Phase 9: Team Workflows & AI-Native Process · 45 min · Claude Code · GitHub · Python

The AI-Native Team Operating Model

Once agents write most of the code, the highest-leverage review moment isn't the diff anymore — it's the spec and the harness that produced it.

Real-world application: Naming spec owner, harness maintainer, and eval owner as real team roles — not just 'everyone reviews everything' — is the organizational vocabulary that shows you've thought about how a team actually operates at AI-native scale, not just how one engineer does.

What you will learn

Name the three new team roles an AI-native operating model requires: spec owner, harness maintainer, eval owner
Explain why the primary review activity shifts from code review to spec and harness review
Describe the three daily rituals of an AI-native team: spec review, eval review, harness regression check
Route a real PR's changes to the correct owner role based on what actually changed

Introduction

The AI-Native Team Operating Model

Objective

Classify a PR's changed files into ownership categories and aggregate which owner role(s) and daily ritual(s) the PR requires.

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Problem, The Concept, Build, Check Yourself, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.

Create a free account — the opening phases of 29 of 35 courses are free, no credit card.