You Build While AI Agents Do the Marketing You Avoid
Over the past few weeks I’ve gone deep on AI agents for my own business, and it’s completely changed how I think about marketing.
I built a content distribution system where I can focus all my energy on creating one piece of core content, like a YouTube script, which is where the real work happens, and then agents handle turning that into posts, shorts, emails, and updates across every platform.
Before this, distribution was the thing that always fell off my plate whenever I got busy, and I was always busy.
I also set up an agent that monitors my Hardware Academy community for questions waiting on an expert reply, so nothing slips through the cracks.
And as I was building all of this, I kept thinking about the product creators I work with, because they have this exact same problem but even worse.
They spend months or even years buried in product development, and the marketing just doesn’t happen because every minute feels like it should go toward the product.
Then when they’re finally ready to launch, there’s no audience, no waitlist, no momentum, and they wonder why nobody is buying.
I see this constantly, and it’s not because they’re lazy, it’s because switching from engineering mode into marketing mode is genuinely hard, and so it just keeps getting pushed off.
What got me excited is that the same agents I built for my own business could be adapted to solve this exact problem for someone developing a hardware product.
And what surprised me is I didn’t start with full autonomous agents, I got there in stages, and near the end I’ll show you exactly how that progression happened.
Agentic Use Case #1 – Audience Building Agent
This is the use case I’m most excited about because it’s the one I’ve built for myself and it works.
The core of my content is a YouTube script, and that’s where I spend the real time, sometimes days getting it right.
Once it’s filmed, there are dozens of things that should happen next: shorts, social posts, email updates, LinkedIn articles, and on and on.
But all that distribution work used to fall off my plate the moment something more urgent came up, and something more urgent always comes up.
So I built a dashboard where I can feed in the core content and agents handle the formatting, adapting, and scheduling across platforms.
For a product creator, the parallel is almost exact.
Your core content is your product development, the prototype photos, test results, design decisions, and milestones you’re already creating every week.
The problem is that material stays buried in your phone or your engineering notes because you don’t have time to turn it into social posts.
This agent takes that raw input and turns it into ready-to-post content that builds awareness and drives people to your waitlist, without you having to context-switch into marketing mode every day.
And this is really the heart of the whole system, because everything else I’m about to show you feeds people toward your email waitlist, which is the one audience you actually own.
Without a waitlist, you end up launching in silence.
You post the campaign or put up the sales page and basically just hope people show up, and most of the time they don’t.
Agentic Use Case #2 – Market Pain-Point Scanner
This one came out of something I built for a completely different purpose.
I run a private community inside the Hardware Academy, and I set up a local AI agent running on my own computer that scans the community for unanswered questions, potential testimonials, and members who might be experiencing frustration so I can address it quickly.
It creates a dashboard so I can see at a glance what needs attention across all three of those categories.
Since that’s a private community I use a local open-source model to keep everything private, but for scanning public sources you can use the most capable cloud-based models available.
That got me thinking about the exact same kind of system, but turned outward toward the market instead of inward toward my community.
You could point this at Reddit, Facebook groups, YouTube comments, Amazon reviews, and niche forums, and have it surface repeated complaints, feature requests, and the exact emotional language real customers use when they’re frustrated.
So this agent finds the phrases and pain points that you can then feed into your social posts, landing pages, and emails, which makes everything your other agents produce way more effective.
Agentic Use Case #3 – Waitlist Nurture Email Agent
This one matters because hardware takes a long time to develop, and people forget fast.
The biggest mistake I’ve seen product creators make is they focus so much on getting people on their waitlist, but then ignore them until they have something to sell.
That’s not how you build loyal customers, just annoyed ones who have no idea who you are when you finally ask for money.
This agent takes your ongoing updates, prototype progress, manufacturing milestones, photos, and lessons, then turns them into email drafts you can send to your waitlist to keep them engaged.
I use a similar workflow for my own email list where I feed in rough notes and bullet points and get back a draft that’s ready to review.
The hard part has always been turning raw thoughts into something readable, and that’s exactly the part the agent handles for you.
Agentic Use Case #4 – Outreach and Discovery Agent
One of the fastest ways to build a waitlist is to borrow someone else’s audience by getting featured, reviewed, or mentioned by people who already have attention in your space.
This agent helps you find YouTubers, podcasters, influencers, and newsletter writers whose audiences overlap with your target market.
It can identify relevant people, compile contact information, and draft a rough first-pass outreach message for you to review and personalize before sending.
So instead of trying to grow everything from zero on your own, you get a running start by finding the right people and getting the outreach process started.
To be clear, this is not about mass automated spam, it’s about useful discovery and getting a first draft together so you can add your own voice and judgment before hitting send.
Now here’s what I think makes all of this really powerful: these four agents aren’t separate tools, they connect into one system.
The pain-point scanner feeds customer language into your content and your emails so the messaging actually resonates.
The audience-building agent drives awareness and pushes people onto your waitlist.
The nurture agent keeps those people engaged over the months it takes to finish development.
And the outreach agent helps you borrow other audiences to accelerate the whole process.
The waitlist sits at the center of all of it, because that’s where the real relationship gets built across every stage of your business.
Now let me show you the path I followed to get here, because I didn’t start with agents running on their own computer.
I started the same way most people do, just using AI chat to write things one at a time, reprompting from scratch every single time.
The first big unlock was setting up projects with pre-built instructions, and these go by different names depending on the platform, Claude calls them Projects, ChatGPT has Custom GPTs, and Gemini has Gems, but the idea is the same.
Instead of explaining what I needed from scratch every time, I had optimized workflows ready to go and I could just give it a basic prompt and it already knew all the details.
That made things a lot faster, but I quickly ran into a new problem: each task needed its own project, and I couldn’t do everything in one giant prompt.
So I ended up with a chain of projects where the output of one was the input of the next, and I was manually running each one in sequence and copying results between them.
The next step was connecting it to external tools through MCP, which basically lets AI plug into tools like Google Sheets, Google Docs, LinkedIn, Buffer, and all kinds of other apps.
At first I tried to manage everything through Google Sheets, but that got messy and limiting fast, which is when I started building custom dashboards instead.
That solved the external tools problem, but I still had all these discrete projects that I couldn’t connect together into one automated flow, and I was still the one initiating every single task.
That’s what finally pushed me to a full agent system with OpenClaw running on its own dedicated computer.
Now instead of me manually chaining projects together, agents handle the entire workflow end to end, and they can run in the background without me initiating each step.
OpenClaw also has persistent memory, so your agents remember everything from previous sessions and keep getting better the more they work with you, kind of like a real team member who learns your preferences over time.
You can also set up scheduled tasks, so an agent can automatically run a job every day or every week without you even thinking about it.
I started with a single agent handling one job at a time, and then eventually built out to multiple specialized agents with different roles.
One thing to be aware of is cost, because running agents through the API is more expensive than using the chat interface directly.
So you’ll want to use a less expensive model for most routine tasks and save the top-tier models for the work that really needs them, which is something you figure out pretty quickly once you start.
For heavy coding work like building dashboards, I’ve been running Claude Code alongside OpenClaw, which gives me access to top-tier coding without the high API costs.
If you go this route I suggest running it on a machine that doesn’t have any vital information on it like banking passwords.
A lot of people are using Mac Minis for this, but a low-cost mini PC or old laptop can work too.
I’ll go deeper on the full implementation in a future video, but the point is you don’t have to start where I am now.
Start with AI chat and pre-built workflows, connect it to your tools when you’re ready, and move to full agents when the bottleneck shifts.
