AI-Native Ways of Working
As AI absorbs the mechanical work of building software, what's left is the part that still has to be human: the judgment. This is my working canon for that shift — what I believe, how I work, and the practices I keep testing on my own systems before I'd ask anyone else to trust them. It changes as the evidence does.
Someone has to go first. Then mark the route.
The three shifts
not model magic
The four values
There's value on both sides. When they pull against each other, choose the left.
The seven principles
The depth behind the card — how the shifts and values get practiced.
- Shift the human up the loop — doing, reviewing, coaching, setting intent — as trust is earned.
- Gate only the irreversible and the high-blast-radius; let the rest flow.
- Give every agent a role, constraints, and an escalation path.
- Pair workers with checkers — reliability comes from checking.
- Make delivery-state visible; pay down comprehension debt; never rubber-stamp.
- Preserve discovery — put back, on purpose, the learning AI skips.
- Adopt AI as change management, on an owned and durable substrate, matched to your risk.
The practice areas
Seventeen recurring problems of human-agent teams — the field guide's table of contents. The four marked ◆ FOUNDATION run underneath all three shifts above. Each area becomes an essay as the evidence comes in, tested here first.
The Companion Instrument
AI-Native Engineering Maturity Model
Where your organization stands across ten dimensions, and what to move next. Free, no gate.
Take the assessment →Created and maintained by Ed Schaefer — executive coach & strategic advisor.
Host of the Leadership Explored podcast.