Let's Build Your First AI Agent

Everyone's talking about AI agents. Most people have no idea what they actually are — or that you can build one yourself without writing a single line of code.

Let's fix that today. By the end of this post, you'll have built a working AI agent that does something genuinely useful.

Wait, What's an AI Agent?

Forget the sci-fi definition. An AI agent is simply an AI that can take actions, not just answer questions. Instead of you copy-pasting AI outputs into other tools, the agent does it for you.

Think of it this way:

• Chatbot: You ask a question, it answers. Done.
• Agent: You give it a goal, it figures out the steps, uses tools, and delivers a result.

A simple example: instead of asking ChatGPT to "write a LinkedIn post about our new feature," an agent could monitor your product changelog, draft posts for each update, format them for LinkedIn, and queue them in your scheduling tool — automatically.

The Building Blocks

Every AI agent has three components:

1. A trigger — what kicks it off (a schedule, a new email, a form submission)
2. AI processing — the LLM that does the thinking (GPT-4, Claude, etc.)
3. Actions — what the agent does with the result (sends an email, updates a database, posts to Slack)

That's it. Trigger → Think → Act. Simple.

Let's Build One: The Competitor Monitor Agent

Here's what we're building: an agent that monitors competitor websites weekly, summarizes what changed, and posts a digest to your Slack channel.

What You'll Need

• A Make.com account (free tier works)
• A Slack workspace
• An OpenAI API key (or you can use Make's built-in AI module)

Step 1: Create a New Scenario in Make.com

Log into Make.com and click "Create a new scenario." This is your agent's workspace — each bubble represents a step in the workflow.

Step 2: Add the Trigger

Add a Schedule module and set it to run every Monday at 9 AM. This is your trigger — the agent wakes up once a week.

Step 3: Fetch Competitor Content

Add an HTTP module to fetch your competitor's blog or changelog page. Set the URL to their public page. Make will grab the raw HTML content.

Repeat this for each competitor you want to track (up to 3-4 for a clean digest).

Step 4: AI Analysis

Add an OpenAI module (or Make's AI module). Use this prompt:

"Analyze the following web page content. Identify any new products, features, pricing changes, or notable announcements. Summarize the key changes in 3-5 bullet points. If nothing significant changed, say 'No major updates this week.' Content: [paste the HTTP output]"

Step 5: Post to Slack

Add a Slack module. Configure it to post to your #competitive-intel channel. Format the message with the competitor name and the AI's summary.

Step 6: Test and Activate

Run the scenario once manually to test. Check your Slack channel. If the message looks good, toggle the scenario to "Active" and you're done.

Total time: 30-45 minutes.

Making It Smarter

Once your basic agent works, here are ways to level it up:

• Add a filter: Only post to Slack if the AI detects actual changes (skip "no updates" weeks)
• Store history: Add a Google Sheets module to log every change, building a competitor timeline
• Multi-source: Add RSS feeds, Twitter/X monitoring, or job postings to get a fuller picture
• Team routing: Use AI to categorize changes (pricing vs. features vs. hiring) and route to different Slack channels

Other Agents You Can Build This Way

Once you get the pattern, the possibilities open up fast:

• Lead qualifier: New form submission → AI scores the lead → routes to the right salesperson
• Content repurposer: New blog post → AI creates social posts, email snippet, and tweet thread
• Support triager: New support ticket → AI categorizes urgency and topic → assigns to right team
• Meeting summarizer: Recording uploaded → AI transcribes and extracts action items → posts to project channel

The Key Takeaway

Building AI agents isn't about coding skills. It's about identifying repetitive workflows where you're the bottleneck between an input and an action. Find those bottlenecks, and you've found your next agent.

Start simple. One trigger, one AI step, one action. Get that working perfectly. Then add complexity.

The best first agent isn't the most impressive one — it's the one you'll actually use every day.