Requirements-as-Code

AI-native specification paradigm for product, engineering, and QA teams

Shipped as mycontext-ai v0.10.0 � June 2026 � LinkedIn Pulse series � Docs

The Problem

AI-assisted development has surfaced a structural gap: the tools that write and review code � Claude Code, Cursor, GitHub Copilot, Kiro � operate at the build layer, but the problem often lives upstream. Ambiguous requirements produce ambiguous implementations, and no amount of AI-assisted coding compensates for a spec that was never precise to begin with.

Traditional requirements artifacts � user stories, acceptance criteria, BRDs � were designed for human readers and manual review cycles. They are too informal for AI agents to act on reliably and too disconnected from code to serve as CI gates.

The Approach

Requirements-as-Code (RaC) treats specifications as code: authored in structured plain English, parsed into machine-checkable artifacts, versioned alongside the codebase, and validated in CI.

Three Spec Layers

Layer Output Consumer
Product spec Feature intent, user outcomes, acceptance criteria PMs, BAs, stakeholders
Technical spec Architecture decisions, interface contracts, data models Engineers, architects
Trace report Mapping of requirements ? implementation ? test coverage QA, compliance, auditors

How It Works

  1. Author � write requirements in structured plain English using the RaC schema (intent, scope, acceptance criteria, constraints)
  2. Parse � mycontext-ai parses the spec and generates the three-layer artifact suite
  3. Gate � CI checks that all traced requirements have corresponding implementation artifacts and passing tests before merge is allowed
  4. Audit � trace reports provide a continuous, machine-readable accountability chain from intent to deployment

The Positioning

RaC sits upstream of Claude Code, Cursor, and GitHub Spec Kit. Where those tools assist at the implementation layer, RaC governs the specification layer � ensuring that what gets built was precisely what was intended before a single line of code is written.

This addresses a widely recognized shift in the industry: from “add AI to existing automation” toward AI-native architecture, where the entire development lifecycle � from intent to deployment � is designed for AI-assisted execution.

Publication & Reception

A 7-part LinkedIn Pulse series launched June 2026 � “AI-Native Specifications for Product, Engineering, and QA Teams” � introduced RaC to practitioners. The series addresses product managers, business analysts, engineers, QA teams, and compliance practitioners and was recognized by AI practitioners as addressing a real and widely felt problem in the transition to AI-native development.

The series positions RaC alongside Claude Code, Cursor, GitHub Spec Kit, and Kiro as the upstream specification layer in the AI-native development stack.

Links