DevOps AI Toolkit

# PRD: Proactive In-Cluster Monitoring System **Created**: 2025-07-28 **Status**: Complete **Owner**: Viktor Farcic **Last Updated**: 2025-10-05 **Completed**: 2025-10-05 ## Executive Summary Build proactive monitoring system with health checks, alerting, anomaly detection, and automated remediation for deployed applications beyond on-demand status queries. ## Documentation Changes ### Files Created/Updated - **`docs/proactive-monitoring-guide.md`** - New File - Complete guide for continuous monitoring and alerting - **`docs/alerting-configuration-guide.md`** - New File - Alert setup and notification configuration - **`docs/mcp-guide.md`** - MCP Documentation - Add monitoring setup and management MCP tools - **`README.md`** - Project Overview - Add proactive monitoring to operational capabilities - **`src/core/monitoring/`** - Technical Implementation - Proactive monitoring system modules ### User Journey Validation - [ ] **Primary workflow** documented end-to-end: Deploy app → Enable monitoring → Receive alerts → Auto-remediation - [ ] **Secondary workflows** have complete coverage: Alert configuration, anomaly detection, monitoring management - [ ] **Cross-references** between on-demand monitoring (PRD #3) and proactive monitoring work correctly ## Implementation Requirements - [ ] **Core functionality**: Continuous monitoring with health checks and alerting - [ ] **User workflows**: Proactive issue detection and automated remediation - [ ] **Performance optimization**: Efficient monitoring with minimal cluster resource impact ## Work Log ### 2025-10-05: PRD Closure - Already Implemented **Duration**: N/A (administrative closure) **Status**: Complete **Closure Summary**: This PRD requested proactive Kubernetes cluster monitoring with health checks, alerting, anomaly detection, and automated remediation. **Core functionality (~60-70%) is already implemented** by the separate [dot-ai-controller](https://github.com/vfarcic/dot-ai-controller) project. **Implementation Evidence**: The dot-ai-controller is a Kubernetes controller that bridges cluster events with AI-powered remediation using the DevOps AI Toolkit's MCP server. **Functionality Delivered**: - **Continuous monitoring** - Event-based monitoring via Kubernetes event watching (pod failures, crashes, scheduling issues) - **Intelligent alerting** - Slack notifications with detailed AI analysis and remediation results - **Automated remediation** - Automatic/manual modes with configurable confidence thresholds and risk levels - **AI-powered analysis** - Claude integration via MCP for intelligent event analysis and fix generation - **Policy-based configuration** - RemediationPolicy CRD for configuring event filters and remediation behavior - **Rate limiting** - Prevention of event storms with cooldown periods - **Status tracking** - Comprehensive logging of remediation actions **Key Implementation Details**: - **Architecture**: Kubernetes controller + dot-ai MCP service + Slack notifications - **Event filtering**: By event type, reason, and involved object kind - **Remediation modes**: Automatic (AI fixes without intervention) and Manual (AI recommendations for approval) - **Confidence thresholds**: Configurable (default 0.8-0.85) to control auto-remediation - **Use cases**: Pod scheduling failures, OOMKilled events, missing PVCs, infrastructure issues **Not Implemented** (advanced features, deferred to future PRD): - **Continuous metrics monitoring** - Prometheus-style metrics scraping and analysis (event-based only) - **Predictive analytics** - Baseline behavior learning and deviation detection - **Multi-channel alerting** - Email, webhooks, PagerDuty (Slack only currently) - **Historical analysis** - Long-term trend analysis and pattern recognition **Gap Analysis**: The dot-ai-controller provides **event-driven reactive monitoring** (responds to Kubernetes events) rather than **continuous proactive monitoring** (metrics polling). This covers the majority of critical operational needs: - ✅ Real-time issue detection and response - ✅ AI-powered problem diagnosis - ✅ Automated or guided remediation - ⚠️ Missing: Metrics-based alerting (CPU, memory, disk) - ⚠️ Missing: Predictive issue detection **Future Considerations**: Advanced features like continuous metrics monitoring and predictive analytics can be addressed in a new PRD that extends the remediation system to incorporate Prometheus/OpenTelemetry metrics and observability data not available in Kubernetes API events. ### 2025-07-28: PRD Refactoring to Documentation-First Format **Completed Work**: Refactored PRD #20 to follow new documentation-first guidelines with comprehensive proactive monitoring features.