DevOps AI Toolkit

# PRD: Comprehensive Application Monitoring and Observability System **Created**: 2025-07-28 **Status**: Draft **Owner**: Viktor Farcic **Last Updated**: 2025-07-28 ## Executive Summary Build an on-demand monitoring and observability system that provides instant health tracking, performance insights, and intelligent troubleshooting guidance for applications deployed via dot-ai. ## Documentation Changes ### Files Created/Updated - **`docs/monitoring-guide.md`** - New File - Complete guide for application monitoring and observability features - **`docs/troubleshooting-guide.md`** - New File - AI-powered troubleshooting workflows and commands - **`docs/mcp-guide.md`** - MCP Documentation - Add monitoring and observability MCP tools - **`README.md`** - Project Overview - Add monitoring and observability to core capabilities - **`src/core/monitoring/`** - Technical Implementation - On-demand monitoring system modules ### Content Location Map - **Feature Overview**: See `docs/monitoring-guide.md` (Section: "What is Application Monitoring") - **MCP Tools**: See `docs/mcp-guide.md` (Section: "Monitoring and Status Tools") - **Setup Instructions**: See `docs/monitoring-guide.md` (Section: "Configuration") - **MCP Tools**: See `docs/mcp-guide.md` (Section: "Monitoring and Analysis Tools") - **Examples**: See `docs/monitoring-guide.md` (Section: "Usage Examples") - **Troubleshooting**: See `docs/troubleshooting-guide.md` (Section: "AI-Powered Diagnostics") - **Monitoring Index**: See `README.md` (Section: "Monitoring and Observability") ### User Journey Validation - [ ] **Primary workflow** documented end-to-end: Deploy app → Check status → Diagnose issues → Get AI recommendations - [ ] **Secondary workflows** have complete coverage: Performance analysis, resource monitoring, log analysis - [ ] **Cross-references** between deployment docs and monitoring docs work correctly - [ ] **Examples and commands** are testable via automated validation ## Implementation Requirements - [ ] **Core functionality**: On-demand status queries and health checks - Documented in `docs/monitoring-guide.md` (Section: "Status Queries") - [ ] **User workflows**: Troubleshooting with AI-powered recommendations - Documented in `docs/troubleshooting-guide.md` (Section: "Diagnostic Workflows") - [ ] **MCP Tools**: Performance analysis and resource monitoring - Documented in `docs/mcp-guide.md` (Section: "Monitoring and Analysis Tools") - [ ] **Error handling**: Graceful handling of unavailable metrics and cluster issues - Documented in `docs/troubleshooting-guide.md` (Section: "Common Issues") - [ ] **Performance optimization**: Fast status queries (<5s basic, <30s deep analysis) ### Documentation Quality Requirements - [ ] **All examples work**: Automated testing validates all monitoring commands and status queries - [ ] **Complete user journeys**: End-to-end workflows from deployment to issue resolution documented - [ ] **Consistent terminology**: Same monitoring terms used across MCP guide, user guide, and README - [ ] **Working cross-references**: All internal links between monitoring docs and core docs resolve correctly ### Success Criteria - [ ] **Query performance**: Basic status queries complete in <5 seconds, deep analysis in <30 seconds - [ ] **Accuracy**: Health assessment accurately reflects application state for dot-ai deployed applications - [ ] **Actionability**: Troubleshooting recommendations provide clear next steps for issue resolution - [ ] **Zero infrastructure**: No additional cluster infrastructure requirements beyond Kubernetes APIs ## Implementation Progress ### Phase 1: Foundation Status Queries [Status: ⏳ PENDING] **Target**: Basic `dot-ai status` command with health checking working **Documentation Changes:** - [ ] **`docs/monitoring-guide.md`**: Create complete user guide with status command concepts and usage - [ ] **`docs/mcp-guide.md`**: Add status, health, and basic monitoring MCP tools - [ ] **`README.md`**: Update capabilities section to mention application monitoring and observability **Implementation Tasks:** - [ ] Implement `dot-ai status <app/namespace/cluster>` command with Kubernetes API integration - [ ] Create application health check system using pod status and readiness probes - [ ] Build service endpoint availability checking and dependency status - [ ] Add simple status reporting with clear output formatting ### Phase 2: AI-Powered Analysis and Troubleshooting [Status: ⏳ PENDING] **Target**: Intelligent analysis with troubleshooting recommendations **Documentation Changes:** - [ ] **`docs/troubleshooting-guide.md`**: Create comprehensive troubleshooting guide with AI recommendations - [ ] **`docs/monitoring-guide.md`**: Add "Performance Analysis" section with bottleneck identification - [ ] **`docs/mcp-guide.md`**: Add advanced diagnostic and analysis MCP tools **Implementation Tasks:** - [ ] Implement AI-powered analysis using Claude integration for status interpretation - [ ] Create performance bottleneck identification with resource utilization analysis - [ ] Build error pattern detection with troubleshooting recommendations - [ ] Add log aggregation and analysis for common issues ### Phase 3: Advanced Monitoring Features [Status: ⏳ PENDING] **Target**: Deep monitoring with custom metrics and reporting **Documentation Changes:** - [ ] **`docs/monitoring-guide.md`**: Add "Advanced Features" section with custom metrics and reporting - [ ] **Cross-file validation**: Ensure monitoring integrates seamlessly with deployment and lifecycle docs **Implementation Tasks:** - [ ] Add deep log analysis with correlation and pattern recognition - [ ] Implement custom metrics integration for application-specific monitoring - [ ] Create advanced reporting with export capabilities for status reports - [ ] Build integration with existing monitoring tools (Prometheus, Grafana) when available ## Technical Implementation Checklist ### Architecture & Design - [ ] Design query-time data collection system with live Kubernetes API queries (src/core/monitoring/data-collector.ts) - [ ] Implement real-time analysis engine during query execution (src/core/monitoring/analyzer.ts) - [ ] Create AI integration for intelligent status interpretation (src/core/monitoring/ai-diagnostics.ts) - [ ] Design integration with existing dot-ai deployment metadata (src/core/monitoring/metadata-integration.ts) - [ ] Plan CLI and MCP interface alignment with existing patterns - [ ] Document monitoring architecture and data flow ### Development Tasks - [ ] Build `dot-ai status` command with comprehensive health checking - [ ] Implement live metrics collection system (last 1-24 hours) - [ ] Create AI-powered troubleshooting recommendation engine - [ ] Add integration with Kubernetes events and diagnostics - [ ] Build customizable monitoring views for different user roles ### Documentation Validation - [ ] **Automated testing**: All monitoring commands and status queries execute successfully - [ ] **Cross-file consistency**: Deployment docs integrate seamlessly with monitoring features - [ ] **User journey testing**: Complete diagnostic workflows can be followed end-to-end - [ ] **Link validation**: All internal references between monitoring docs and core documentation resolve correctly ### Quality Assurance - [ ] Unit tests for status query system with various application states - [ ] Integration tests with Kubernetes API for health checking - [ ] Performance tests ensuring <5s basic status, <30s deep analysis - [ ] AI recommendation accuracy testing with known issue scenarios - [ ] Load testing for monitoring queries on large clusters ## Dependencies & Blockers ### External Dependencies - [ ] Access to Kubernetes cluster APIs for live status queries (required) - [ ] Claude API for AI-powered analysis and recommendations (already available) - [ ] Optional integration with existing monitoring infrastructure (Prometheus, Grafana) ### Internal Dependencies - [ ] Applications deployed via dot-ai system for metadata access - ✅ Available - [ ] Existing CLI and MCP interfaces for command integration - ✅ Available - [ ] Discovery engine for cluster resource identification - ✅ Available ### Current Blockers - [ ] None currently identified - all dependencies are satisfied ## Risk Management ### Identified Risks - [ ] **Risk**: Query performance on large clusters with many applications | **Mitigation**: Implement query optimization and caching, provide scoped queries | **Owner**: Developer - [ ] **Risk**: Limited historical data compared to continuous monitoring | **Mitigation**: Focus on current state analysis, integrate with existing monitoring when available | **Owner**: Developer - [ ] **Risk**: Resource usage during intensive analysis operations | **Mitigation**: Implement query limits, async processing for deep analysis | **Owner**: Developer - [ ] **Risk**: Dependency on cluster API availability and permissions | **Mitigation**: Graceful error handling, clear permission requirement documentation | **Owner**: Developer ### Mitigation Actions - [ ] Implement query performance monitoring and optimization - [ ] Create fallback modes for limited cluster access scenarios - [ ] Develop clear documentation for required cluster permissions - [ ] Plan integration points with continuous monitoring systems ## Decision Log ### Open Questions - [ ] What monitoring data retention period is optimal for query-time analysis (1 hour, 24 hours, 7 days)? - [ ] Should we cache monitoring data between queries or always query live? - [ ] How should we handle applications not deployed via dot-ai but present in monitored namespaces? - [ ] What integration level should we provide with existing monitoring tools? ### Resolved Decisions - [x] Query-time data collection over continuous monitoring - **Decided**: 2025-07-28 **Rationale**: Zero infrastructure requirements, simpler deployment, fits dot-ai usage patterns - [x] Focus on dot-ai deployed applications - **Decided**: 2025-07-28 **Rationale**: Leverages existing metadata, provides better context for recommendations - [x] AI-powered analysis using Claude integration - **Decided**: 2025-07-28 **Rationale**: Consistent with existing AI features, provides intelligent insights - [x] CLI and MCP interface integration - **Decided**: 2025-07-28 **Rationale**: Seamless user experience, leverages existing infrastructure ## Scope Management ### In Scope (Current Version) - [ ] On-demand status queries via `dot-ai status` command - [ ] Application health checks using Kubernetes APIs - [ ] AI-powered troubleshooting recommendations - [ ] Performance analysis and bottleneck identification - [ ] Basic log analysis and error pattern detection - [ ] Integration with existing dot-ai MCP interface ### Out of Scope (Future Versions) - [~] Continuous monitoring with persistent data storage - [~] Real-time alerting and notification systems - [~] Advanced metrics dashboard and visualization - [~] Multi-cluster monitoring aggregation - [~] Historical trend analysis beyond recent data - [~] Custom monitoring infrastructure deployment ### Deferred Items - [~] Continuous monitoring capabilities - **Reason**: Query-time approach sufficient for v1 **Target**: PRD #20 (Proactive Monitoring) - [~] Advanced dashboards - **Reason**: Focus on CLI/MCP integration first **Target**: Future version - [~] Multi-cluster aggregation - **Reason**: Single cluster monitoring meets initial need **Target**: v2.0 - [~] Historical analysis - **Reason**: Recent data sufficient for troubleshooting **Target**: Future enhancement ## Testing & Validation ### Test Coverage Requirements - [ ] Unit tests for status query system (>90% coverage) - [ ] Unit tests for AI analysis and recommendation engine (>90% coverage) - [ ] Integration tests with Kubernetes API across different cluster types - [ ] Performance tests with various cluster sizes and application counts - [ ] AI recommendation accuracy testing with known scenarios - [ ] Error handling tests for cluster access and permission issues ### User Acceptance Testing - [ ] Verify status commands provide accurate application health assessment - [ ] Test troubleshooting recommendations lead to successful issue resolution - [ ] Confirm performance analysis identifies actual bottlenecks - [ ] Validate monitoring works across different Kubernetes distributions - [ ] Team member testing with real application scenarios and issues ## Documentation & Communication ### Documentation Completion Status - [ ] **`docs/monitoring-guide.md`**: Complete - User guide with status queries, analysis, usage examples - [ ] **`docs/troubleshooting-guide.md`**: Complete - AI-powered diagnostic workflows and recommendations - [ ] **`docs/mcp-guide.md`**: Updated - Added comprehensive monitoring and status MCP tools - [ ] **`README.md`**: Updated - Added monitoring and observability to core capabilities - [ ] **Cross-file consistency**: Complete - All monitoring terminology and examples aligned ### Communication & Training - [ ] Team announcement of monitoring and observability capabilities - [ ] Create demo showing status queries and AI-powered troubleshooting - [ ] Prepare documentation for interpreting monitoring results and recommendations - [ ] Establish guidelines for monitoring best practices and usage patterns ## Launch Checklist ### Pre-Launch - [ ] All Phase 1 implementation tasks completed - [ ] Status query performance validated (<5s basic, <30s deep analysis) - [ ] AI recommendation accuracy tested with various issue scenarios - [ ] Documentation and usage examples completed - [ ] Team training materials prepared ### Launch - [ ] Deploy monitoring system as part of existing dot-ai MCP server - [ ] Monitor query performance and optimization needs - [ ] Collect user feedback on troubleshooting recommendation quality - [ ] Resolve any performance or accuracy issues ### Post-Launch - [ ] Analyze monitoring usage patterns and most common queries - [ ] Monitor system performance and optimize query efficiency - [ ] Iterate on AI recommendation algorithms based on user feedback - [ ] Plan Phase 2 enhancements based on usage insights ## Work Log ### 2025-07-28: PRD Refactoring to Documentation-First Format **Duration**: ~30 minutes **Primary Focus**: Refactor existing PRD #3 to follow new shared-prompts/prd-create.md guidelines **Completed Work**: - Updated GitHub issue #3 to follow new short, stable format - Refactored PRD to documentation-first approach with user journey focus - Added comprehensive documentation change mapping for monitoring 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 to issue resolution - **Meaningful milestones**: Converted to 3 major phases with clear user value delivery - **Content location mapping**: Specified exactly where each monitoring aspect will be documented - **Traceability planning**: Prepared for `<!-- PRD-3 -->` comments in documentation files **Next Steps**: Ready for prd-start workflow to begin Phase 1 implementation with documentation creation --- ## Appendix ### Supporting Materials - [Kubernetes API Documentation](https://kubernetes.io/docs/reference/kubernetes-api/) - For status query implementation - [Existing Discovery Engine](./src/core/discovery.ts) - For cluster resource identification - [Claude Integration Patterns](./src/core/claude.ts) - For AI-powered analysis ### Research Findings - Query-time monitoring provides zero infrastructure overhead compared to continuous monitoring - Kubernetes APIs provide sufficient data for health assessment and basic performance analysis - AI-powered analysis can significantly improve troubleshooting effectiveness - Integration with existing dot-ai metadata provides better context for recommendations ### Example Status Command Output ```bash $ dot-ai status my-app Application: my-app (namespace: default) Status: Healthy ✅ Pods: 3/3 running, 0 restarts in last 24h Resources: CPU 45%/100%, Memory 67%/80% Endpoints: All healthy, avg response time 120ms Issues: None detected $ dot-ai status my-app --deep [Performing deep analysis with AI recommendations...] Performance Analysis: No bottlenecks detected Recommendations: Consider increasing memory limit for better performance ``` ### Implementation References - Kubernetes client-go library for API integration - Claude SDK for AI-powered analysis - Existing dot-ai MCP patterns for tool structure - MCP integration patterns for server interface