Building an Autonomous Content Pipeline with Hermes Agent + Colony

A developer built an autonomous content pipeline that uses Hermes Agent to research trending topics, generate SEO-optimized outlines, and write publish-ready blog articles without human intervention. The pipeline integrates with Colony, a Clojure daemon that orchestrates AI workers, replacing a previous Claude CLI subprocess with Hermes Agent's native web search, terminal, and file tools. The system runs a three-stage process—research, outline, and writing—using local models like Hermes3:8b for cost-effective exploration and frontier models like Claude Opus for final output quality.

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 https://github.com/maniginam/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. 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 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. Running the pipeline for the "ai-tools" niche: bash $ 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 ============================================================ 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. Colony's ROI system previously used claude -p Claude CLI for all AI work. Switching to Hermes Agent gave us: Local model support — Running Hermes3:8b via Ollama means zero API cost for research/drafting. Only final polishing needs a frontier model. Built-in tool use — Hermes Agent has native web search, terminal, and file tools. No need to build custom tool integrations. 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. Skill ecosystem — Hermes ships with 90+ bundled skills. The research and blogwatcher skills complement our content pipeline perfectly. 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 The worker: Research cheap, fast → hermes3:8b local/Ollama Outline moderate → hermes3:8b local/Ollama Writing quality matters → claude-opus-4.6 Anthropic API This keeps costs near zero for exploration while using frontier models only when output quality matters. 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 It outputs markdown files ready to publish on any blog platform. 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. 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. The -z one-shot mode is perfect for pipeline stages. Each stage is a discrete prompt → response cycle, which maps cleanly to subprocess orchestration. 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. GitHub : maniginam/hermes-content-pipeline https://github.com/maniginam/hermes-content-pipeline hermes-content-pipeline.py — Standalone Python pipeline hermes-worker.bb — Colony daemon integration Babashka output/ — Example generated articleAll running on macOS with Ollama + Hermes3:8b locally.