this portfolio
Pioneering Agent-Driven Development
Challenge
The question wasn't "Can I build a portfolio faster with AI?" but "How do spec-driven, agent-orchestrated workflows actually work in practice?" Traditional "AI coding assistants" help write individual functions but lack the architectural understanding needed for full applications. The industry is shifting toward agent-driven development, but practical implementations remain rare, creating a knowledge gap for developers wanting to adopt these emerging practices professionally.
Solution
BMAD (Breakthrough Method for Agile AI-Driven Development) transforms chaotic "prompt engineering" into structured, spec-driven workflows. Instead of throwing prompts at AI, the framework uses specialized agents working from comprehensive documentation. I orchestrated 6 specialized agents: UX Expert, Design Thinking, Research, Development, QA, and Brainstorming—plus a custom UI/UX Agent built specifically for this project.
The key innovation: comprehensive documentation provides agents with full context. Created 63+ markdown files (PRDs, technical specs, user stories, architecture docs, design system) that agents access during execution. This architectural approach eliminates planning inconsistency (specs ensure alignment) and context loss (documentation gives agents full project understanding). Sequential workflow: Brainstorming → Design Thinking → Architecture Planning → Specs/PRDs → Development → QA Review.
This Portfolio: Agent-Driven Development with BMAD-Method
Pioneering Spec-Driven AI Agent Orchestration for Production Applications
This portfolio was built entirely using AI agents orchestrated through the BMAD-Method framework—a deliberate exploration of emerging agentic development practices. As a progressive developer based in Berlin, I recognized AI agent development as a highly relevant technology evolution and chose to learn by doing: building a production-grade portfolio application while mastering agent-driven workflows.
The BMAD-Method Framework: Structured AI Development
BMAD (Breakthrough Method for Agile AI-Driven Development) transforms chaotic "prompt engineering" into structured, spec-driven workflows. Instead of throwing prompts at AI and hoping for good code, the framework uses specialized agents working from comprehensive documentation—ensuring consistency, quality, and architectural integrity across complex applications.
Agent Orchestration Architecture
I orchestrated 6 specialized AI agents in a sequential workflow:
- - UX Expert Agent - User experience architecture and design patterns
- Design Thinking Agent - User-centered design methodology
- Research Agent - Technology evaluation and best practices research
- Development Agent - Implementation from detailed specifications
- QA Agent - Quality assurance, code review, and testing
- Brainstorming Agent - Concept exploration and ideation
Additionally, I built a custom UI/UX Agent specifically for this project's unique design system, demonstrating the flexibility to extend the BMAD-Method framework for domain-specific needs.
The Documentation Breakthrough: Specs as Infrastructure
The key innovation: comprehensive documentation provides agents with full context. I created 63+ markdown files (PRDs, technical specs, user stories, architecture docs, design system documentation) that agents access during execution. This architectural approach eliminates the two biggest problems with AI-assisted development:
1. Planning inconsistency - Detailed specs ensure alignment across all agents 2. Context loss - Comprehensive documentation gives agents full project understanding
Technical Implementation: Next.js 15 with Modern Stack
Technology Stack: - Frontend: Next.js 15, React 19, TypeScript, Tailwind CSS v4 - 3D/Animation: Three.js, Framer Motion for interactive experiences - Development Methodology: BMAD-Method framework with custom agent workflows - Documentation: 63+ specification files ensuring agent context - Quality Assurance: Agent-driven QA + manual validation
Development Metrics: - Development Time: ~16 hours (agent-orchestrated) - Manual Code Written: 0 lines - Quality Scores: Lighthouse Performance & Accessibility > 95 - Documentation: 63+ markdown files (architecture, specs, stories) - Agent Count: 6 specialized agents + 1 custom agent
What This Proves: The Future of Development
It's possible to develop comprehensive applications with custom UI without writing code manually—but only with proper architecture. The BMAD-Method succeeds because it emphasizes specifications and documentation, not because the AI is magic. Agents are only as good as the context they receive.
Key Insights from Agent-Driven Development: - Specs are infrastructure - Detailed documentation enables agent autonomy - Agent orchestration is architecture - Sequential workflows with clear handoffs work better than parallel chaos - Quality control still requires humans - Agents execute, developers review and validate - Custom agents extend capabilities - Building domain-specific agents amplifies results
Why This Matters for Forward-Thinking Companies
This project demonstrates not just technical capability but progressive thinking about development's evolution. Instead of writing every line of code, developers become system architects, specification writers, and agent orchestrators. This portfolio is my practical answer to "How do developers leverage AI agents to build better systems faster?"
Technologies & Skills Demonstrated: AI agent orchestration, BMAD-Method framework, spec-driven development, Next.js 15, React 19, TypeScript, Three.js, documentation architecture, quality assurance, progressive development methodologies, Berlin-based developer
Location: Berlin, Germany | Timeline: 2025 | Role: Developer & Agent Orchestrator
Screenshots
Frontend
Tools & Services
Methodology
AI Agents
Impact
Production-grade portfolio developed in ~16 hours of agent-orchestrated time, 0 manual code lines written. Lighthouse Performance & Accessibility scores > 95. 63+ specification files serving as documentation infrastructure. Proves spec-driven agent orchestration works for real production applications—not just demos. Built with Next.js 15, React 19, TypeScript, Tailwind CSS v4, Three.js, Framer Motion.
Key Learnings
- Specs are infrastructure: The 63+ specification files transformed from "nice-to-have" into critical infrastructure enabling agents to maintain context and consistency
- Agent orchestration is architecture: Sequential workflows with clear handoffs (UX → Dev → QA) outperform parallel chaos
- Quality control still requires humans: Agents execute specifications accurately, but the role shifts to writing specs and validating output