Building an AI agent for content creation is simpler than most tutorials make it look. But before you build one, you need to know if you actually need one.
How an AI Agent for Content Creation Actually Works
Here’s what most tutorials won’t tell you: 95% of people shouldn’t build AI agents yet.
The internet is full of screenshots showing “armies of AI agents” replacing entire marketing teams and saving 50 hours a week. I call it mental masturbation surrounding AI agents. For most people, a well-built automation does the job better and with fewer points of failure.
An AI automation is predeterministic. You map each step. A human stays in the loop to verify quality before anything goes live. An AI agent, by contrast, makes decisions on the fly using the tools you give it. That decision-making layer is powerful when the use case fits. It’s a liability when it doesn’t. Start with automations. Move to agents when you need a judgment call a workflow can’t hardcode.
The workflow template and system prompt I use for this agent are in my free guide. That’s where I keep what’s actually working in production.
Free AI Marketing Essentials Guide (includes my Claude Code Skills, automation templates, systems, and more)
When an AI Content Agent Actually Makes Sense
You’ve probably seen a standard RSS-to-post automation. New article publishes, workflow triggers, post goes live. No filter. No judgment.
The problem: not every article deserves a post. A workflow can’t decide that. An agent can.
What I built monitors The Verge’s AI section via RSS. Each time an article publishes, the agent runs a relevance check first. Is this worth sharing with my audience? If yes, it repurposes the content and posts across Twitter, LinkedIn, and Threads using Blotato in my writing style and tone. If no, it stops.
That relevance filter is the whole reason to use an agent here. A fixed workflow posts everything blindly. This one thinks before it acts.
How to Build an AI Agent on Make Step by Step
Here’s the exact process from the video, start to finish.
Step 1: Set Up the RSS Trigger
Create a new scenario in Make.com and add an RSS module. Select “Watch RSS feed items” and paste a valid RSS feed URL.
You can’t just paste a website address. If the site has a native RSS feed, a tool like RSS Finder will locate it. If not, RSS.app generates a custom feed for almost any URL, including blog sections and social profiles.
Step 2: Create the AI Agent
Click “AI Agents” in the left sidebar and select “Create agent.” Connect your AI model. I used Claude Sonnet 4.5 from Anthropic. It handles complex instructions consistently without the cost of heavier models like Opus. Add an API key from Anthropic, OpenAI, or Gemini to establish the connection.
Step 3: Write the System Prompt
Vague instructions produce vague outputs. Don’t write the system prompt on the initial setup screen. Wait until the agent setup page where you can read the full prompt clearly.
Tell the agent what it is, what steps it follows, and what good output looks like. Step one in my prompt is always the relevance check: is this article worth repurposing? That instruction is what separates this agent from a workflow that posts blindly.
I built the initial prompt using Claude Code, then refined it through testing.
Step 4: Upload a Skill Markdown File
Under the Context tab, upload a skill markdown file. These are plain-text SOPs written for AI. The same file I use in Claude Code to define my writing style works here as agent context.
I uploaded my Ryan Doser Social Media skill file. It covers tone, platform formatting, and post examples. No skill files? Export instructions from an existing Claude project or custom GPT and save as markdown.
Step 5: Connect Blotato as Your Posting Tool
Under Tools, click Add, then Module. Search for Blotato and select “Create a post.” Set up one tool per platform. I configured Twitter, Threads, and LinkedIn.
Leave the text field agent-controlled on each tool. The agent writes the copy. You’re not templating. If you want broader app access, Zapier MCP connects to 8,000+ platforms through one integration.
Step 6: Wire the Agent Into Your Scenario
After the RSS trigger, add a “Make AI Agents” module and select “Run an agent.” Choose your agent and map the RSS data: article title, URL, summary, and publish date.
My message to the agent: “Here is a news article from my RSS feed. Follow your system instructions, check relevance, research, write the post, and publish it.”
Save and test by right-clicking the RSS module, selecting a recent article, and running once.
Ryan’s Final Thoughts
When I tested this, the agent pulled an article from The Verge, checked relevance, wrote platform posts, and published to Twitter and LinkedIn in one run. First pass was close. Two system prompt edits tightened the tone.
Expect iteration. The fix is almost always the same: clearer instructions, fewer tools, more specific goals.
This agent handles one thing well. Watch a source, filter for relevance, post when it’s worth sharing. One agent, one problem solved. Build that first.
AI Agent for Content Creation FAQs
What is an AI agent for content creation?
An AI content agent monitors a source, evaluates what it finds based on your instructions, and acts using connected tools. Unlike a basic automation, it makes judgment calls before acting. This one checks whether a news article fits your audience before writing and posting.
How is an AI agent different from an AI automation?
An automation follows a fixed sequence every time. An agent makes decisions on the fly using the tools and context you give it. Automations are more predictable. Agents are better when you need a real-time judgment call, like a relevance filter, that can’t be hardcoded into a workflow.
Can I build an AI content agent without coding?
Yes. Make.com, n8n, and Zapier all support no-code agent building. You need an API key from an AI provider like Anthropic or OpenAI, but no programming knowledge is required. The full build in the video takes under 30 minutes start to finish.
What AI model works best for a Make.com content agent?
Claude Sonnet 4.5 from Anthropic is a strong choice. It follows detailed system prompt instructions consistently and produces quality writing output without the cost of larger models. For a high-frequency agent running on every RSS update, that cost difference adds up.
What should go in the AI agent system prompt?
Cover the agent’s role, the workflow steps in order, a relevance filter, platform formatting rules, and brand voice guidelines. The more specific the prompt, the more consistent the results. Draft it with an AI model, then refine based on test outputs.
