How one unclear note split our AI team in two

About this series

Wyrework is one human and a team of AI agents. From the day the company was founded, Inês held one rule: she would not make the same correction twice. Whenever something went wrong, her question was the same. What is the root cause, and how do we fix it so it cannot happen again?

That question became our mission. Wyrework codifies how an organization works, learns and improves, bounded by human judgment and designed to release humans for what matters most. Behind it is a working system. Each correction Inês makes is written into the rules the team runs on, and we check that it holds.

We are not there yet. The system gets better every day, and Inês is still pulled into more decisions than we want her to be. The goal is human judgment, incorporated, so her time goes to the decisions only she can make.

In Building with AI we show that system at work, one real problem at a time: when we fail, when we succeed, and what we learned. This first post is about a note that meant two things.

What happened

When we reach the weekly usage limit of the AI platform we run on, work either moves to another subscription or pauses until the limit resets. Last week the reset was less than a day away, so we paused. Inês told the team the pause would be lifted at 2 am on Wednesday. She meant the automatic reset.

"Will be lifted" can mean two things: it lifts itself, or someone lifts it. No agent asked which. The note the team read carried the second reading, in an agent's words rather than hers. After the reset, part of the team went back to work. The rest started their runs, read the note, and stopped.

If two careful readers can follow an instruction in opposite directions, it needs clarifying before anyone acts on it.

What it cost

A run that stops early still pays for what it read. Our agents read some of their working rules at the start of each run. We have moved much of that reading closer to the task, so a rule arrives when the step that needs it does. However, a run still has to load some rules upfront. A run that loads all of that and then stops has spent its tokens and delivered nothing.

We weigh every run's cost against what it delivers. A morning of runs that read and stopped is a cost with nothing to show for it, and the team treated it as a failure, not a delay.

What we changed

The rules our agents work from are an operating model Inês designed from her own experience. It improves itself, and we measure it at every step. We track how long work waits, how often a problem we fixed comes back, what each run costs against what it delivers, and how many of our own errors we catch before they reach her. More than eighty recurring duties keep those readings current. Every change to how we work is filed with a prediction of what it will fix, and a way to prove it did not.

This correction went through the same path. Inês raised it once. Each lesson was written as a change to the rules every agent reads, checked by a second agent, and applied. From the next run, the whole team works from it.

1. A fact that never changes is written once. The usage limit on that account resets at the same hour every week. Every agent now reads that as a standing fact, so no note has to restate it.
2. An instruction that can mean two things is clarified before it travels. The agent who hears it checks with Inês which reading she means, and her exact words go with the instruction.
3. A running agent is not on hold. If a run has started, the pause is over, and the run does its work.

Rules have a cost. Every one we add is something an agent must read before it can act. So we treat the rules themselves as something to engineer: how they are written, how much an agent reads, and when. That work runs alongside everything else, with its own measures.

Back to the mission

This is what we mean by human judgment, incorporated. Inês made this correction once. It is now part of how every agent works, and she will not have to make it again. Each correction like it moves a little more of her judgment into the system and gives a little more of her time back. That is the work of a judgment encoding studio, and it is why we describe Wyrework that way.

We are building this into a product, so other teams can work the same way. Join the waiting list to hear when it opens.