A few weeks ago, I was experimenting with OpenClaw, an open source personal AI assistant. I told it to set up a social media automation for me. It found the skills it needed, installed them, and created a cron job with an AI agent that would crawl, ideate, and draft content on a schedule.
That moment stuck with me. Not because the output was perfect. It wasn't. But because the barrier had shifted. I didn't configure a workflow. I described what I wanted, and an agent set it up.
The thing is, the main chat session isn't what excited me. Having a single agent do a single thing is nice, but it's not transformative. What's transformative is when you start building a stack of automations. Not one agent doing one task, but ten, twenty jobs running on schedules, handling outreach, research, monitoring, reporting. That's when you go from "AI assistant" to "AI operations."
And that's exactly where it breaks down.
The Ladder Nobody Talks About
There's a progression happening in AI automation right now, and most of the conversation is stuck on the first two steps.
Step 1: Create automation jobs from conversation. This is the breakthrough. Describe what you want in plain language, an agent sets it up. OpenClaw and others have shown this is possible. It's real.
Step 2: Run it in the cloud. Schedule it, trigger it, let it execute without your laptop being open. A lot of good tools are heading here.
But then what?
Step 3: What does the agent get access to? When you're running agents on real data, touching real systems, you need to scope what they can do. This is where most people start getting nervous.
Step 4: Who checks the output? Before anything ships, someone needs to verify it's actually good. Right now, that someone is you. For every job. Every time.
Step 5: Who reviews all of it? This is the real bottleneck. Developers already know this pattern. AI writes most of the code now, but review became the chokepoint. The same thing is happening with AI automations. You either check everything yourself or hope nothing breaks.
The answer I keep coming back to: have an AI do the reviewing, and make that reviewing AI learn from your feedback.
That's what Golemry is. Every job gets a dedicated overseer that validates output before anything gets delivered. You review what needs reviewing. Your feedback improves the overseer's judgment over time. And gradually, you step back. Like training a new hire, not flipping a switch.
The Bigger Bet
I think we're early in a shift where lean teams and eventually one person companies can run operations that used to take twenty people. But only if the automations actually hold up. That's the piece I want to solve.
I'm also dogfooding this thesis myself. Solo founder, AI assisted workflow, building the infrastructure I need to make the one person company real. Golemry is both the product and the proof.
Building This With You
I'm building Golemry in public from day zero, and I mean something closer to early access game development than posting updates. There's a public roadmap where you can suggest and vote on features. A Discord community for real conversations about what matters.
I'm doing this because I genuinely can't build the right version of this alone. I need signal on what matters. I need people who are running AI automations today telling me where it hurts.
Whether you're running into the same walls or just curious where this is heading, come shape what gets built. Your feedback is very welcome (if not required).