📚 A curated collection of architecture patterns, templates, and learnings - Reflecting real-world experiences in evolving systems through observability, security, messaging, AI-human collaboration, and adaptive design patterns
This repository curates architectural patterns and templates drawn from real-world experiences, focusing on evolutionary principles and continuous learning. It reflects the journey of systems as they grow and adapt, offering pragmatic insights for teams navigating similar challenges.
Architecture is a journey of continuous learning and adaptation. This repository shares patterns and insights gathered from real experiences:
- Evolutionary Patterns: Learn from documented journeys of system evolution
- Practical Templates: Apply proven approaches adapted to your context
- Collaborative Workflows: Explore modern AI-human development paradigms
- Learning Principles: Understand the "why" behind architectural decisions
- Adaptive Strategies: Discover patterns that grow with your needs
| Guide | Description | Use When |
|---|---|---|
| Architecture Evolution | Journey from MVP to enterprise scale | Planning long-term architecture strategy |
| Observability Adoption | Building comprehensive system visibility | Improving distributed system debugging |
| Service Mesh Adoption | Managing microservices at scale | Service complexity needs platform solutions |
| WAF Adoption | Comprehensive security implementation | Building defense-in-depth strategies |
| BFF Pattern | Backend for Frontend patterns | Designing client-specific APIs |
| Guide | Description | Use When |
|---|---|---|
| AI-Human Collaboration Workflow | "Vibe Coding" methodology for AI-assisted development | Exploring modern development paradigms |
| Desktop Review Process | Structured review approach for AI-human teams | Ensuring quality in collaborative development |
pragmatic-architecture-patterns/
├── README.md # This file
├── guides/ # Comprehensive adoption guides
│ ├── ARCHITECTURE_EVOLUTION_GUIDE.md # MVP → Growth → Scale → Enterprise
│ ├── OBSERVABILITY_ADOPTION_GUIDE.md # OpenTelemetry & distributed tracing
│ ├── SERVICE_MESH_ADOPTION_GUIDE.md # Istio, Linkerd, service mesh patterns
│ ├── WAF_ADOPTION_GUIDE.md # Web Application Firewall adoption
│ ├── BFF_PATTERN_GUIDE.md # Backend for Frontend patterns
│ ├── AI_HUMAN_COLLABORATION_WORKFLOW_GUIDE.md # Modern development paradigms
│ └── AI_HUMAN_DESKTOP_REVIEW_GUIDE.md # Quality assurance in AI collaboration
├── patterns/ # Specific pattern implementations
│ ├── messaging-evolution/ # Kafka → Kinesis → EventBridge
│ ├── security-layers/ # Defense in depth strategies
│ └── data-architecture/ # Data layer evolution patterns
├── case-studies/ # Real-world migration stories
│ ├── kafka-to-eventbridge-migration.md
│ └── monolith-to-mesh-journey.md
└── templates/ # Reusable templates and tools
├── decision-matrix.md # Architecture decision templates
└── roi-calculator.xlsx # ROI calculation spreadsheets
- Continuous adaptation based on real experiences
- Iterative refinement of patterns and practices
- Knowledge sharing across teams and organizations
- Context-appropriate solutions over universal prescriptions
- Practical templates that can be adapted to your needs
- Real-world trade-offs documented and understood
- Human creativity combined with AI capabilities
- Shared learning between human and machine partners
- Quality through collaboration in modern development
Patterns organized by growth context:
- MVP Stage: Foundation and rapid iteration
- Growth Stage: Selective optimization and learning
- Scale Stage: Platform thinking and standardization
- Enterprise Stage: Governance and organizational alignment
- Cognitive load management in system design
- Organizational alignment (Conway's Law)
- Sustainable practices for long-term success
✅ This repository is for teams who:
- Value learning from real-world experiences
- Seek patterns that evolve with their systems
- Appreciate pragmatic approaches to complex challenges
- Want to explore modern development paradigms (including AI collaboration)
- Believe in continuous improvement and adaptation
- Need templates and patterns for various scales (10-1000+ services)
- Value both technical excellence and team sustainability
🎯 Particularly helpful for:
- Architects designing evolutionary systems
- Teams transitioning between growth stages
- Organizations exploring AI-human collaboration
- Engineers seeking practical pattern implementations
- Leaders planning technical transformations
This repository includes cutting-edge patterns for AI-human collaboration in software development, reflecting the emerging paradigm of "Vibe Coding" where humans provide vision and context while AI handles implementation details.
- SPECS-Driven Development: Comprehensive specification before implementation
- Desktop Review Process: Structured alignment between human and AI
- Test-Driven Development (TDD): AI-generated tests driving implementation
- Quality Assurance Workflows: Joint human-AI code review and validation
- Reduced Cognitive Load: Humans focus on strategy, AI handles syntax
- Comprehensive Documentation: Generated alongside code by default
- Consistent Quality: Enforced patterns and best practices
- Rapid Iteration: Faster development cycles with maintained quality
This repository organizes patterns around four evolutionary stages:
- Focus: Time to market
- Architecture: Monolith or simple services
- Team: 1-5 developers
- Patterns: Simple BFF, basic monitoring
- AI Collaboration: Rapid prototyping, initial documentation
- Focus: Selective optimization
- Architecture: Service separation beginning
- Team: 5-20 developers
- Patterns: API Gateway, distributed tracing
- AI Collaboration: Refactoring assistance, test generation
- Focus: Platform capabilities
- Architecture: Microservices proliferation
- Team: 20-100 developers
- Patterns: Service mesh consideration, comprehensive observability
- AI Collaboration: Complex system design, migration planning
- Focus: Governance and efficiency
- Architecture: Platform-based development
- Team: 100+ developers
- Patterns: Full service mesh, zero-trust security
- AI Collaboration: Architecture governance, compliance automation
- Assess your current stage using the evolution guides
- Identify pain points that justify architectural change
- Build ROI case using provided calculators and metrics
- Plan incremental migration using step-by-step guides
- Measure success with suggested KPIs
- Understand trade-offs at each evolution stage
- Plan team structure aligned with architecture
- Budget for transitions using cost analyses
- Set realistic timelines based on case studies
- Communicate strategy using provided frameworks
- Learn patterns appropriate for your current stage
- Understand the "why" behind architectural decisions
- Follow migration guides for systematic implementation
- Contribute learnings back to the community
- Before: 4-8 hours average debugging time
- After: 15-30 minutes with distributed tracing
- ROI: $910,000 annual savings in engineering time
- Before: 100+ services with inconsistent resilience patterns
- After: Platform-level reliability and observability
- ROI: 70% reduction in cascade failures
- Before: 44% endpoint coverage, multiple breaches
- After: 100% coverage, zero breaches
- ROI: $4.35M breach cost avoided
We welcome contributions from the community! Please see our Contributing Guidelines for details.
- Share your migration story as a case study
- Add new patterns you've successfully implemented
- Improve existing guides with your experience
- Submit ROI data from your implementations
- Report issues or suggest improvements
- "Building Evolutionary Architectures" by Neal Ford, Rebecca Parsons, Patrick Kua
- "Monolith to Microservices" by Sam Newman
- "Team Topologies" by Matthew Skelton and Manuel Pais
- Observability: OpenTelemetry, Jaeger, Zipkin, Sumo Logic, New Relic
- Service Mesh: Istio, Linkerd, Consul Connect, AWS App Mesh
- API Gateway: Kong, Traefik, AWS API Gateway, nginx
- Security: CloudFlare WAF, AWS WAF, ModSecurity
This repository is licensed under the MIT License - see the LICENSE file for details.
This repository is built from real-world experience evolving systems at:
- Fortune 500 enterprises
- High-growth SaaS companies
- Companies managing 100+ microservices
- Organizations serving millions of users globally
Special thanks to all the engineers, architects, and leaders who have contributed their experiences and lessons learned.
- Issues & Bug Reports: GitHub Issues
- Discussions & Ideas: GitHub Discussions
- Contributions: See Contributing Guidelines for how to contribute
Remember: Architecture is a continuous journey of learning and adaptation. The patterns and insights shared here are meant to inspire and guide, not prescribe. Every system, team, and context is unique - use these resources as a foundation for your own evolutionary journey.
"In the beginner's mind there are many possibilities, but in the expert's mind there are few." - Shunryu Suzuki
Start where you are. Use what you have. Do what you can. - Arthur Ashe