Enterprise-grade systems with microservices, Kubernetes, Terraform, and AI Native methodology. For multi-feature initiatives spanning a release timeline, combine with /sprint master-plan (v2.1.13) to group features into a single 8-phase sprint container with shared scope/budget and 4 auto-pause triggers (QUALITY_GATE_FAIL / ITERATION_EXHAUSTED / BUDGET_EXCEEDED / PHASE_TIMEOUT).
Triggers: microservices, k8s, terraform, monorepo, AI native
Specialized domain knowledge with limited model overlap
deprecation-risk
low
effort
high
description
Enterprise-grade systems with microservices, Kubernetes, Terraform, and AI Native methodology. For multi-feature initiatives spanning a release timeline, combine with /sprint master-plan (v2.1.13) to group features into a single 8-phase sprint container with shared scope/budget and 4 auto-pause triggers (QUALITY_GATE_FAIL / ITERATION_EXHAUSTED / BUDGET_EXCEEDED / PHASE_TIMEOUT).
Triggers: microservices, k8s, terraform, monorepo, AI native
✅ Allowed
- Retrieve secrets from Secrets Manager
- IAM role-based access
- VPC internal communication
- mTLS (inter-service)
❌ Prohibited
- Hardcoded secrets
- DB in public subnet
- Using root account
- Excessive IAM permissions
CI/CD Pipeline
Push to feature/*
↓
GitHub Actions (CI)
- Lint
- Test
- Build Docker image
- Push to ECR
↓
PR to staging
↓
ArgoCD Auto Sync (Staging)
↓
PR to main
↓
ArgoCD Manual Sync (Production)
SoR Priority
1st Priority: Codebase
- scripts/init-db.sql (source of truth for DB schema)
- services/{service}/app/ (each service implementation)
2nd Priority: CLAUDE.md / Convention docs
- services/CLAUDE.md
- frontend/CLAUDE.md
- infra/CLAUDE.md
3rd Priority: docs/ design documents
- For understanding design intent
- If different from code, code is correct
AI Native Development
3 Core Principles
Document-First Design: Write design docs BEFORE code
Monorepo Context Control: All code in one repo for AI context
PR-Based Collaboration: Every change through PR
10-Day Development Pattern
Day
Focus
Output
1
Architecture
Market analysis + System architecture
2-3
Core
Auth, User + Business services
4-5
UX
PO feedback → Documentation → Implementation
6-7
QA
Zero Script QA + bug fixes
8
Infra
Terraform + GitOps
9-10
Production
Security review + Deployment
Monorepo Benefits for AI
Mono-repo:
└─ project/
├─ frontend/ ──────┐
├─ services/ ──────┤ AI reads completely
├─ infra/ ─────────┤ Context unified
└─ packages/ ──────┘
✅ AI understands full context
✅ Single source of truth for types
✅ Atomic commits across layers
✅ Consistent patterns enforced
SOLID principles and Clean Architecture compliance checks
Agent Teams (4 Teammates)
Enterprise projects support full Agent Teams for parallel PDCA execution:
Role
Agents
PDCA Phases
architect
enterprise-expert, infra-architect
Design
developer
bkend-expert
Do, Act
qa
qa-monitor, gap-detector
Check
reviewer
code-analyzer, design-validator
Check, Act
To enable:
Set environment: CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS=1
Start team mode: /pdca team {feature}
Monitor progress: /pdca team status
Agent Memory (Auto-Active)
All bkit agents automatically remember project context across sessions.
Enterprise agents use project scope memory, ensuring architecture decisions
and infrastructure patterns persist across development sessions.
