DevOps AI Toolkit

# PRD: Application Lifecycle Management and Operations System **Created**: 2025-07-28 **Status**: Draft **Owner**: Viktor Farcic **Last Updated**: 2025-07-28 ## Executive Summary Build a comprehensive application lifecycle management system that handles post-deployment operations including updates, scaling, configuration changes, troubleshooting, and deletion for applications deployed via dot-ai. ## Documentation Changes ### Files Created/Updated - **`docs/lifecycle-management-guide.md`** - New File - Complete guide for application lifecycle operations - **`docs/scaling-guide.md`** - New File - Application scaling strategies and automation - **`docs/lifecycle-management-guide.md`** - User Documentation - Add application lifecycle management operations - **`README.md`** - Project Overview - Add lifecycle management to core capabilities - **`src/core/lifecycle/`** - Technical Implementation - Lifecycle management system modules ### Content Location Map - **Feature Overview**: See `docs/lifecycle-management-guide.md` (Section: "What is Lifecycle Management") - **Update Operations**: See `docs/lifecycle-management-guide.md` (Section: "Updates and Rollbacks") - **Scaling Operations**: See `docs/scaling-guide.md` (Section: "Scaling Strategies") - **Setup Instructions**: See `docs/lifecycle-management-guide.md` (Section: "Configuration") - **MCP Operations**: See `docs/lifecycle-management-guide.md` (Section: "Lifecycle Operations") - **Examples**: See `docs/lifecycle-management-guide.md` (Section: "Usage Examples") - **Troubleshooting**: See `docs/lifecycle-management-guide.md` (Section: "Common Issues") - **Lifecycle Index**: See `README.md` (Section: "Application Lifecycle") ### User Journey Validation - [ ] **Primary workflow** documented end-to-end: Deploy app → Update → Scale → Troubleshoot → Manage lifecycle - [ ] **Secondary workflows** have complete coverage: Configuration management, backup/recovery, deletion procedures - [ ] **Cross-references** between deployment docs and lifecycle management docs work correctly - [ ] **Examples and commands** are testable via automated validation ## Implementation Requirements - [ ] **Core functionality**: Rolling updates and zero-downtime deployments - Documented in `docs/lifecycle-management-guide.md` (Section: "Update Strategies") - [ ] **User workflows**: Scaling operations with manual and automated options - Documented in `docs/scaling-guide.md` (Section: "Scaling Workflows") - [ ] **MCP Operations**: Configuration management and troubleshooting tools - Documented in `docs/lifecycle-management-guide.md` (Section: "Lifecycle Operations") - [ ] **Error handling**: Graceful handling of update failures and rollback scenarios - Documented in `docs/lifecycle-management-guide.md` (Section: "Error Recovery") - [ ] **Performance optimization**: Zero-downtime updates for 99% of deployments ### Documentation Quality Requirements - [ ] **All examples work**: Automated testing validates all lifecycle commands and update procedures - [ ] **Complete user journeys**: End-to-end workflows from deployment through retirement documented - [ ] **Consistent terminology**: Same lifecycle terms used across CLI reference, user guide, and README - [ ] **Working cross-references**: All internal links between lifecycle docs and core docs resolve correctly ### Success Criteria - [ ] **Update success**: Zero-downtime updates for 99% of application deployments - [ ] **Scaling responsiveness**: Automated scaling response within 30 seconds of demand changes - [ ] **Operational efficiency**: 80% reduction in manual operational tasks - [ ] **Recovery time**: Mean Time To Recovery (MTTR) under 15 minutes for common issues - [ ] **Cleanup success**: 100% successful cleanup operations with no resource leaks ## Implementation Progress ### Phase 1: Core Update and Scaling Operations [Status: ⏳ PENDING] **Target**: Basic update, rollback, and scaling capabilities working **Documentation Changes:** - [ ] **`docs/lifecycle-management-guide.md`**: Create complete user guide with update and rollback concepts - [ ] **`docs/scaling-guide.md`**: Create comprehensive scaling guide with manual and automated strategies - [ ] **`docs/lifecycle-management-guide.md`**: Add update, rollback, scale, and basic lifecycle operations - [ ] **`README.md`**: Update capabilities section to mention application lifecycle management **Implementation Tasks:** - [ ] Implement rolling updates with zero-downtime deployment strategies - [ ] Create manual scaling operations (horizontal and vertical) - [ ] Build simple configuration management for environment-specific updates - [ ] Add safe deletion procedures with dependency checking ### Phase 2: Automation and Intelligence [Status: ⏳ PENDING] **Target**: Automated scaling, AI-powered troubleshooting, and advanced update strategies **Documentation Changes:** - [ ] **`docs/scaling-guide.md`**: Add "Automated Scaling" section with HPA/VPA integration - [ ] **`docs/lifecycle-management-guide.md`**: Add "AI-Powered Operations" section with intelligent recommendations - [ ] **`docs/troubleshooting-guide.md`**: Add lifecycle-specific troubleshooting workflows **Implementation Tasks:** - [ ] Implement automated scaling based on metrics (HPA, VPA, custom metrics) - [ ] Create AI-powered troubleshooting assistance with Claude integration - [ ] Add advanced update strategies (blue-green, canary deployments) - [ ] Build predictive operations recommendations using historical data ### Phase 3: Advanced Lifecycle Management [Status: ⏳ PENDING] **Target**: Full GitOps integration, multi-cluster operations, and advanced governance **Documentation Changes:** - [ ] **`docs/lifecycle-management-guide.md`**: Add "Advanced Features" section with GitOps and multi-cluster operations - [ ] **Cross-file validation**: Ensure lifecycle management integrates seamlessly with all deployment docs **Implementation Tasks:** - [ ] Add full GitOps integration with ArgoCD/Flux workflows - [ ] Implement multi-cluster operations and lifecycle management - [ ] Create advanced disaster recovery and backup/restore capabilities - [ ] Build compliance and governance automation ## Technical Implementation Checklist ### Architecture & Design - [ ] Design update strategies with rolling, blue-green, and canary deployment patterns (src/core/lifecycle/update-strategies.ts) - [ ] Implement scaling intelligence with demand prediction algorithms (src/core/lifecycle/scaling-engine.ts) - [ ] Create configuration management system with versioning and validation (src/core/lifecycle/config-manager.ts) - [ ] Design operations automation with runbook integration (src/core/lifecycle/operations-automation.ts) - [ ] Plan integration with existing dot-ai deployment and monitoring systems - [ ] Document lifecycle management architecture and workflows ### Development Tasks - [ ] Build comprehensive update system with rollback capabilities - [ ] Implement intelligent scaling with cost optimization algorithms - [ ] Create configuration management with policy-based validation - [ ] Add operations automation with self-healing capabilities - [ ] Build safe deletion system with resource cleanup validation ### Documentation Validation - [ ] **Automated testing**: All lifecycle commands and update procedures execute successfully - [ ] **Cross-file consistency**: Deployment docs integrate seamlessly with lifecycle management features - [ ] **User journey testing**: Complete lifecycle workflows can be followed end-to-end - [ ] **Link validation**: All internal references between lifecycle docs and core documentation resolve correctly ### Quality Assurance - [ ] Unit tests for update strategies with various deployment scenarios - [ ] Integration tests with Kubernetes APIs for scaling and configuration management - [ ] Performance tests ensuring zero-downtime updates and fast scaling - [ ] Disaster recovery testing with backup/restore validation - [ ] End-to-end lifecycle testing from deployment to retirement ## Dependencies & Blockers ### External Dependencies - [ ] Kubernetes cluster APIs for resource management (required) - [ ] Optional GitOps tools integration (ArgoCD, Flux) for advanced workflows - [ ] Optional monitoring integration (Prometheus) for automated scaling ### Internal Dependencies - [ ] Applications deployed via dot-ai system for metadata access - ✅ Available - [ ] Existing CLI and MCP interfaces for command integration - ✅ Available - [ ] Discovery and recommendation systems for intelligent operations - ✅ Available ### Current Blockers - [ ] None currently identified - all dependencies are satisfied ## Risk Management ### Identified Risks - [ ] **Risk**: Automated operations causing unintended service disruptions | **Mitigation**: Comprehensive testing, gradual rollout, manual override capabilities | **Owner**: Developer - [ ] **Risk**: Complex dependency management during updates and scaling | **Mitigation**: Dependency mapping, staged update procedures, rollback automation | **Owner**: Developer - [ ] **Risk**: Data loss during operations and cleanup | **Mitigation**: Backup validation, safe deletion procedures, recovery testing | **Owner**: Developer - [ ] **Risk**: Security implications of automated access and changes | **Mitigation**: RBAC integration, audit logging, permission validation | **Owner**: Developer ### Mitigation Actions - [ ] Implement comprehensive operation validation and safety checks - [ ] Create automated backup and recovery verification procedures - [ ] Develop operation monitoring and alerting for safety oversight - [ ] Plan gradual feature rollout with fallback to manual operations ## Decision Log ### Open Questions - [ ] What update strategies should be default vs. opt-in (rolling, blue-green, canary)? - [ ] How should we handle scaling decisions when cost optimization conflicts with performance? - [ ] What level of GitOps integration should be included in v1 vs. future versions? - [ ] How should we handle multi-cluster lifecycle operations and coordination? ### Resolved Decisions - [x] Focus on post-deployment lifecycle operations - **Decided**: 2025-07-28 **Rationale**: Complements existing deployment capabilities, addresses user needs after deployment - [x] Zero-downtime updates as primary goal - **Decided**: 2025-07-28 **Rationale**: Production readiness requirement, differentiates from basic deployment tools - [x] Integration with existing dot-ai infrastructure - **Decided**: 2025-07-28 **Rationale**: Seamless user experience, leverages existing metadata and patterns - [x] AI-powered operations assistance - **Decided**: 2025-07-28 **Rationale**: Consistent with dot-ai's AI-first approach, provides intelligent automation ## Scope Management ### In Scope (Current Version) - [ ] Rolling updates with zero-downtime deployment strategies - [ ] Manual and automated scaling operations (horizontal and vertical) - [ ] Configuration management with environment-specific updates - [ ] Interactive troubleshooting tools and AI-powered assistance - [ ] Safe deletion procedures with dependency validation - [ ] Basic backup and recovery capabilities ### Out of Scope (Future Versions) - [~] Full GitOps integration with advanced workflow management - [~] Multi-cluster lifecycle operations and coordination - [~] Advanced disaster recovery with cross-region failover - [~] Compliance automation and regulatory requirement management - [~] Advanced analytics and FinOps cost optimization - [~] Service mesh integration for advanced traffic management ### Deferred Items - [~] GitOps integration - **Reason**: Focus on core lifecycle operations first **Target**: Phase 3 - [~] Multi-cluster operations - **Reason**: Single cluster management meets initial need **Target**: v2.0 - [~] Advanced disaster recovery - **Reason**: Basic backup/restore sufficient for v1 **Target**: Future version - [~] Compliance automation - **Reason**: Core operations take priority **Target**: Enterprise version ## Testing & Validation ### Test Coverage Requirements - [ ] Unit tests for update strategies and rollback procedures (>90% coverage) - [ ] Unit tests for scaling algorithms and configuration management (>90% coverage) - [ ] Integration tests with Kubernetes APIs across different cluster types - [ ] End-to-end lifecycle tests from deployment through retirement - [ ] Disaster recovery and backup/restore validation testing - [ ] Performance tests for zero-downtime updates and scaling responsiveness ### User Acceptance Testing - [ ] Verify update procedures maintain application availability during changes - [ ] Test scaling operations respond appropriately to load changes - [ ] Confirm troubleshooting tools provide actionable guidance for common issues - [ ] Validate deletion procedures properly clean up all resources - [ ] Team member testing with real application lifecycle scenarios ## Documentation & Communication ### Documentation Completion Status - [ ] **`docs/lifecycle-management-guide.md`**: Complete - User guide with update, scaling, troubleshooting workflows - [ ] **`docs/scaling-guide.md`**: Complete - Comprehensive scaling strategies and automation guide - [ ] **`docs/lifecycle-management-guide.md`**: Complete - Added comprehensive lifecycle management operations - [ ] **`README.md`**: Updated - Added application lifecycle management to core capabilities - [ ] **Cross-file consistency**: Complete - All lifecycle terminology and examples aligned ### Communication & Training - [ ] Team announcement of lifecycle management capabilities and workflows - [ ] Create demo showing complete application lifecycle from deployment to retirement - [ ] Prepare documentation for lifecycle best practices and operational procedures - [ ] Establish guidelines for update strategies and scaling policies ## Launch Checklist ### Pre-Launch - [ ] All Phase 1 implementation tasks completed - [ ] Zero-downtime update procedures validated with test applications - [ ] Scaling operations tested across different load scenarios - [ ] Documentation and operational procedures completed - [ ] Team training materials prepared ### Launch - [ ] Deploy lifecycle management as extension to existing dot-ai MCP server - [ ] Monitor update and scaling operation success rates - [ ] Collect user feedback on operational workflow effectiveness - [ ] Resolve any performance or reliability issues ### Post-Launch - [ ] Analyze lifecycle operation patterns and success metrics - [ ] Monitor system performance and optimize operation efficiency - [ ] Iterate on automation algorithms based on operational outcomes - [ ] Plan Phase 2 enhancements based on usage patterns ## Work Log ### 2025-07-28: PRD Refactoring to Documentation-First Format **Duration**: ~30 minutes **Primary Focus**: Refactor existing PRD #4 to follow new shared-prompts/prd-create.md guidelines **Completed Work**: - Updated GitHub issue #4 to follow new short, stable format - Refactored PRD to documentation-first approach with user journey focus - Added comprehensive documentation change mapping for lifecycle management features - Structured implementation as meaningful milestones rather than micro-tasks - Aligned format with successful PRD patterns **Key Changes from Original**: - **Documentation-first**: Mapped all user-facing content to specific documentation files - **User journey focus**: Emphasized end-to-end workflows from deployment through retirement - **Meaningful milestones**: Converted to 3 major phases with clear user value delivery - **Content location mapping**: Specified exactly where each lifecycle aspect will be documented - **Traceability planning**: Prepared for `<!-- PRD-4 -->` comments in documentation files **Next Steps**: Ready for prd-start workflow to begin Phase 1 implementation with documentation creation --- ## Appendix ### Supporting Materials - [Kubernetes Deployment Strategies](https://kubernetes.io/docs/concepts/workloads/controllers/deployment/) - For update strategy implementation - [Kubernetes Horizontal Pod Autoscaling](https://kubernetes.io/docs/tasks/run-application/horizontal-pod-autoscale/) - For automated scaling integration - [Existing dot-ai MCP Patterns](./src/interfaces/mcp.ts) - For tool structure consistency ### Research Findings - Zero-downtime updates require careful coordination of health checks and traffic routing - Automated scaling effectiveness depends on appropriate metrics and thresholds - Configuration management complexity increases significantly with environment proliferation - Safe deletion requires comprehensive dependency mapping and validation ### Example Lifecycle Commands ```bash # Update operations dot-ai update my-app --version v2.0.0 --strategy rolling dot-ai rollback my-app --to-version v1.9.0 # Scaling operations dot-ai scale my-app --replicas 5 dot-ai scale my-app --auto --target-cpu 70% # Configuration management dot-ai config my-app --set ENV=production dot-ai config my-app --file production.yaml # Lifecycle management dot-ai delete my-app --confirm --cleanup-all dot-ai backup my-app --include-data ``` ### Implementation References - Kubernetes client-go library for API integration - Blue-green deployment patterns for zero-downtime updates - Horizontal Pod Autoscaler integration for automated scaling - ArgoCD/Flux patterns for GitOps integration preparation