These principles define how AI-native engineering should work inside this framework.
Do not rely on clever prompts alone. Build repeatable systems with clear inputs, processes, outputs, review gates, and feedback loops.
Code generation is not the starting point. Architecture, domain modeling, constraints, and contracts must guide implementation.
The quality of AI execution depends on structured context, not just larger context windows.
Memory is useful for history. Knowledge is the source of truth.
Product facts, domain rules, architecture decisions, and technical references should live in explicit knowledge artifacts.
Agents should not invent systems from vague prompts. They should execute from product blueprints and engineering contracts.
Rules define constraints that protect consistency, security, maintainability, and product quality.
A skill is a reusable execution procedure. Agents should use skills when performing repeatable engineering work.
Different responsibilities should be separated across agent roles: product, planning, architecture, building, testing, review, security, and documentation.
Every generated output must be evaluated against quality gates before being accepted.
The default mode is human-in-the-loop. Fully automated publishing, deployment, or destructive operations require explicit approval.
The framework prioritizes systems that can be maintained, reviewed, extended, and reused.
Every useful workflow should be designed so it can become a reusable asset, internal tool, automation template, digital product, or SaaS feature.