Picture a pipeline with five steps. An AI agent reads a request, three more research it in parallel, and a human signs off at the end. It runs fine for weeks. Then one night the process dies at step three.

To recover, the system needs to answer two questions. Where was I? Which steps already ran, what did they return, which ones must not run again. And where am I going? What comes after step three, which branch to take, who is still waiting for an approval.

A single LLM answer has a neat, confident shape. Public opinion does not.

When a policy changes, a product launches, or a company announces something unpopular, the interesting part is rarely the first reaction. It is what happens after people see each other reacting. Arguments harden. Coalitions form. A practical objection becomes the sentence everyone repeats. A weak point disappears because nobody picks it up.

That is hard to study with one prompt.

After a long string of 0.0.x releases, Phero finally reaches v1.0.0.

We made it to 1.0.0

There’s a particular feeling that comes with cutting a 1.0.0 tag. The 0.0.x versions are a workshop: you tear walls down, you move the staircase, you sleep on it and rebuild it the next morning. 1.0.0 is the moment you finally open the door and say: this is ready, and I stand behind it.

Now that anyone can build the prototype, the rarest skill is knowing which prototypes deserve to live.

In two previous articles I argued that AI does the easy 20% of software, writing code, and leaves the hard 80% untouched. The first traced the pattern back forty years, the second catalogued the 80% in nine parts a demo never shows. Both pieces did the same thing: they pointed a finger. At the PM who builds something in an afternoon and declares engineering obsolete. At the leap from “it works in the demo” to “we don’t need them anymore.”

A working demo is a promise the system hasn’t agreed to keep yet.

In a previous article I argued that writing code is, by Pareto, about 20% of the job, and that the other 80% is the part AI doesn’t replace. I listed that 80% as a string of bullet points and moved on. That was a cheat. Those bullet points are the whole argument, and they deserve more than a list.

Every builder reaches a moment when they start to suspect their own work.

Mine came while I was adding yet another example to phero, my Go framework for multi-agent AI systems. The example looked clean. The code was elegant. The abstractions composed nicely. But there was a question I kept circling: was any of this actually modular, or had I just written boilerplate that I was too close to see?