Building Self-Validating Codebases: The Requirements System
One of the most critical challenges in AI-assisted development is ensuring that code changes maintain compliance with requirements, especially in regulated environments. How do you know an AI agent hasn't inadvertently broken a critical business rule while implementing a feature? How do you maintain traceability between requirements, code, and tests?
The answer lies in making requirements machine-readable, version-controlled, and automatically validated.
The Problem with Traditional Requirements
Traditional requirements management suffers from several fundamental problems:
Disconnection: Requirements live in separate documents (Word, Confluence, Jira) that become stale the moment code changes.
Manual Validation: Testing against requirements is a manual process prone to human error and interpretation differences.
Poor Traceability: When code changes, tracking which requirements are affected requires manual detective work.
Context Loss: AI agents can't understand requirements documents written in natural language prose.