ai-first-engineering

name	ai-first-engineering
description	Engineering operating model for teams where AI agents generate a large share of implementation output.
origin	Multiversal

AI-First Engineering

Use this skill when designing process, reviews, and architecture for teams shipping with AI-assisted code generation.

Process Shifts

1. Planning quality matters more than typing speed.
2. Eval coverage matters more than anecdotal confidence.
3. Review focus shifts from syntax to system behavior.

Architecture Requirements

Prefer architectures that are agent-friendly:

• explicit boundaries
• stable contracts
• typed interfaces
• deterministic tests

Avoid implicit behavior spread across hidden conventions.

Code Review in AI-First Teams

Review for:

• behavior regressions
• security assumptions
• data integrity
• failure handling
• rollout safety

Minimize time spent on style issues already covered by automation.

Hiring and Evaluation Signals

Strong AI-first engineers:

• decompose ambiguous work cleanly
• define measurable acceptance criteria
• produce high-signal prompts and evals
• enforce risk controls under delivery pressure

Testing Standard

Raise testing bar for generated code:

• required regression coverage for touched domains
• explicit edge-case assertions
• integration checks for interface boundaries