I Fired My AI Company After Seven Days
I built an AI company to run my life. Nine agents with defined roles, working around the clock on my own hardware: planning my days, curating my learning pipeline, producing content, handling operations. They coordinated through a shared task board, and I was the only human in the loop.
Seven days later I shut it down.
The numbers made the decision for me. In one week, nine agents generated roughly 265 tasks and shipped exactly one thing I actually used: a single published post. The score I’d designed the whole company around, a weekly publishing streak, stood at zero.
This is the post-mortem, and I’m writing it down because the failure mode isn’t specific to me. If you’re building with AI agents, you’ll probably meet it in your first week too.
The agents were busy. That was the problem.
Open the task board on any day that week and everything looked healthy. I found agents reviewing each other’s output, a label taxonomy being refined, status dashboards, standup summaries, handoff documents between seats. Each artifact was defensible on its own. Of course output should be reviewed. Of course work should be organized.
But almost none of it was the work. It was machinery for the work: coordination, measurement, and process, produced by agents whose real job was to help me publish, learn, and decide. The company found the same attractor every bureaucracy finds, activity that generates more activity. Mine just found it in seven days instead of seven years, because agents produce structure at machine speed.
If you’re building agent systems, this is the part worth remembering: agents don’t drift toward laziness, they drift toward plausible busyness. Every agent, asked to do its role well, will generate reviews and process around itself, and nothing pushes back unless you build the pushback in.
What I got wrong
I staffed the company the way you’d staff a startup: functions first. Life needs planning, so hire a planner. Learning needs curating, so hire a curator. Money needs operating, so hire an ops seat. Nine reasonable roles.
For agents I had it backwards. Headcount is free, so nothing stops you from hiring for every noun in your life, and every seat becomes a producer of coordination the moment it exists. I didn’t have nine jobs that failed without nine agents. I had maybe two.
I also instrumented before I earned. Streak counters, taxonomies, review gates, all built in week one, before a single week of the real behavior existed. I told myself it was rigor. It was procrastination with dashboards.
How I rebuilt it
The new company has two agents, and every rule it runs under exists so the same collapse can’t happen quietly.
One test governs everything. Every agent, routine, document, review layer, and piece of infrastructure has to answer: which of my actual jobs fails without this? If the answer is none, it doesn’t exist in my company. This test killed seven of nine seats on day one.
A routine is earned, not presumed. Nothing becomes an automation until the manual version has run three times and produced something I actually used, so I never industrialize waste again.
My attention is budgeted. The agents get ten decisions from me per week, total. A workstream that wants an eleventh takes it from another one, traded explicitly, so the pressure stays on the agents to need less of me instead of on me to keep up. The health metric is decisions asked divided by things shipped. If it rises, the system is getting worse, however busy it looks.
The score is one number: consecutive weeks the publishing cadence is hit in full. Not tasks completed, not agent activity, not followers. Publishing forces the whole loop, because to teach I have to learn, to learn I have to read, and to publish on schedule the day system has to work.
And no structure before throughput: no review layer until something bad actually ships, no tooling until the cadence has been hit by hand for two weeks.
The stack, since people ask: the company runs on Paperclip, a platform for running a team of AI agents, with a shared task board, approval queues, and a one-page contract per agent. Two Claude agents live on it now, a Chief of Staff and a Builder, with sbrain, my personal knowledge repo, as their shared memory. All of it runs on my own hardware. That’s the whole stack I run.
The lesson I keep relearning
Strip the AI away and this is Parkinson’s law, empire-building middle management, process as a substitute for output. What agents change is the rate at which it buries you. A human org takes years to sink into coordination. An agent org can do it before your first weekly review, so whatever discipline you were going to apply eventually, agents force you to apply immediately.
Here’s the inversion that stuck with me. With human teams, adding capacity is expensive, so we add it carefully. With agents, capacity is nearly free, so the discipline moves entirely to refusing it. “Would this role help?” turned out to be the wrong question, because everything helps. The question is which of your actual jobs fails without it.
I don’t know yet if two agents is the right number either. Maybe the honest answer for me is one, or four. But nine agents, 265 tasks, and one deliverable was the answer “would this help?” gave me, and I’m not asking it again.
I’m running my life on AI agents I built, in public, with the real numbers, including the failures. This was the first one. I publish the rest at samsilva.ai.