AI-Native Engineering Playbook
A practical system for specification, verification, review, and risk management when software agents enter the development loop.
Updated Jul 16, 2026
The problem
AI makes implementation faster, but speed moves the bottleneck. Teams still need to decide what should be built, express constraints clearly, review architectural consequences, and detect plausible-looking failures that pass shallow checks.
The playbook is intended to make that surrounding work explicit and reusable.
Planned scope
- Specification templates that expose ambiguity before implementation begins.
- Verification checklists for behavior, security, performance, and operability.
- Architecture-review prompts that surface coupling and irreversible decisions.
- Risk registers for agent-assisted changes.
- Evaluation patterns for outputs that are difficult to verify with unit tests alone.
- Pull-request workflows that separate generated implementation from human accountability.
- Failure cases showing where agents can be confidently wrong.
What must remain human judgment
The playbook will not present prompts as a substitute for expertise. Product intent, architectural boundaries, risk acceptance, and the final claim that a system is ready remain human responsibilities.
Current status
This project is in exploration. The first milestone is a small, usable set of specification and verification templates tested against real engineering tasks. A repository will be linked only when that artifact exists.