"Nusquam est qui ubique est."
— Seneca, Epistulae Morales, II
He who is everywhere is nowhere. Seneca wrote this to his friend Lucilius about the danger of constant travel — the man who is always moving, always arriving somewhere new, never actually getting anywhere. He was not writing about AI. He could have been.
When I first started building with AI agents, I did what most technical founders do when they discover a powerful new tool: I tried to use it for everything simultaneously. Customer support agents. Research agents. Content agents. Outreach agents. Email triage. Social scheduling. Invoice processing. I deployed them all in the same quarter, which meant I defined none of them properly.
The agents were not the problem. My specifications were.
Each agent needed a clear output definition before it could do anything useful. What exactly should this agent produce? In what format? Under what conditions? By what standard do I know it has succeeded? These are not technical questions. They are clarity questions. And I had not answered any of them — I had simply pointed the technology at a vague direction and hoped it would find its own way.
"Elige ergo quem velis imitari... donec transferantur in te ista quae in admirando fuere."
— Seneca, Epistulae Morales, II — Choose whom you wish to imitate, until those qualities you admired pass into you.
Seneca's prescription for the scattered mind was not to do less — it was to choose one thing and go deep. Pick one author, read them until you understand them, then move to another. The discipline is sequential, not parallel. You cannot absorb ten writers at once any more than you can digest ten meals simultaneously.
This is what I now call OFA: the Output-First Architecture. Before an agent is deployed, you define its output with the same precision you would give a contractor you're paying by the hour. Not "handle customer support" — but: produce a JSON object containing a resolution category, a draft response, and a confidence score between 0 and 1, formatted as follows, triggered when the following conditions are met, escalated when confidence falls below 0.7.
That specificity is not bureaucracy. It is what allows the agent to actually function. An undefined output cannot be evaluated. An unevaluated output cannot be improved. An unimproved system is just expensive noise.
The early version of my setup had seven agents and zero clear outputs. The current version has three agents I use daily, each with a precise output definition, each measurably better than it was a month ago. I did not get there by building more. I got there by defining more precisely.
Seneca's warning to Lucilius was about the restless traveler who confuses motion with progress. The AI founder version of this person is the one with fourteen integrations, nine automations, and no clear answer to the question: what should this agent produce, exactly?
The agent is everywhere. Which means it is nowhere.
Start with one output. Define it completely. Deploy one agent to produce it. Evaluate the result. Improve the specification. Repeat. This is slower than deploying everything at once, and it produces actual results, which the alternative does not.
Seneca said this in 65 AD. I learned it expensively in 2025. The principle does not change.
Nusquam est qui ubique est. He who is everywhere is nowhere. Choose where you are.