ai-first-engineering

Guide governance and architecture for AI-driven software teams.

Updated Apr 2, 2026

Search Tags:#architecture #ai #governance #code-review #deterministic-tests #team-workflows

Ready to run

npx skills add https://github.com/richardnpaul/everything-vscode-copilot --skill ai-first-engineering-richardnpaul

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: ai-first-engineering
Source: https://github.com/richardnpaul/everything-vscode-copilot/tree/main/.github/skills/ai-first-engineering
Command: npx skills add https://github.com/richardnpaul/everything-vscode-copilot --skill ai-first-engineering-richardnpaul

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Engineering operating model for teams where AI agents generate a large share of implementation output. This skill provides a framework for designing process flows, governance, and architecture to maintain quality and safety when AI-assisted development is the norm.

Core Features & Use Cases

Agent-friendly architecture with explicit boundaries, stable contracts, typed interfaces, and deterministic tests.
Structured code reviews focusing on behavior, security, data integrity, failure handling, and rollout safety.
Planning and evaluation signals to decompose ambiguous work, define measurable acceptance criteria, and enforce risk controls under delivery pressure.

Quick Start

Use the ai-first-engineering skill to establish governance and review practices for AI-driven development teams.

Frequently Asked Questions about ai-first-engineering

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I maintain code quality when AI agents generate most of the implementation?▼

To maintain AI-assisted code quality, you need an engineering operating model with explicit boundaries, stable contracts, and typed interfaces. Structured code reviews focusing on behavior, security, and failure handling enforce safety when AI generates substantial output.

What is agent-friendly architecture in AI-driven software development?▼

Agent-friendly architecture is a system design approach using explicit boundaries, stable contracts, typed interfaces, and deterministic tests. It ensures AI agents generate reliable output by aligning system design with safety and quality requirements across team workflows.

How do I structure code reviews for AI-generated code?▼

Structure code reviews for AI-generated code by focusing on behavior, security, data integrity, failure handling, and rollout safety. This disciplined review approach ensures AI-assisted development meets measurable acceptance criteria under delivery pressure.

How do I define measurable acceptance criteria for ambiguous AI development tasks?▼

Define measurable acceptance criteria for ambiguous tasks by using planning and evaluation signals to decompose work and enforce risk controls. This framework ensures AI-driven teams align output with safety and quality requirements during implementation.

Can I use this governance framework for design reviews and planning phases?▼

Yes, you can apply this governance framework to design reviews, planning, and implementation phases. It guides architecture and process flows for AI-driven software teams, ensuring quality and safety when AI agents generate substantial implementation output.

When do I need deterministic tests for AI-assisted development?▼

You need deterministic tests for AI-assisted development when AI agents generate a large share of implementation output. They enforce explicit boundaries and typed interfaces, ensuring reliability and safety within your team workflows.