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Engineering Principles

Quick reference for core engineering principles. For detailed standards, see standards/.


Core Tenets

  • Clarity over cleverness: Optimize for readability and explicitness
  • Small, safe changes: Incremental delivery with tests and feature flags
  • Separation of concerns: Isolate domains, interfaces, and implementations
  • Defensive boundaries: Validate inputs at edges; trust internal invariants
  • Observability-first: Logs, metrics, traces are first-class citizens
  • Consistency across teams: Shared practices, vocabulary, and decision-making
  • Data-driven decisions: Objective criteria, ADRs, and metrics over opinions
  • Quality & security are everyone's responsibility: Shift-left; built in, not bolted on
  • Living documentation: Auto-generated from code; manually document only ADRs and context
  • Continuous learning: Retrospectives, post-mortems, evolving standards

Cloud-Native Principles

  1. Composable Architecture & DDD - Bounded contexts, ubiquitous language
  2. API-First & Contract-Driven - Independent services with clear contracts
  3. Cloud Portability - Avoid vendor lock-in, use open standards
  4. Observability - Tracing, logging, metrics, alerting
  5. Zero Trust Security - Authenticate, authorize, encrypt all flows
  6. Performance Optimization - Caching, compression, efficient algorithms
  7. High Availability - Redundancy, failover, graceful degradation
  8. Reliability & Resilience - Fault tolerance, chaos engineering
  9. Cost Optimization - Right-sizing, auto-scaling, FinOps
  10. Automation First - CI/CD, automated testing, security scanning
  11. Infrastructure as Code - Version-controlled, repeatable deployments
  12. AI-Native Design - Prompt-driven interfaces, RAG over fine-tuning, agentic orchestration, evaluation-as-code, and human-in-the-loop guardrails from inception
  13. Open Source & Open Standards - Prefer OAUTH, OpenTelemetry, Kubernetes over proprietary

Tradeoffs

Decision Guidance
Performance vs Maintainability Prefer maintainable; optimize proven hotspots
Consistency vs Local Optimization Favor org-wide conventions
Abstraction vs Simplicity Abstract only repeated patterns (Rule of Three)

Technology Selection

  • Open standards preferred
  • Libraries with zero critical/high vulnerabilities
  • Active maintenance and community support
  • Clear licensing compatible with commercial use
  • Performance characteristics matching requirements
  • LLM frameworks with structured output, tool use, and observability (e.g., LangChain, Claude SDK, OpenAI SDK)
  • Evaluation and guardrail tooling for prompt regression testing and safety

Detailed Standards

Document Content
principles.md Full principles with rationale
patterns.md Architecture patterns
tech-stack.md Technology stack standards
devops.md CI/CD and DevOps
metrics.md Observability and metrics
checklists.md Implementation checklists
decision-framework.md Decision framework
llm-design.md LLM-native design standards
glossary.md Glossary of terms