worker.bb (Babashka)
            └── Hermes Agent (hermes -z)
                 ├── Stage 1: Research topics (web search tools)
                 ├── Stage 2: Generate outline (competitor analysis)
                 └── Stage 3: Write full article (markdown output)
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The daemon assigns roi-write-article tasks. The Hermes worker picks them up, runs a 3-stage pipeline through Hermes Agent, and reports results back via Unix domain socket IPC.

Demo

Running the pipeline for the "ai-tools" niche:

$ python3 hermes-content-pipeline.py "ai-tools" --count 1

============================================================
  Hermes Content Pipeline
  Niche: ai-tools | Site: aileapers.com | Articles: 1
============================================================

[1/3] Researching topics...
  Found 3 topic ideas:
    1. The Rise of AI-Powered Content Generation Tools [high]
    2. AI-Powered SEO Optimization Techniques [high]
    3. Best AI Image Generation Tools [high]

--- Article 1/1 ---
[2/3] Generating outline...
  Outline: 4 sections, ~2000 words
[3/3] Writing article...
  Written: 971 words
  Saved: output/2026-05-30-ai-content-tools.md

============================================================
  Pipeline Complete: 1 articles generated
============================================================
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Each stage is a separate Hermes Agent invocation with tool access. The research stage uses web search to find trending topics. The outline stage analyzes competitor content. The writing stage produces publish-ready markdown.

Architecture Deep Dive

Why Hermes Agent?

Colony's ROI system previously used claude -p (Claude CLI) for all AI work. Switching to Hermes Agent gave us:

  1. Local model support — Running Hermes3:8b via Ollama means zero API cost for research/drafting. Only final polishing needs a frontier model.

  2. Built-in tool use — Hermes Agent has native web search, terminal, and file tools. No need to build custom tool integrations.

  3. Model flexibility — Can switch between local Hermes3 and cloud models (Claude, GPT) with a flag. Use cheap models for research, expensive ones for final output.

  4. Skill ecosystem — Hermes ships with 90+ bundled skills. The research and blogwatcher skills complement our content pipeline perfectly.

Colony Integration

The Babashka worker (hermes-worker.bb) bridges Colony's Clojure daemon with Hermes:

;; Invoke Hermes Agent for topic research
(defn hermes-run [prompt & {:keys [model timeout-ms]}]
  (let [cmd (cond-> [hermes-bin "-z" prompt]
              model (into ["-m" model]))
        p   (proc/process {:out :string :err :string} cmd)]
    ;; ... timeout handling, result parsing
    ))
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The worker:

  • Receives tasks from Colony's SQLite queue via IPC
  • Sends heartbeats every 30s to stay alive
  • Runs Hermes Agent for each pipeline stage
  • Reports results back to the daemon

Multi-Model Strategy 

Research (cheap, fast)     → hermes3:8b (local/Ollama)
Outline (moderate)         → hermes3:8b (local/Ollama)
Writing (quality matters)  → claude-opus-4.6 (Anthropic API)
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This keeps costs near zero for exploration while using frontier models only when output quality matters.

The Python Standalone

For those who just want the content pipeline without Colony, there's a standalone Python version:

# Install Hermes Agent
curl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.sh | bash

# Pull local model
ollama pull hermes3:8b

# Run the pipeline
python3 hermes-content-pipeline.py "home-office" --count 3
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It outputs markdown files ready to publish on any blog platform.

What I Learned

  1. Local models are surprisingly capable for research tasks. Hermes3:8b handled topic research and outlining well. The quality gap only shows in long-form writing.

  2. Hermes Agent's tool integration is smooth. Web search and terminal tools worked out of the box — no custom MCP servers or tool definitions needed.

  3. The -z one-shot mode is perfect for pipeline stages. Each stage is a discrete prompt → response cycle, which maps cleanly to subprocess orchestration.

  4. Agentic pipelines benefit from stage separation. Rather than one mega-prompt, breaking into research → outline → write lets you use different models per stage and retry individual failures.

Source Code

GitHub: maniginam/hermes-content-pipeline

  • hermes-content-pipeline.py — Standalone Python pipeline
  • hermes-worker.bb — Colony daemon integration (Babashka)
  • output/ — Example generated article

All running on macOS with Ollama + Hermes3:8b locally.

What's Next

  • Add a review stage (Hermes evaluates its own output before saving)
  • Deploy monitoring: Hermes checks published articles for broken links and SEO drift
  • Expand to other revenue streams: KDP book outlines, Redbubble design briefs
  • Build a custom Hermes skill for the full Colony<>Hermes integration

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