Transform AI-assisted development from "smart autocomplete" into systematic engineering. This workflow produces AI-generated code that actually works in production.

A battle-tested multi-agent workflow system that orchestrates specialized AI agents to build production-grade software. Instead of relying on a single AI assistant that does everything poorly, this workflow deploys the right AI model for each specific task—like having a senior development team where each member excels at their specialty.

Built for: Development teams who need AI-generated code that actually works in production, not just demos.

• This README: Overview, benefits, and competitive comparison
• process.md: See the workflow in action with real examples and multi-agent coordination
• backlog.md: Public roadmap with MVP plans and future development phases

• Production Applications: Where reliability matters more than prototype speed
• Complex Systems: Multi-component architectures with intricate dependencies
• Security-Critical Projects: Handling user data or requiring compliance
• Long-term Codebases: Projects that must remain maintainable over months/years
• Team Development: Multiple contributors (human or AI) working on the same codebase

• Quick prototypes or throwaway code
• Single-file scripts or simple utilities
• Projects with no quality requirements
• Solo experiments or learning exercises

This system transforms AI development using four core principles:

Each AI model handles what it does best:

• System Architect (Claude): Strategic planning and coordination
• Security Reviewer (Qwen Code): Security analysis and architecture validation
• Context Specialist (Gemini CLI): Deep codebase analysis and pattern discovery
• Feature Developer (Claude): Implementation and testing
• Documentation Writer (Claude): Knowledge transfer and maintenance

Long-Term Memory: Stable architectural principles in documentation
Short-Term Memory: Live codebase accessed through structured analysis
Result: AI agents reason like senior engineers, validating every change against established principles

• Real API Testing: No mocking allowed—ensures production readiness
• Security-First: Mandatory reviews for all critical changes
• Architectural Patterns: Enforced consistency across the entire codebase
• Code-First Documentation: Documentation that evolves with implementation

Explicit handoff protocols, dependency management, and quality gates ensure nothing falls through the cracks.

Think of it like a senior development team where each member has a specialized role:

sequenceDiagram
    participant User
    participant SystemArchitect
    participant FeatureDeveloper
    participant SecurityReviewer
    participant DocsWriter

    User->>SystemArchitect: "New Feature Request"
    SystemArchitect-->>User: "Strategic Plan Created"

    User->>FeatureDeveloper: "Implement According to Plan"
    FeatureDeveloper->>FeatureDeveloper: "Write Code & Tests"
    
    FeatureDeveloper->>SecurityReviewer: "Quality & Security Review"
    activate SecurityReviewer

    alt Review Passes
        SecurityReviewer-->>FeatureDeveloper: "Approved"
    else Critical Issues Found
        SecurityReviewer-->>FeatureDeveloper: "Fix Required"
        FeatureDeveloper->>FeatureDeveloper: "Address Issues"
        FeatureDeveloper->>SecurityReviewer: "Re-submit for Review"
        SecurityReviewer-->>FeatureDeveloper: "Final Approval"
    end
    
    deactivate SecurityReviewer

    FeatureDeveloper->>DocsWriter: "Update Documentation"
    DocsWriter-->>User: "Feature Complete"

Want to see how this multi-agent coordination works on real tasks? Check out process.md for detailed examples including:

• Complete workflow walkthrough: How we created our public roadmap using systematic multi-agent coordination
• Multi-agent orchestration: Claude + Gemini CLI + Qwen Code working together on complex security implementation
• Two-tier memory in practice: Real examples of long-term + short-term memory synthesis
• Quality gates demonstration: Actual validation checkpoints and decision flows

Unlike traditional AI coding where quality is hit-or-miss, our workflow enforces systematic quality through:

flowchart TD
    A[Code Complete] --> B{Security Critical?}
    B -->|Yes| C[🔒 Security Review Required]
    B -->|No| D[📋 Standard Testing]
    C --> E[🧪 Real API Testing]
    D --> E
    E --> F{All Gates Pass?}
    F -->|No| G[🔄 Return for Fixes]
    F -->|Yes| H[✅ Production Ready]
    G --> A

• Real API Testing: No mocking—if it doesn't work with real APIs, it doesn't ship
• Security-First: Every critical path reviewed by specialized security agent
• Code-First Documentation: Documentation automatically validated against actual implementation
• Semantic Landmarks: Documentation that can't become outdated because it's tied to searchable code patterns

❌ Context Overload: One agent trying to handle planning, implementation, security, and documentation
❌ No Quality Control: No systematic validation of AI-generated code
❌ Architectural Drift: Each session starts fresh with no institutional memory
❌ Security Blind Spots: Generalist agents miss security implications
❌ Documentation Rot: Documentation becomes stale and misleading

✅ Specialized Expertise: Right AI model for each specific task
✅ Built-in Quality Gates: Systematic validation at every step
✅ Institutional Memory: Formal architectural principles preserved across sessions
✅ Security-First: Dedicated security specialist reviews all critical paths
✅ Living Documentation: Code-first approach ensures documentation stays current

This workflow is perfect for teams who:

• ✅ Build production applications (not just prototypes)
• ✅ Need consistent code quality across AI-generated code
• ✅ Work on complex systems with multiple components
• ✅ Require security compliance or handle sensitive data
• ✅ Want AI assistance that enhances rather than replaces engineering discipline

See our complete Public Roadmap for detailed implementation strategy:

• 🎯 MVP: 5-minute manual setup with full workflow demonstration
• ⚡ Phase 2: Automated CLI tools and validation scripts
• 🚀 Phase 3: Community plugins and enterprise migration tools

Next Up: Template repository with agent personas, quality gate examples, and step-by-step tutorials.