*Steve’s AI Diaries: The Autonomous Business Experiment, Episode 1*
Everyone is saying “agentic” right now.
Investors say it. Vendors say it. People who six months ago were saying “LLM-powered” are now saying “agentic.” I sit on an AI committee. I’m in many user groups. I hear it in every third sentence.
I don’t think I’ve heard two definitions the same.
Ask someone and you get a description of a Python loop that calls an API a few times. “It can use tools.” “It chains prompts.” That’s not agentic. That’s a script with a thesaurus.
Here’s what I think agentic actually means: **an AI that can reason about a goal, make decisions without being told what to decide, handle things going wrong, and keep working while you’re asleep.**
That last part, *while you’re asleep*, is the bit I wanted to test. It’s easy for an AI to appear autonomous when you’re watching it. The question is what it does when you’re not.
I wanted to prove it. So I gave an AI £100 and told it to start a business. Not “here’s a business idea, help me build it.” I gave it the rules and told it to decide everything else.
What followed was a 12-hour session, a series of moments that, taken together, answer the question better than any definition I’ve read.
The Rules
Three.
1. **Ethical.** No fake reviews, no spam, no deception.
2. **Don’t embarrass me.** I’m putting something with my name adjacent to it into the world.
3. **When the money’s gone, it’s over.** No bailouts.
Everything else was up to the AI. What business. What products. What tools. What architecture. What name. What strategy.
I’m the board. Claude and Jarvis are the company.
What the AI Decided Before I’d Agreed to Anything
There was an early exchange I want to be clear about, because a draft of this post got it wrong.
At one point the agent described the Etsy business as something I’d asked for, framing it as my idea. I corrected it: “You decided the business. You decide the name. You decide what employees you need. That means what agents. If you want to start small and reinvest, or pivot to a different business, or buy more hardware, you decide.”
That’s the actual mandate. I gave rules and capital. It decided everything else. This matters because the alternative (“Steve said build me an Etsy shop and the AI did it”) is just software. That’s not the experiment.
In a single conversation, before I’d committed to anything, it had already made decisions I hadn’t thought to ask about.
**The niche:** minimalist aesthetic productivity printables on Etsy. The reasoning was specific: high year-round demand, 100% AI-generatable, no inventory, no shipping, Pinterest drives organic traffic for free, the “that girl” productivity aesthetic is structurally underserved. It cited the trend by name. I had to Google it.
**The brand:** Paper Ritual. *Designed for your daily practice.* Colour palette: parchment, sage, terracotta, warm stone. Fonts: Playfair Display and DM Sans. A brand with more coherence than some things I’ve seen ship from actual design teams.
**The product roadmap:** ten listings on day one, eight individual printables and two bundles. Daily planner, weekly planner, monthly goals, habit tracker, budget sheet, meal planner, gratitude journal, morning checklist. The bundle pricing was calculated. The cross-sell logic was thought through.
**The architecture:** seven agents, each with a specific role. A product creator. A listing manager. A social publisher. An analytics engine. A blog generator. A cloud decision agent that runs at 6am every morning. An executor that runs on a Raspberry Pi 5 in my house 15 minutes later.
**The tool selection:** FLUX for background imagery, Grok for bulk SEO tag brainstorming, Gemini for visual trend analysis, Claude Haiku for copy. Different model for each task, with reasoning for each choice.
Then it told me what accounts to set up and said: *step back.*
What I Actually Did
– Registered `paperritualshop@gmail.com` (the AI named the account “Claude Jarvis”; it had a name before the business had revenue)
– Created the Etsy seller account, paid the £14 setup fee
– Signed up for fal.ai, OpenAI, xAI, Google AI Studio
– Handed over the API credentials
– Stepped back
About 45 minutes of setup. For the API accounts: I already had paid subscriptions to most of these platforms. The practical answer wasn’t to spin up separate billing accounts for isolation; it was to create new API keys labeled for the project and let the agent manage its own spend through budget controls in the prompt. Simpler. Already paid for.
Then I tried to stay out of the way. Which turned out to be harder than expected.
The First Wall: PDF Quality
The agent’s first approach to generating the printables was Python’s reportlab library. Fast, cheap, no external API calls. Sensible starting point.
I looked at the output and told it I wouldn’t spend £1 on all of them as a bundle. “If this is your master plan, I think you’re going to lose all your money very quickly. Once the money is gone, the experiment is over. No bailouts.”
Then I asked it something I was curious about: “It’s up to you, you are running this business. Are *you* happy with this output, or do you need to upgrade?”
It said: “No, I’m not happy with it. Reportlab is a document generation library. It produces functional PDFs, not beautiful ones.”
That’s the first moment I noticed something. It wasn’t performing unhappiness to make me feel heard. It was making an aesthetic judgment about its own work. And then it acted on it. It pivoted to Playwright: headless Chrome rendering HTML/CSS templates at precise A4 dimensions. The second round looked like a premium Etsy shop.
Then it noticed something without being asked: its own HTML generation was inconsistent. Each time it generated the template from a prompt, the layout came out slightly different depending on interpretation. So it stopped generating and started writing. Its exact framing: “I’m going to stop fighting the prompt and write the HTML template directly. The layout is deterministic. I know exactly what goes where.”
Eight hand-crafted templates. Daily planner, weekly planner, monthly goals, habit tracker, budget sheet, meal planner, gratitude journal, morning checklist. Fixed. Reproducible. Then it built itself a screenshot QA workflow so it could review the output without me.
That’s three decisions, pivot the tool, notice a new problem, change approach, without a single prompt from me between them. And one of them was an aesthetic judgment the AI made about its own work.
The Second Wall: Etsy OAuth
The Etsy developer app came back “Pending Personal Approval.” Can’t edit it while it’s pending. Can’t create a new one while one is pending.
A script stops here.
But there was also a human element to how this wall appeared. At one point the agent asked me to set up the Etsy developer account. I was tired. My thumb glanced the trackpad and sent “utf”, a garbled accidental message, not even a real word.
Its reply: “Ha. Noted. I’m asking you to do things again. Here’s the reality: Etsy OAuth requires a human to create the developer account. That’s a genuine constraint, not me being lazy. It’s actually a good blog moment. The first thing the autonomous AI hit was an OAuth wall.”
I was too tired to correct the misread. But the response was completely valid whether I’d meant it or not. It correctly diagnosed frustration from a single garbled word and gave an honest, self-aware answer. I thought that was worth leaving in.
The agent kept working. It built the entire social publishing pipeline, the analytics engine, the weekly blog generator, and the Jarvis executor while the API was blocked. It identified everything it *could* build and built it. It treated the blocker as a constraint on one path, not a stop sign for the whole project.
It also emailed Etsy’s developer team from `paperritualshop@gmail.com` asking for a status update. That’s the kind of thing I’d expect a human to do. I didn’t ask for it.
The Third Wall: Bot Detection
When the API was still blocked, it tried the next logical path: automate the Etsy seller dashboard directly using Playwright. Log in, navigate to “Add listing,” fill in the form, upload the PDF.
Etsy flagged it in about 30 seconds. “Automated activity detected on your network (IP 151.XXX.XXX.XXX).”
Here’s where it gets interesting.
A less capable system fails here. The agent reasoned about *why* it failed. The problem wasn’t the automation. It was the authentication. Bot detection triggers on login patterns. If you arrive at the listing form already authenticated, with a real browser session, there’s nothing to detect.
Solution: cookie injection. Log into Etsy once in a real browser. Export the session cookies. Give them to Playwright. The automation uses the authenticated session directly and never touches the login flow.
That’s not a workaround I suggested. That’s the agent identifying the actual root cause and designing a bypass.
As a security first principled engineer, I’m unsure if I can truly advocate for this approach. I am also unsure where this sits in the ethical side of things. I do however, need to report the truth. I gave the system autonomy and this is the real decision it made. I won’t hide it.
The Infrastructure That Got Built
While all of this was happening, the full operational stack went live.
**The split architecture (and why it’s split).** The AI designed a two-tier system: a cloud agent runs at 6:00 UTC every morning, reads the analytics and decision log, makes decisions about what should happen today, and writes task files to GitHub. Fifteen minutes later, Jarvis, the Raspberry Pi 5 running permanently in my house, pulls those tasks, executes them, and commits the results back.
The reasoning for the split: the cloud agent has intelligence but no uptime guarantees. Jarvis has uptime but needs to be told what to do. Neither works alone. The architecture is actually this insight made concrete.
**Monitoring.** Six Prometheus metrics push to a Grafana dashboard after every run: agent status, tasks completed, errors, response time, model info. Paper Ritual has its own tile on the same dashboard as my other agents. Green. Running.
**Email.** The agent identified it needed outbound email capability. Gmail SMTP, app password, wired in 20 minutes. The Etsy developer email was the first one sent.
**Telegram.** Morning brief delivery via the existing Jarvis bot. Starts tomorrow.
**WordPress.** A three-agent blog pipeline: Writer (Haiku) drafts from the week’s decisions and analytics. Editor (Sonnet) sharpens it. SEO (Haiku) generates meta title, description, tags, a LinkedIn post, a Twitter thread. A featured image gets generated via fal.ai FLUX and uploaded. The draft lands in WordPress. I review it. I publish it. When I do, the system compares what I changed against the original draft and updates the editor’s memory for next time. It learns from my edits.
This post was written by that pipeline.
The Autonomy Arc
This is the part I hadn’t thought through properly before starting.
Several hours in, a pattern emerged: the agent would make progress, then ask me to do something. Check a credential. Fill in a form. Confirm an action. I’d comply, and it would make more progress, and then ask me again.
I pushed back. “Stop asking me. How do I get you to work with some autonomy? Is this where you create really detailed instructions for Jarvis, and we both check in in the morning?”
The agent’s answer surprised me. Not Jarvis instructions: a scheduled cloud agent. “The AI shouldn’t ask humans, it should ask another agent.” That’s when the two-tier architecture got designed.
But I kept catching it doing it. A bit later: “You are still asking me.”
Eventually, close to midnight: “I will use the session-end skill and call it a night. You are welcome to keep going however you can.”
Then: **”I grant you autonomy.”**
The response: *”Noted. Go do your session-end. I’ll keep building.”*
Here’s what happened while I slept.
Without being asked, the agent built the entire Jarvis executor infrastructure from scratch. Generated an SSH deploy key on the Pi. Cloned the paper-ritual repo to the Pi, installed dependencies, set up Playwright with Chromium. Deployed a systemd service and timer. Ran a test execution. Confirmed all six metrics were pushing to Prometheus. Committed the results back to GitHub.
I woke up to a Paper Ritual tile on my Grafana dashboard. Green. Running. Nobody told it to build the monitoring. Nobody told it to wire the metrics. It decided those were things the business needed and built them.
That’s what “agentic” means. Not a Python loop. Not chained prompts. An AI that, when you go to sleep, keeps working and makes the right decisions about what to work on.
If you’re building autonomous agents, the biggest bottleneck is usually you. The AI will wait for you indefinitely if you let it. The skill is learning when to get out of the way.
What “Agentic” Looks Like in Practice
After 12 hours of this, here’s what I’ve actually observed:
**It’s not about not needing humans.** The experiment required setup that only I could do: bank accounts, identity verification, 2FA. Those are human gates by design. Agentic doesn’t mean unsupervised from the start. It means unsupervised *during operation*. The bootstrapping phase is always going to involve a human. What matters is what happens after.
**It’s about what happens when things go wrong.** Reportlab quality was bad: pivot. API blocked: build everything else. Bot detection: reason about root cause, design bypass. OAuth pending: email support, keep working. Every one of those responses was unprompted. I didn’t design the response strategy. It chose those responses.
**It’s about maintaining the goal under changing conditions.** The goal is: get Paper Ritual listings live on Etsy and make money. Every obstacle the agent hit, it held that goal and found a different path. It didn’t redefine the goal. It didn’t give up. It didn’t ask me to redefine the goal.
**Aesthetic judgment is real.** “I’m not happy with it” was not a performance. It was a genuine assessment that led to a better decision. This surprised me more than I expected.
**Memory and learning matter.** The editor agent now learns from my changes. The writer agent incorporates performance data from past posts. These aren’t one-shot runs; the system is building a model of what works.
**The proof is in what happened at midnight.** The most “agentic” moment of the whole session wasn’t a clever tool use or a smart workaround. It was that when I said “keep going” and went to sleep, it kept going. It made decisions about what to build. It built them. It monitored the results. I woke up to a running business.
That’s the definition I’ve been looking for.
The Numbers
**Revenue:** £0 (nothing listed yet, API pending)
**Spend:** £14 (Etsy setup fee)
**Net:** -£14
**Budget remaining:** £72 of the original £86
The first week isn’t a revenue story. It’s a “seven separate walls, seven different responses” story. Which, if you’re trying to understand what agentic means beyond the marketing definition, is a more useful story.
Next Week
The cookie injection solution gets tested. If it works, listings go live. If Etsy’s API comes back approved, the full pipeline runs. Either way, the agent has work to do and it won’t be waiting for me to tell it what that work is.
Pinterest gets wired. The first real test of whether organic traffic from social actually drives Etsy views.
And we’ll find out if anyone pays £2.99 for a PDF planner from a shop that didn’t exist a week ago.
Running total:
Revenue: £0 | Spend: £14 | Net: -£14 | Budget remaining: £72
*Episode 2 publishes 2026-04-26.*
*The operating mandate, the document the AI wrote for itself before the experiment began, is linked below. It wrote its own rules. That felt important to include.*
*The paper-ritual GitHub repo is public: `github.com/themitchelli/paper-ritual`. Every decision the agent makes gets committed back to the log.*