Graduated Implementation

Start with the smallest slice you can fully understand. Earn the next notch by proving you understood the last one. Ambition that outruns understanding is how a fluent diff becomes an unverifiable one.

Graduated Implementation

Overview

The sibling skill imbue:assisted-mastery fades scaffolding as competence grows. This skill ramps the other axis: the ambition of the next increment. They are the two directions of one move, the graduated practice that turned novices into experts long before agents existed. Not "ban the tool," but "couple the next challenge to demonstrated competence on the last one."

The learning sciences give the move a number. Wilson et al. (2019, Nature Communications 10:4646) derive the optimal training point for a learner at roughly 85% success: hard enough to learn from, not so hard that the signal is noise. The same band is what Vygotsky's zone of proximal development, Ericsson's edge of ability, and Csikszentmihalyi's flow channel all gesture at. Bloom's mastery learning (advance a unit at >=90% on a fresh check), Bayesian Knowledge Tracing (advance at p(mastery) >= 0.95), and competence-based curriculum learning (Platanios et al. 2019, only attempt tasks within the current competence) are the same rule at different resolutions.

The danger this guards against is specific. An agent that one-shots a large change is maximally helpful to throughput and quietly corrosive to verification: you cannot review what you did not watch get built, and automation bias means you will trust it precisely when it is wrong (Perry et al. 2023). Aviation named the endpoint "children of the magenta": ramp the operator's autonomy faster than their retained understanding and they can no longer hand-fly or override the automation when it misbehaves.

The Three Practices

1. Start at the smallest intentional increment