Production-ready framework for governing AI agent behavior in software development. Enforces consistency, quality, and compliance across AI-generated code with plan gating, self-healing, and drift detection.
What This Proves
I think about AI adoption at enterprise scale
I build tools that govern AI, not just use it
I understand compliance and auditability requirements
I create frameworks others can adopt
The Problem
AI coding tools generate inconsistent code across a team. Without governance, you get drift in patterns, violations of standards, and no auditability. Enterprises can't adopt AI coding at scale without controls.
The Approach
Created a governance framework that wraps AI coding tools with "constitutional" rules. Every AI-generated change goes through plan gating (is this change appropriate?), validation (does it meet standards?), and evolution tracking (what patterns are emerging?). The framework learns from the codebase and gets smarter over time.
Architecture Decisions
Plan gating system (MVP → Surgical → Systemic)
Self-healing blueprint engine with drift detection
Evolution story tracking (learns from patterns)
Democratic amendment system (version governance)
Cross-framework learning engine
MCP (Model Context Protocol) server integration
Why I Built It This Way
Built as a governance layer, not a replacement for AI tools
Implemented "constitutional" approach inspired by Anthropic
Designed for teams adopting AI-assisted development
Made framework-agnostic to work with any AI coding tool
Technology Stack
TypeScript Bun Vite Playwright Vitest CLI Docker
Outcomes
Constitutional governance for AI code generation
Automated quality enforcement
Multi-agent coordination patterns
Reproducible blueprint system
Team adoption playbook
Started: 2024-08
6 months to v2.5