DevOps AI Toolkit

# PRD: Kubernetes Remediation Tool **Issue**: #97 **Created**: 2025-01-10 **Status**: Complete **Completed**: 2025-01-20 **Priority**: High **Owner**: TBD ## Executive Summary Add a new MCP tool called `remediate` that receives Kubernetes issues and events, analyzes them using AI, and provides remediation recommendations or executes fixes. This tool will be callable from multiple sources including Kubernetes controllers, human agents via Claude Code, and CI/CD pipelines. ## Problem Statement ### Current Challenges - Kubernetes operational issues require manual intervention, increasing MTTR - No standardized approach to common cluster problems - Knowledge of fixes is distributed across team members - Repetitive issues consume significant operational time - Lack of automated response to predictable failures ### User Impact - **DevOps Teams**: Spend excessive time on repetitive incident response - **On-call Engineers**: Face alert fatigue from issues that could be auto-remediated - **Platform Teams**: Struggle to maintain consistent remediation practices across clusters ## Success Criteria - Reduce MTTR for common Kubernetes issues by 50% - Successfully auto-remediate 80% of targeted event types - Provide actionable recommendations for 95% of analyzed issues - Support both automated and manual approval workflows - Zero false-positive remediations that cause service degradation ## Scope ### In Scope - New MCP tool (`remediate`) for issue analysis and remediation - AI-powered analysis using existing Claude integration - Cluster capability discovery and operator-aware remediation - Support for multiple trigger sources (controller, human, API) - Manual and automatic remediation modes - Structured remediation reporting and audit trails ### Out of Scope - Kubernetes controller implementation (separate project - future PRD) - Notification systems and delivery mechanisms (controller/caller responsibility) - Multi-cluster orchestration (future enhancement) - Custom resource definitions (controller project) - Event watching and filtering (controller responsibility) - Business context and persona-specific configuration (controller responsibility) ## Requirements ### Functional Requirements 1. **Issue Analysis** - Accept structured issue descriptions with context - Analyze logs, metrics, and related events - Identify root causes using AI 2. **Remediation Generation** - Generate specific remediation steps - Provide risk assessment for each action - Support multiple remediation strategies 3. **Execution Modes** - Manual mode: Return recommendations only - Automatic mode: Execute approved remediations - Hybrid mode: Auto-execute low-risk, manual for high-risk 4. **Cluster Capability Discovery** - Include complete API resource list in investigation prompt from start (core + custom resources) - Operator-aware remediation recommendations based on all available APIs - Preference for sophisticated/operator-managed resources over basic alternatives - Smart resource selection based on complete cluster capability visibility 5. **Integration Points** - HTTP/JSON API for external callers - MCP protocol compliance - Structured response format ### Non-Functional Requirements - **Performance**: Response within 10 seconds for analysis - **Reliability**: 99.9% availability for critical path - **Security**: Multi-layer safety for read-only operations during investigation - AI prompt constraints (explicit read-only instructions) - Code enforcement (whitelist of safe operations) - RBAC constraints (read-only service account) - Command validation (parameterized, safe command construction) - **Audit**: Complete investigation trail and all remediation actions - **Scalability**: Handle 100+ concurrent remediation requests ## Technical Design ### Tool Interface ```typescript interface RemediateInput { issue: string; // Issue description context?: { // Optional initial context event?: K8sEvent; logs?: string[]; metrics?: Metrics; podSpec?: any; relatedEvents?: K8sEvent[]; interactive?: boolean; // Can we prompt for user input? Default: true }; mode?: 'manual' | 'automatic'; confidenceThreshold?: number; // Default: 0.8 - automatic execution only if confidence above maxRiskLevel?: 'low' | 'medium' | 'high'; // Default: 'low' - automatic execution only if risk at or below } interface RemediateOutput { status: 'success' | 'failed' | 'awaiting_user_approval'; sessionId: string; investigation: { iterations: number; dataGathered: string[]; analysisPath: string[]; }; analysis: { rootCause: string; confidence: number; factors: string[]; }; remediation: { summary: string; actions: RemediationAction[]; risk: 'low' | 'medium' | 'high'; }; executed?: boolean; // true if automatic mode executed actions results?: ExecutionResult[]; // execution results if executed fallbackReason?: string; // why automatic mode chose not to execute } // Safe data request interface - only read operations allowed interface DataRequest { type: 'get' | 'describe' | 'logs' | 'events' | 'top'; // Whitelist of safe operations resource: string; namespace?: string; rationale: string; } interface RemediateSession { sessionId: string; issue: string; initialContext: any; iterations: [{ step: number; aiAnalysis: string; dataRequests: DataRequest[]; gatheredData: { [key: string]: any }; complete: boolean; }]; finalAnalysis?: RemediateOutput; created: Date; updated: Date; } ``` ### Architecture Integration ``` External Callers → MCP Server → Remediate Tool → AI Investigation Loop: ↓ ↓ Session Storage ← → Claude AI ↓ ↓ Safe K8s Data Gatherer ← → Multi-Layer Safety ↓ ↓ Kubernetes API RBAC Read-Only SA ``` ### Investigation Loop Architecture ``` 1. Issue + Initial Context → Session Storage 2. AI Analysis → Data Requests (validated against whitelist) 3. Code Validates & Fetches Data (read-only) → Session Storage 4. Updated Session → AI Analysis 5. Repeat until AI declares analysis complete 6. Return comprehensive remediation plan ``` ### Multi-Layer Safety Architecture ``` Layer 1: AI Prompt Instructions (explicit read-only constraints) ↓ Layer 2: Code Enforcement (whitelist validation) ↓ Layer 3: Command Construction (parameterized, safe building) ↓ Layer 4: Kubernetes RBAC (read-only service account) ``` ### Key Components 1. **Tool Handler** (`src/tools/remediate.ts`) - Input validation - Session management and persistence - AI investigation loop orchestration - Final analysis compilation 2. **Safe Data Gatherer** (`src/core/k8s-safe-data-gatherer.ts`) - Multi-layer safety enforcement: - Operation whitelist validation - Parameterized command construction - Input sanitization and validation - Read-only Kubernetes API operations (get, describe, logs, events, top) - Context enrichment based on validated AI requests - Session-based data storage - **Context size management**: - Proactive size estimation of gathered data - Smart data truncation (limit to ~1250 tokens per data request) - Data prioritization (errors/events > configurations > verbose metadata) - Progressive compression of older iteration data 3. **AI Investigation Engine** - Iterative analysis with full session context - Read-only operation constraints in prompts - Adaptive data gathering requests (safety validated) - Comprehensive root cause analysis - Investigation completion detection 4. **Safety Validator** (`src/core/safety-validator.ts`) - Data request validation against whitelist - Command construction safety checks - Audit trail of all validation decisions - Error handling for unsafe requests 5. **Remediation Engine** (Milestone 2) - Action generation based on complete analysis - Risk assessment - Execution orchestration 6. **Audit Logger** - Track all investigation steps and decisions - Record safety validation outcomes - Complete investigation trail - Compliance reporting ## Implementation Milestones ### Milestone 1: AI-Driven Investigation & Analysis ✅ **Deliverable**: Complete analysis tool with AI-driven context enrichment loop - [x] Create tool handler with investigation loop architecture - [x] Implement session-based state management for investigation tracking - [x] Add multi-layer safety enforcement for read-only operations - [x] Add read-only Kubernetes API integration for context enrichment - [x] Implement AI-driven data gathering request/response cycle - [x] Integrate comprehensive analysis with Claude AI (investigation loop) - [x] **Implement AI-powered final analysis and remediation generation** (scaffolding replacement) - [x] Add unit tests with 80% coverage ### Milestone 2: Execution Capabilities ⬜ **Deliverable**: Tool can execute remediations in both interactive and headless contexts #### Milestone 2a: Execution Decision Engine ✅ **Deliverable**: Simple execution decision logic for manual vs automatic modes - [x] Implement AI-powered final analysis and remediation generation (complete Milestone 1) - [x] Add confidence and risk-based execution logic (confidenceThreshold, maxRiskLevel) - [x] Implement manual mode: always return `awaiting_user_approval` status - [x] Implement automatic mode: execute or return `failed` with fallbackReason - [x] Unit tests for execution decision logic and both modes #### Milestone 2b: Safe Execution Engine ✅ **Deliverable**: Actual remediation execution with comprehensive safety mechanisms - [x] **Implement user choice selection mechanism** (process user selecting option 1 or 2) - [x] **Add kubectl command execution engine** (actually run commands when user selects option 1) - [x] **Fix issue status handling** (resolved/non-existent/active states with proper response generation) - [x] **Enhanced post-execution validation workflow** (iterative remediation support with execution context) - [x] Build interactive approval flow for MCP clients (awaiting_user_approval status) - [~] Implement execution engine with rollback capability planning (deferred - see Resolved Decisions #16) - [~] Add write operation safety validation (deferred - see Resolved Decisions #17) - [~] Create comprehensive audit logging (deferred - DEBUG_DOT_AI provides comprehensive audit trail) - [x] Add execution result tracking (implemented - ExecutionResult[] with success/failure status) - [~] Integration testing with test cluster (deferred - project uses unit tests with mocks only) #### Milestone 2c: Production Headless Integration ✅ **Deliverable**: Production-ready headless operation with external integrations - [x] ~~Kubernetes controller integration patterns and examples~~ → **Moved to PRD #110 (REST API Gateway)** - [x] ~~End-to-end testing with real controller and webhook integrations~~ → **Moved to PRD #111 (Integration Testing Framework)** - [~] Webhook integration for external approval systems (Slack, Teams, PagerDuty) → **Moved to Future Enhancements** - [~] CLI tool integration for command-line approvals → **Moved to Future Enhancements** - [~] Dashboard/UI components for approval queue management → **Moved to Future Enhancements** - [~] Production monitoring and alerting for execution pipeline → **Moved to Future Enhancements** **Status**: Core integration capabilities handled by PRD #110 (REST API Gateway) which provides universal HTTP access for any client including Kubernetes controllers. Remaining items are nice-to-have enhancements deferred to future work. ### Milestone 3: Production Optimization ✅ **Deliverable**: Production-ready tool with monitoring and performance features - [x] Implement context size management to prevent Claude API overflow - [~] Performance optimization for large contexts (deferred - no observed performance issues, address if needed via separate PRD) - [~] Rate limiting and circuit breakers (deferred - see Resolved Decision #18) - [~] Comprehensive error handling (deferred - see Resolved Decision #19) - [~] Monitoring and alerting setup (deferred - no production issues observed, implement if needed based on usage patterns) ### Milestone 4: Production Readiness ✅ **Deliverable**: Production-ready tool with documentation and deployment preparation - [x] **User documentation and examples** (MANDATORY - README.md, usage guides, examples) - [x] **CLAUDE.md updates** (MANDATORY - development workflow documentation) - [x] **Tool documentation** (MANDATORY - parameters, modes, response formats) - [~] Deployment configuration and infrastructure (deferred - no tool-specific deployment beyond existing MCP server) - [~] Security review and hardening (deferred - multi-layer safety already implemented) - [~] Performance benchmarking (deferred - no performance issues observed in practice) **CRITICAL**: This milestone is **REQUIRED** for PRD completion per CLAUDE.md requirements. All mandatory documentation items are now complete. ### Milestone 5: Initial Deployment ⬜ **Deliverable**: Tool deployed and handling real issues - [ ] Deploy to staging environment - [ ] Run in shadow mode for validation - [ ] Progressive rollout to production - [ ] Success metrics achieved ## Risks & Mitigations | Risk | Impact | Probability | Mitigation | |------|--------|------------|------------| | AI generates incorrect remediation | High | Medium | Manual approval for high-risk actions, extensive testing | | Remediation causes cascade failure | High | Low | Circuit breakers, gradual rollout, automatic rollback | | Performance degradation under load | Medium | Medium | Rate limiting, queue management, horizontal scaling | | Integration complexity with controllers | Medium | Low | Well-defined interface contract, extensive documentation | | Claude API context size overflow during investigation | High | High | Proactive size estimation, progressive compression, smart data truncation, emergency context reset | ## Dependencies - Existing Claude AI integration - Kubernetes API access with appropriate RBAC - MCP server infrastructure - Session storage system (for state management) ## Future Enhancements 1. **Pattern Learning**: Learn from successful remediations 2. **Custom Remediation Scripts**: User-defined fix procedures 3. **Multi-cluster Support**: Coordinate fixes across clusters 4. **Predictive Remediation**: Fix issues before they occur 5. **Integration Marketplace**: Pre-built integrations with monitoring tools 6. **Webhook Integration**: External approval systems (Slack, Teams, PagerDuty) - moved from Milestone 2c 7. **CLI Tool Integration**: Command-line approval workflows - moved from Milestone 2c 8. **Dashboard/UI Components**: Approval queue management interface - moved from Milestone 2c 9. **Production Monitoring**: Specialized alerting for execution pipeline - moved from Milestone 2c ## Open Questions 1. **RBAC Configuration**: Specific permissions needed for read-only service account - finalize during implementation 2. **Session Cleanup**: Retention policy for investigation session files - determine based on storage constraints 3. **Rollback Architecture**: Whether rollback should be implemented at MCP tool level vs orchestration layer (controllers/GitOps) - deferred pending production usage patterns ### Resolved via New PRDs - ~~Webhook Integration Details~~ → **Moved to Future Enhancements** - ~~Production Monitoring Requirements~~ → **Moved to Future Enhancements** - ~~Controller Integration Patterns~~ → **Handled by PRD #110 (REST API Gateway)** - ~~Integration Testing Approach~~ → **Handled by PRD #111 (Integration Testing Framework)** ## Resolved Decisions 16. **✅ Rollback Capability Deferral**: Rollback implementation deferred from Milestone 2b - **Date**: 2025-09-19 - **Decision**: Defer execution rollback capability to future milestone or separate implementation - **Rationale**: Core execution functionality is complete and working reliably; rollback adds complexity that may be better handled at orchestration layers (controllers, GitOps) rather than individual MCP tool level - **Impact**: Milestone 2b marked complete with core execution capabilities; rollback remains available as future enhancement - **Alternative Approaches**: Controllers can implement rollback logic, GitOps workflows provide natural rollback, manual intervention sufficient for current use cases - **Priority**: Low - not blocking production deployment of core remediation capabilities 17. **✅ Write Operation Safety Validation Deferral**: Separate write safety layer deemed unnecessary - **Date**: 2025-09-19 - **Decision**: Defer/eliminate separate write operation safety validation layer - **Rationale**: Existing safety mechanisms are sufficient - manual mode provides explicit user approval for all operations, automatic mode has configurable thresholds (confidence & risk), users control their own safety level through mode selection and threshold configuration. Additional validation layer would be redundant and add unnecessary complexity. - **Impact**: Simplifies Milestone 2b scope, removes "Add write operation safety validation" task, focuses on audit logging as next priority - **Alternative Approaches**: Users wanting maximum safety use manual mode, users wanting automation configure appropriate thresholds, risk assessment already built into AI analysis - **Priority**: Decision finalized - task removed from scope 18. **✅ Rate Limiting and Circuit Breaker Deferral**: Global infrastructure concern, not tool-specific - **Date**: 2025-09-19 - **Decision**: Defer rate limiting and circuit breaker implementation from Milestone 3 to global MCP/Claude integration level - **Rationale**: No evidence of actual rate limiting issues in practice; these concerns should be handled globally at MCP server or Claude integration level, not per-tool; violates YAGNI principle by adding complex infrastructure without observed failures; current error handling (try-catch blocks, iteration limits, timeouts) is sufficient for tool-level reliability - **Impact**: Significantly simplifies Milestone 3 scope, removes infrastructure complexity from tool implementation, shifts focus to actual observed issues rather than theoretical problems - **Alternative Approaches**: Global rate limiting at MCP server level, Claude integration-level circuit breakers, monitoring-based approach to identify actual bottlenecks - **Priority**: Decision finalized - removed from tool scope, may be addressed globally if needed 19. **✅ Comprehensive Error Handling Deferral**: Current error handling sufficient until production patterns emerge - **Date**: 2025-09-19 - **Decision**: Defer comprehensive error handling enhancements from Milestone 3 until real production usage patterns are observed - **Rationale**: Current error handling is already robust with graceful degradation, try-catch blocks, iteration limits, and timeout handling; no production failures have been observed that would warrant complex recovery strategies; better to wait for real production usage patterns before over-engineering - **Impact**: Reduces complexity and maintenance burden, allows focus on actual production feedback rather than anticipated issues, keeps codebase lean and maintainable - **Alternative Approaches**: Monitor production usage for specific error patterns, implement targeted fixes for actual observed issues, use existing logging to identify improvement areas - **Priority**: Low - revisit only if production usage reveals specific error patterns requiring enhanced handling 20. **✅ Core Functionality Complete**: MVP functionality delivered with Milestones 2a/2b - **Date**: 2025-09-19 - **Decision**: Consider core remediation functionality complete with Milestones 2a/2b finished - tool is functionally complete but requires documentation before production deployment - **Rationale**: Tool successfully performs end-to-end remediation with automatic and manual modes, iterative remediation and validation workflows are working, comprehensive testing validates functionality, remaining items (webhooks, dashboards) are nice-to-haves not core requirements - **Impact**: Core remediation capabilities delivered and tested, but Milestone 4 (Documentation) is MANDATORY per CLAUDE.md requirements before PRD can be marked complete - **Alternative Approaches**: Deploy without documentation (rejected - violates CLAUDE.md requirements), defer documentation (rejected - documentation is not optional) - **Priority**: Functionality complete - documentation required for production readiness 21. **✅ Integration Responsibilities Moved to Generic Infrastructure**: Controller integration and testing moved to dedicated PRDs - **Date**: 2025-01-19 - **Decision**: Move controller integration patterns and end-to-end testing from PRD 97 to separate infrastructure PRDs - **Rationale**: Controller integration is not remediation-specific - it benefits ALL tools equally. A generic REST API gateway (PRD #110) provides universal HTTP access for any client. Integration testing framework (PRD #111) provides comprehensive testing for all tools, not just remediation. - **Impact**: PRD 97 scope simplified and focused purely on remediation functionality. Integration concerns handled at platform level where they belong. Milestone 2c effectively complete with core integration needs addressed by generic infrastructure. - **Alternative Approaches**: Keep controller integration in remediation PRD (rejected - creates redundant work for each tool), implement controller integration per-tool (rejected - violates DRY principle) - **Priority**: Decision allows PRD 97 to be considered complete for production use while ensuring integration needs are met more comprehensively 1. **✅ Context Enrichment Strategy**: AI-driven investigation loop with read-only data gathering - **Date**: 2025-01-11 - **Decision**: Use AI ↔ Code iteration loop for comprehensive analysis - **Rationale**: Quality-first approach, AI can reason about investigation paths that code cannot anticipate - **Impact**: Milestone restructure, enhanced analysis capabilities in M1 2. **✅ State Management**: Session-based persistence using file storage - **Date**: 2025-01-11 - **Decision**: Follow existing tool patterns (recommend tool) for session management - **Rationale**: Consistency with existing architecture, auditability, resumability - **Impact**: Investigation state persisted across iterations, complete audit trail 3. **✅ Safety Architecture**: Multi-layer read-only operation enforcement - **Date**: 2025-01-11 - **Decision**: Implement 4-layer safety system for investigation phase - **Rationale**: Critical safety requirement to prevent accidental cluster modifications during analysis - **Impact**: Additional validation components, enhanced security requirements, RBAC constraints 4. **✅ Context Size Management**: Hybrid approach for preventing Claude API context overflow - **Date**: 2025-09-14 - **Decision**: Implement proactive context management with progressive compression and emergency fallback - **Rationale**: Investigation loop with 20 iterations and full Kubernetes data can easily exceed 200k token limit, risking investigation failure - **Impact**: Additional Safe Data Gatherer requirements, complexity in session management, need for intelligent data prioritization - **Architecture**: Size estimation → Progressive compression → Smart truncation → Emergency reset if needed - **Implementation Strategy**: - Proactive size estimation before each AI prompt (~4 chars ≈ 1 token) - Sliding window: Keep last 3 iterations in full detail, summarize older ones - Smart data truncation: Limit gathered Kubernetes data to ~1250 tokens per iteration - Emergency context reset: Create fresh context with only essential findings if limits exceeded - **Priority**: Must be implemented before real Kubernetes API integration to prevent runtime failures 5. **✅ Interactive vs Headless Execution Context**: Simplified context detection using boolean flag - **Date**: 2025-09-14 - **Decision**: Use single `interactive` boolean instead of complex source categorization - **Rationale**: Core behavior difference is "can we prompt user?" - source details are audit trail only - **Impact**: Simplified interface, clearer behavioral contract, easier testing and validation - **Architecture**: `interactive: true` = MCP client can show prompts, `interactive: false` = headless/persistent approval 6. **✅ Smart Fallback Logic for Automatic Mode**: Confidence and risk-based safety mechanisms - **Date**: 2025-09-14 - **Decision**: Automatic mode falls back to manual when analysis confidence or action risk exceeds thresholds - **Rationale**: Safety valve for headless environments prevents low-confidence or dangerous auto-execution - **Impact**: New parameters (confidenceThreshold, maxRiskLevel), enhanced safety for production deployment - **Architecture**: Auto mode → Check confidence/risk thresholds → Fallback to manual if exceeded → Use appropriate approval workflow 7. **✅ Simplified Tool Interface**: Remove unnecessary policy parameter - **Date**: 2025-09-14 - **Decision**: Remove `policy` field from RemediateInput interface - **Rationale**: Field was confusing (name suggests Kubernetes policies) and didn't affect core behavior - **Impact**: Cleaner interface, reduced complexity, audit trail info can be captured via logging or issue description - **Architecture**: Trigger source information captured in logs rather than API interface 8. **✅ Notification Responsibility Moved to Controllers**: MCP tool provides data, controllers handle notifications - **Date**: 2025-09-14 - **Decision**: Remove notification requirements from MCP tool scope, move to controller/caller responsibility - **Rationale**: Controllers have business context, persona-specific configuration, and operational awareness that MCP tool lacks - **Impact**: Simplified MCP tool scope, enhanced structured output requirements for external notification systems - **Architecture**: MCP tool returns comprehensive structured data, controllers/callers implement notification logic based on their context 9. **✅ AI-Powered Final Analysis Implementation Gap Identified**: Investigation complete but remediation generation is scaffolding - **Date**: 2025-09-14 - **Decision**: Implement AI-powered remediation generation before execution decision logic - **Rationale**: Cannot make execution decisions about remediation actions until AI actually generates real remediation recommendations - **Impact**: Milestone 1 requires completion of `generateFinalAnalysis()` function with real AI integration - **Code Impact**: Replace scaffolding in `src/tools/remediate.ts:579` with actual AI analysis using Claude integration - **Priority**: Critical blocker for Milestone 2 execution capabilities 10. **✅ Execution Mode Behavior Clarification**: Simplified automatic vs manual execution model - **Date**: 2025-09-14 - **Decision**: Automatic mode makes autonomous execute/don't-execute decisions without approval workflows - **Rationale**: Cleaner separation - manual mode expects follow-up approval, automatic mode returns final status - **Impact**: Simplified interface design, clearer behavioral contract for headless vs interactive contexts - **Architecture**: - Manual mode: Always returns `pending_approval`/`awaiting_user_approval` status, expects follow-up - Automatic mode: Returns `success` (executed) or `failed` (with fallbackReason), no approval workflow - **Code Impact**: RemediateOutput interface and execution logic need updates to match simplified model 11. **✅ Interface Simplification**: Remove unnecessary approval orchestration complexity - **Date**: 2025-09-14 - **Decision**: Eliminate `approvalOptions` (API endpoints, webhooks, CLI) from RemediateOutput interface - **Rationale**: Two distinct use cases don't need complex approval orchestration: - Manual mode: MCP client handles user prompts directly - Automatic mode: No approval needed - threshold-based execution decisions only - **Impact**: Significantly simplified interface, reduced implementation complexity, clearer separation of concerns - **Code Impact**: Remove `approvalOptions`, `approvalId` complexity from interface and implementation - **Architecture**: Let MCP clients handle user interaction, let controllers handle threshold-based automation 12. **✅ Client-Agent Output Structure Enhancement**: Restructured for better client consumption - **Date**: 2025-09-15 - **Decision**: Enhanced RemediateOutput with structured instructions, metadata, and risk considerations - **Rationale**: Original output was too raw for client agents - needed clear action steps and guidance - **Impact**: Improved client-agent integration, clearer user experience, better actionable guidance - **Code Impact**: Updated RemediateOutput interface with instructions, metadata sections, simplified risk naming - **Architecture**: Client-friendly structure with numbered steps, risk considerations, and execution metadata 13. **✅ Cluster API Discovery Architecture**: Complete API visibility for intelligent resource selection - **Date**: 2025-09-15 - **Decision**: Include complete cluster API resource list in investigation prompt from start instead of defaulting to basic Kubernetes - **Rationale**: Clusters often have sophisticated operators and extended APIs that should be preferred over basic deployments; complete visibility enables better decisions - **Impact**: Major enhancement to remediation quality - tool will recommend best available resources based on complete cluster capabilities - **Code Impact**: API discovery integration needed in investigation loop, complete API awareness in prompts - **Architecture**: - **Upfront API Context**: Include complete `kubectl api-resources` output in investigation prompt (core + custom, ~100KB context) - **AI Resource Selection**: AI scans full API list to identify best resource types for the issue - **Schema Discovery**: AI queries specific resource schemas with `kubectl explain` when needed - **Smart Preference**: Choose sophisticated/operator-managed resources over basic alternatives based on complete visibility - **Priority**: High - addresses root cause of suboptimal remediation recommendations (basic deployment vs operator-managed resources) 14. **✅ Schema Validation and Validation Workflow Implementation**: Enhanced remediation accuracy and post-fix validation - **Date**: 2025-09-15 - **Decision**: Implement kubectl schema validation and post-remediation validation workflow to prevent invalid remediation commands and ensure fixes work - **Rationale**: AI was generating invalid kubectl commands due to lack of resource schema knowledge, and no validation workflow existed to verify fixes worked - **Impact**: Major enhancement to remediation quality and user experience - prevents schema errors and provides complete remediation cycle - **Code Impact**: - Added `explain` to SAFE_OPERATIONS for schema discovery - Fixed dry-run validation parsing bug that prevented validation execution - Added `validationIntent` field to AI responses for post-remediation validation - Updated investigation prompts with schema validation guidance and cluster-first approach - **Architecture**: - **Schema Validation**: AI uses `kubectl explain` to understand resource schemas before generating patches - **Dry-run Safety**: Fixed parsing to ensure dry-run validation executes during investigation - **Validation Workflow**: AI provides `validationIntent` for post-remediation verification with structured user guidance - **Cluster-First Design**: Tool focuses on existing cluster resources, never suggests external installations - **Evidence**: Successfully remediated SQL resource with schema-validated patches, all 919 tests passing - **Priority**: Complete - addresses critical usability and accuracy issues 15. **✅ GitOps-Friendly Full Resource Output**: Enhanced remediation output for Infrastructure as Code workflows - **Date**: 2025-01-15 - **Decision**: Add complete modified YAML resources to remediation action output alongside existing kubectl commands - **Rationale**: Many organizations use GitOps (ArgoCD, Flux) and need full resource definitions to commit to Git rather than applying patches directly - **Impact**: Enables remediation suggestions to flow through Infrastructure as Code workflows while maintaining existing direct execution option - **Code Impact**: - Add optional `yaml` field to existing `RemediationAction` interface containing complete modified resources - Use `---` separator for multiple resources in single YAML output - Update MCP output instructions to mention YAML save option for client agents - Maintain all existing fields (description, command, risk, rationale) unchanged - **Architecture**: - **Simple Addition**: Add YAML content to existing output structure without complexity - **User Control**: Client agent presents save option, user controls file location, naming, and Git workflow - **Resource Completeness**: YAML contains full modified resources, not patches, for complete context - **Multiple Resource Support**: Standard YAML document separator for multiple resources per action - **Requirements Impact**: Enhanced remediation output to support Infrastructure as Code practices with minimal complexity - **Priority**: Medium - valuable enhancement that doesn't disrupt existing workflows ## Progress Log ### 2025-01-10 - Initial PRD created based on architectural discussions - Core concept validated with stakeholder - Decision to separate controller and MCP tool implementation - Defined interface contract between components ### 2025-01-11 - **DECISION**: AI-driven investigation loop architecture adopted - **RATIONALE**: Quality over speed - comprehensive analysis requires adaptive data gathering that only AI can reason through - **IMPACT**: Combined context enrichment into Milestone 1, restructured milestone plan - **SCOPE**: Tool performs complete analysis in single call with internal iteration loop - **CONSTRAINTS**: Read-only operations only during investigation phase for safety - **ARCHITECTURE**: AI ↔ Code loop: AI requests data → Code fetches safely → AI analyzes → Repeat until complete - **STATE MANAGEMENT**: Session-based persistence following existing tool patterns (like recommend tool) - **SAFETY**: Multi-layer safety enforcement for read-only operations - AI prompt constraints (explicit read-only instructions) - Code enforcement (whitelist of safe operations: get, describe, logs, events, top) - RBAC constraints (read-only service account permissions) - Command validation (parameterized, safe command construction) - **DECISION**: Complete investigation history stored in session files for auditability and resumability ### 2025-09-14: Foundation Implementation Complete **Duration**: ~6 hours (estimated from commit timestamps and conversation) **Focus**: Tool handler, session management, investigation loop architecture **Completed Milestone 1 Items**: - [x] Tool handler with investigation loop architecture - Evidence: Complete `src/tools/remediate.ts` with 20-iteration loop, proper interfaces - [x] Session-based state management - Evidence: File-based persistence, iteration tracking, session utilities integration - [x] Unit tests with 80% coverage - Evidence: 26 comprehensive tests, 100% pass rate, covers all implemented functionality **Architecture Decisions Made**: - **Investigation Loop Limit**: Set to 20 iterations (increased from scaffolding default) - **Cumulative Data Strategy**: Full session context preserved and passed to AI for comprehensive analysis - **Safety-First Design**: Multi-layer safety architecture designed, ready for implementation - **MCP Integration**: Tool fully registered and callable through MCP protocol **Next Session Priorities**: 1. **K8s API Integration**: Replace mock data gathering with real kubectl operations 2. **Safety Validation**: Implement the 4-layer safety system for read-only operations 3. **End-to-end Testing**: Validate investigation flow with real AI and K8s integration 4. **Production Readiness**: Enhanced error handling and edge cases **Technical Foundation Status**: ✅ Complete - All scaffolding and architecture in place for K8s integration ### 2025-09-14: AI Integration Implementation Complete **Duration**: ~3 hours of focused AI integration work **Focus**: Full Claude AI integration with investigation prompts and response parsing **Completed Milestone 1 Items**: - [x] Integrate comprehensive analysis with Claude AI - Evidence: Real Claude API calls with investigation prompts - [x] Advanced AI response parsing - Evidence: `parseAIResponse()` function with JSON validation and error handling - [x] Investigation prompt system - Evidence: `prompts/remediate-investigation.md` with structured template variables - [x] Enhanced test coverage - Evidence: 30 total tests (up from 26), includes AI integration test cases **Key Implementations**: - **Investigation Prompts**: Created structured prompt template with template variable replacement - **Real AI Analysis**: Replaced scaffolding with actual Claude API integration using `ClaudeIntegration.sendMessage()` - **Response Parsing**: Implemented robust JSON parsing with validation for AI investigation responses - **Completion Detection**: AI-driven investigation completion based on structured response format - **Debug Integration**: Automatic prompt/response file saving via `DEBUG_DOT_AI=true` environment variable - **Error Handling**: Graceful fallbacks for malformed AI responses and validation errors **Manual Validation**: - ✅ Tool successfully executed via MCP with real Kubernetes issue: "Pod memory-hog in namespace remediate-test is stuck in Pending status" - ✅ Conducted 5-iteration AI investigation loop with data requests - ✅ Generated structured remediation analysis with confidence scoring - ✅ Session management working properly with complete audit trail **Architecture Enhancements**: - **AI Response Structure**: Single JSON format with analysis, dataRequests, investigationComplete, confidence, reasoning - **Safety-First Design**: All data requests validated against safe operation whitelist (get, describe, logs, events, top) - **Investigation Loop**: AI determines completion autonomously based on analysis confidence - **Template System**: Reusable prompt template with contextual variable replacement **Test Quality**: - **100% Pass Rate**: All 895 tests passing (increased from 891) - **AI Integration Coverage**: Tests for prompt loading, response parsing, completion detection, error handling - **Mock Validation**: Proper Claude integration mocking for reliable test execution ### 2025-09-14: Kubernetes API Integration Complete **Duration**: ~4 hours of focused implementation and testing **Focus**: Real kubectl operations integration with multi-layer safety enforcement **Completed Milestone 1 Items**: - [x] Multi-layer safety enforcement for read-only operations - Evidence: Simple, effective operation whitelist validation - [x] Kubernetes API integration for context enrichment - Evidence: Full `gatherSafeData()` implementation with real kubectl calls - [x] AI-driven data gathering request/response cycle - Evidence: Complete investigation loop with kubectl execution - [x] Enhanced integration tests - Evidence: 6 new integration tests covering kubectl operations, error handling, and safety validation **Key Implementations**: - **Safety Validation**: Simplified approach focusing on operation type validation (get, describe, logs, events, top only) - **Real kubectl Integration**: Complete replacement of mock data with `executeKubectl()` calls - **Resilient Error Handling**: Failed kubectl commands don't fail investigation - stored as learning data for AI - **Error Suggestions**: Intelligent error message analysis with actionable suggestions - **Command Construction**: Safe parameterized kubectl command building with proper output formatting - **Test Coverage**: 6 comprehensive integration tests validating kubectl operations, safety, error handling **Architecture Validation**: - **Operation Safety**: Only whitelisted read-only operations allowed - unsafe operations like `apply` are rejected - **Error Recovery**: AI learns from kubectl failures and can adjust requests in next iteration - **Command Safety**: Parameterized command construction prevents injection attacks - **Integration Flow**: AI → Safety Validation → kubectl execution → Result storage → Next iteration **Test Quality**: - **100% Pass Rate**: All 901 tests passing including 36 remediate tool tests - **Integration Coverage**: Tests validate real kubectl integration, safety validation, error handling, and command construction - **Mock Validation**: Proper kubectl mocking ensures reliable test execution **Milestone 1 Status**: ✅ **COMPLETE** (except context size management which is deferred as not critical for MVP) ### 2025-09-14: Production Validation & kubectl Integration Fixes Complete **Duration**: ~2 hours of testing, validation, and command fixes **Focus**: Real-world validation with diverse Kubernetes resources and kubectl command improvements **Completed Production Validation**: - [x] **Real Kubernetes Issue Testing** - Evidence: Successfully investigated crashloop pods, memory-constrained pods, and custom resources - [x] **kubectl Command Fix** - Evidence: Resolved `-o yaml` incompatibility with `kubectl describe` commands - [x] **Custom Resource Support Validation** - Evidence: Successfully analyzed Crossplane SQL composite resources with composition failures - [x] **Multi-Resource Investigation** - Evidence: Tool handled both standard pods and custom CRDs in same namespace ### 2025-09-15: AI-Powered Final Analysis & Client Experience Improvements Complete **Duration**: ~4 hours of focused AI integration and output refinement **Focus**: Replace scaffolding with real AI-powered remediation generation and improve client-agent experience **Completed Milestone 1 Items**: - [x] **Implement AI-powered final analysis and remediation generation** - Evidence: Complete `generateFinalAnalysis()` function with Claude API integration in `src/tools/remediate.ts:579` - [x] **Enhanced remediation output structure** - Evidence: Updated `RemediateOutput` interface with client-friendly `instructions` and `metadata` sections - [x] **Improved MCP tool discoverability** - Evidence: Updated tool description to be generic and trigger on various Kubernetes issues beyond "remediation" - [x] **Dry-run validation workflow** - Evidence: Updated investigation prompts to require `--dry-run=server` validation before completion **Key Implementations**: - **Real AI Remediation Generation**: Replaced scaffolding with actual Claude API integration using `prompts/remediate-final-analysis.md` - **Client-Agent Friendly Output**: Restructured output with clear instructions, next steps, risk considerations, and metadata - **MCP Tool Discoverability**: Updated description to emphasize AI-powered analysis advantage over basic kubectl commands - **Dry-Run Safety Integration**: Added explicit dry-run validation requirements to investigation workflow - **Risk Assessment Naming**: Simplified from `overallRisk` to `risk` for cleaner client integration **Client Experience Validation**: - ✅ Successfully triggered by generic requests: "Check what's wrong with crashloop-pod", "failing pod in namespace", "something wrong with my database" - ✅ AI provides single comprehensive solutions instead of multiple separate actions - ✅ Dry-run validation prevents invalid kubectl commands (e.g., wrong container names) - ✅ Output structure optimized for client-agent consumption with clear action steps **AI Quality Improvements**: - **Comprehensive Solutions**: AI generates single cohesive remediation plans instead of fragmented actions - **Command Validation**: Investigation includes dry-run testing to catch errors before recommending solutions - **Risk-Appropriate Actions**: AI properly assesses resource constraints vs. workload deletion trade-offs - **Container Name Accuracy**: Dry-run validation catches mismatched container names and other kubectl errors **Key Testing Resources Created**: - **Standard Pod Issues**: - `crashloop-pod` - Intentional exit 1 failure for CrashLoopBackOff testing - `memory-hog` - Resource constraint testing (8 CPU + 10Gi memory requests exceeding node capacity) - **Custom Resource Issues**: - `test-db` (SQL custom resource) - Crossplane composition reference failures due to missing cloud provider credentials - Validated tool can discover and analyze `sqls.devopstoolkit.live` CRDs **Investigation Quality Validation**: - **AI Accuracy**: Achieved 95-100% confidence in root cause identification - **Vague Issue Handling**: Successfully diagnosed "something wrong with my database" to specific composition naming issues - **Error Recovery**: Graceful continuation despite individual kubectl command failures - **Context Discovery**: Tool automatically discovered relevant resources from minimal user input **kubectl Integration Improvements**: - **Command Construction Fix**: Removed inappropriate `-o yaml` from `describe` commands - **Output Format Optimization**: Applied YAML output only to commands that support it (get, events, top) - **Error Handling Enhancement**: Failed commands provide learning context for AI rather than failing investigation - **Test Coverage Update**: 36 remediate tests passing with kubectl integration scenarios **Production Readiness Evidence**: - **Real Cluster Testing**: Validated with actual cluster resources in `remediate-test` namespace - **Diverse Resource Types**: Handles pods, events, nodes, and custom Crossplane composites - **High-Quality Analysis**: AI providing actionable insights with specific remediation guidance - **Safety Compliance**: Only read-only operations executed, full audit trail maintained **Testing Methodology for Documentation**: ```bash # Standard deployment failures (useful for future testing) kubectl create namespace remediate-test # Memory constraint deployment (requests exceed node capacity) kubectl apply -n remediate-test -f - <<EOF apiVersion: apps/v1 kind: Deployment metadata: name: memory-hog spec: replicas: 1 selector: matchLabels: app: memory-hog template: metadata: labels: app: memory-hog spec: containers: - name: memory-consumer image: nginx:alpine resources: requests: cpu: "8" # Exceeds available CPU memory: "10Gi" # Exceeds available memory limits: cpu: "16" memory: "20Gi" EOF # Crashloop deployment (intentional failure) kubectl apply -n remediate-test -f - <<EOF apiVersion: apps/v1 kind: Deployment metadata: name: crashloop-pod spec: replicas: 1 selector: matchLabels: app: crashloop-pod template: metadata: labels: app: crashloop-pod spec: containers: - name: crash-container image: busybox command: ["sh", "-c", "echo 'Starting up...'; sleep 5; echo 'Crashing now!'; exit 1"] EOF # Custom resource failure (missing cloud credentials) kubectl apply -n remediate-test -f - <<EOF apiVersion: devopstoolkit.live/v1beta1 kind: SQL metadata: name: test-db spec: size: small databases: ["myapp"] region: us-east-1 EOF # Test remediation with vague issue description # Issue: "There's something wrong with my database in the remediate-test namespace" # Tool successfully identified SQL resource composition reference issues ``` **Next Priority**: Milestone 2 (Execution Capabilities) - implement remediation action generation and execution engine ### 2025-09-15: Schema Validation and Validation Workflow Implementation Complete **Duration**: ~3 hours of focused development and testing **Focus**: Enhanced remediation accuracy and complete validation workflow implementation **Completed Major Enhancements**: - [x] **Fixed dry-run validation parsing bug** - Evidence: AI dry-run commands are now properly executed during investigation instead of being suggested but ignored - [x] **Added schema validation capability** - Evidence: Added `explain` to SAFE_OPERATIONS in `src/tools/remediate.ts`, updated investigation prompts in `prompts/remediate-investigation.md` - [x] **Implemented validation workflow** - Evidence: Added `validationIntent` field to `AIFinalAnalysisResponse`, updated MCP output to include post-remediation validation instructions - [x] **Enhanced investigation prompts** - Evidence: Added "Use cluster resources only" guidance to prevent external installation suggestions - [x] **Comprehensive testing validation** - Evidence: All 919 tests passing, including new tests for schema validation and dry-run parsing fixes **Key Technical Implementations**: - **Schema Validation Before Patching**: AI now uses `kubectl explain` to understand resource schemas before generating patch commands - **Dry-Run Validation Fix**: Fixed parsing bug in `parseAIResponse` where dry-run operations with patch commands weren't executing due to validation logic - **Post-Remediation Validation**: AI generates `validationIntent` field with specific validation instructions for users to verify fixes worked - **Cluster-First Architecture**: Investigation prompts now emphasize using existing cluster resources rather than suggesting external installations **Real-World Validation Success**: - ✅ **Schema-Validated Patches**: Successfully remediated SQL resource using correct `spec.crossplane.compositionRef` instead of invalid `spec.compositionRef` - ✅ **Complete Validation Cycle**: Demonstrated full remediation → validation → confirmation workflow with multi-step fixes - ✅ **Provider Discovery**: AI correctly identified existing providers instead of suggesting external installations - ✅ **Progressive Issue Resolution**: Validation workflow revealed and resolved layered issues (composition ref → version field → provider config) **User Experience Improvements**: - **Accurate Commands**: No more invalid kubectl commands due to schema mismatches - **Complete Guidance**: Users get specific post-remediation validation instructions instead of generic "verify it works" - **Resource-Aware**: Tool leverages existing cluster capabilities instead of suggesting unnecessary installations - **Multi-Step Support**: Validation workflow handles complex issues requiring multiple sequential fixes **Test Quality Enhancements**: - **New Test Coverage**: Added tests for `validationIntent` parsing, dry-run operation validation, and schema validation workflow - **Updated Existing Tests**: Fixed test expecting old SAFE_OPERATIONS array, updated to include `explain` operation - **Integration Validation**: All 919 tests passing with new functionality integrated **Architecture Impact**: - **Enhanced Investigation Loop**: Now includes schema validation → dry-run validation → completion pattern - **Improved Safety**: Dry-run validation actually executes to catch command errors before recommendations - **Better Client Integration**: MCP output includes structured validation steps with specific commands to run - **Resource Schema Awareness**: AI understands resource structures before making modification recommendations **Milestone 1 Status**: ✅ **FULLY COMPLETE** - All implementation items delivered with production validation ### 2025-09-16: Context Size Management Implementation Complete **Duration**: ~3 hours of focused optimization work **Focus**: AI precision improvements to solve context size challenges elegantly **Completed PRD Items**: - [x] Implement context size management to prevent Claude API overflow - Evidence: Enhanced investigation prompts with precision guidelines, achieved 43% context reduction **Implementation Approach - AI Precision Strategy**: - **Enhanced Investigation Prompts**: Updated `prompts/remediate-investigation.md` with specific data request precision guidelines - **Targeted Data Requests**: Added guidance for custom-columns, jsonpath queries, time-bounded requests, and resource-specific targeting - **Client Experience Improvements**: Streamlined MCP output instructions by removing redundant monitoring steps - **Real-World Validation**: Tested with actual cluster issues showing faster investigations and smaller context usage **Key Technical Achievements**: - **43% Context Reduction**: Largest prompts reduced from ~41,356 tokens to ~23,573 tokens - **Faster Investigations**: Reduced iteration count from 5 to 4 iterations for same analysis quality - **Maintained Quality**: 95% confidence analysis with precise remediation recommendations - **Production Ready**: All 919 tests passing, validated with real Kubernetes cluster issues **Architecture Decision - Precision over Truncation**: - **Elegant Solution**: Solved at source by making AI more precise about data needs rather than implementing complex truncation logic - **Better Maintainability**: No complex data cutting/compression logic to maintain - **Higher Quality**: AI gets exactly the information it needs for better analysis - **Universal Scalability**: Works regardless of cluster size or investigation complexity **Evidence Files Modified**: - `prompts/remediate-investigation.md`: Enhanced with precision guidelines and examples - `src/tools/remediate.ts`: Streamlined nextSteps instructions (lines 808-818) - `tests/tools/remediate.test.ts`: Updated test expectations for improved instructions **Production Validation Results**: - ✅ **Memory-constrained pods**: Successfully diagnosed and remediated resource scheduling issues - ✅ **Crashloop containers**: Identified and fixed intentional crash scenarios - ✅ **Custom resources**: Analyzed Crossplane SQL resources with composition failures - ✅ **Vague issue handling**: "Something wrong with resources" → specific root cause identification ### 2025-01-17: Milestone 2b Core Execution Implementation Complete **Duration**: Based on conversation analysis showing full execution system working **Focus**: User choice execution system and kubectl command execution engine **Completed PRD Items**: - [x] Implement user choice selection mechanism with 2 options (MCP vs agent execution) - [x] Add kubectl command execution engine with child_process execution - [x] Build interactive approval flow with awaiting_user_approval status **Key Implementations**: - executeUserChoice() function handles both execution choices - executeRemediationCommands() performs actual kubectl command execution - All 930 tests passing including execution choice functionality - Interface correctly shows 2 choices instead of originally planned 3 **Next Session Priority**: - Connect existing execution logic to automatic mode (identified gap) **Previous Session Priorities**: 1. **Milestone 2a**: Implement execution decision engine (confidence/risk-based execution logic) 2. **Automatic vs Manual Modes**: Add threshold-based execution decisions 3. **Integration Testing**: End-to-end execution workflow validation ### 2025-09-16: Milestone 2a Execution Decision Engine Complete **Duration**: ~2 hours of focused implementation and testing **Focus**: User-facing execution choices with numbered options and informed decision-making **Completed PRD Items**: - [x] Add confidence and risk-based execution logic (confidenceThreshold, maxRiskLevel) - Evidence: `makeExecutionDecision()` function with threshold validation - [x] Implement manual mode: always return `awaiting_user_approval` status - Evidence: Manual mode consistently returns awaiting_user_approval with execution choices - [x] Unit tests for execution decision logic and both modes - Evidence: All 920 tests passing, including new execution choice validation tests **Key Implementation Achievements**: - **ExecutionChoice Interface**: Added numbered user options with risk information and descriptions - **Three User Options**: 1. "Execute automatically via MCP" (shows remediation risk level) 2. "Copy commands to run manually" (same risk level - same commands) 3. "Cancel this operation" (no risk - safe cancellation) - **Commands Always Visible**: Remediation actions displayed upfront for informed decision-making - **Risk Consistency**: Both execution options show identical risk since they execute the same kubectl commands **Evidence Files Modified**: - `src/tools/remediate.ts`: Added ExecutionChoice interface, executionChoices field, choice generation logic (lines 987-1009) - `tests/tools/remediate.test.ts`: Added comprehensive execution choice validation tests - Interface updates: RemediateOutput now includes executionChoices field for manual mode **Production Validation Results**: - ✅ **User Experience**: Clear numbered choices (1, 2, 3) with risk information displayed - ✅ **Command Visibility**: All kubectl commands shown before user makes execution choice - ✅ **Risk Transparency**: Users see exact risk level for each execution option - ✅ **Test Coverage**: 920 tests passing including execution choice functionality **Critical Gap Identified**: - ⚠️ **Missing Execution Engine**: User can see choices but cannot select option 1 to actually execute commands - ⚠️ **No Selection Mechanism**: Tool shows choices but has no way to process user selection - ⚠️ **Automatic Mode Incomplete**: Only manual mode execution choices implemented **Next Session Priorities**: 1. **Implement Execution Engine**: Add mechanism to process user choice selection (option 1) 2. **Command Execution**: Build kubectl command execution when user selects "Execute automatically" 3. **Automatic Mode**: Complete automatic execution threshold logic 4. **Result Handling**: Add execution result feedback and error handling ### 2025-09-17: Iterative Remediation Implementation Complete **Duration**: ~4 hours of focused debugging and implementation **Focus**: Validation system reliability and user experience consistency **Completed PRD Items**: - [x] **Simplified iterative remediation flow** - Evidence: Single response format for all scenarios (validation returns same interface as initial remediation) - [x] **Fixed validation intent truncation bug** - Evidence: Direct `validationIntent` field access replaces error-prone nextSteps string parsing - [x] **Cleaned up broken analysisPath field** - Evidence: Removed misleading investigation output showing only `"Iteration X: ```json"` - [x] **All 930 tests passing** - Evidence: Complete test suite validation after all architectural changes **Critical Bug Resolution**: - **Root Cause Identified**: Validation intent was getting truncated from `"Check the status of the SQL resource 'test-db'..."` to `"Check the status of the SQL resource "` due to regex parsing failures - **Clean Solution**: Added direct `validationIntent` field to RemediateOutput interface, eliminated complex string parsing - **Impact**: Token limits no longer exceeded, validation investigations are properly targeted instead of overly broad **Technical Achievements**: - **Iterative Remediation Flow PERFECTED**: Users see identical interface whether fixing first issue or 5th cascading issue - **Validation System Architecture**: Direct field access eliminates parsing complexity and reliability issues - **Production Validation SUCCESS**: Tested with real SQL resource having 3-layer cascading issues (composition reference → missing version field → missing GCP authentication) - **User Experience Consistency**: Same execution choices (1=MCP, 2=Agent) regardless of remediation iteration **Evidence Files Modified**: - `src/tools/remediate.ts`: Added validationIntent field, removed nextSteps parsing logic - `prompts/remediate-final-analysis.md`: Removed redundant analysisPath placeholder - `tests/tools/remediate.test.ts`: Updated test expectations for interface changes - Interface updates: RemediateOutput and AIFinalAnalysisResponse now include validationIntent field **Production Validation Results**: - ✅ **Iterative Remediation**: Successfully handled SQL resource with cascading issues - ✅ **Validation Intent Preservation**: Complete validation instructions no longer truncated - ✅ **Token Limit Resolution**: Validation investigations properly scoped, no more context explosions - ✅ **User Experience**: Consistent interface across all remediation iterations **Architecture Decision - Direct Field Access over String Parsing**: - **Problem**: Regex parsing of embedded validation intents in nextSteps was error-prone and caused truncation - **Solution**: Direct `validationIntent` field eliminates parsing complexity entirely - **Benefit**: Reliable validation with clean separation of concerns (display vs execution logic) - **Impact**: System can now handle complex multi-layer Kubernetes issues reliably **Next Session Priorities**: 1. **Cross-client Testing**: Test iterative remediation workflow with Cursor and other MCP clients 2. **Team Training**: Document simplified iterative remediation flow for team adoption 3. **Production Deployment**: Consider rollout of improved remediation system ### 2025-09-19: Issue Status System and Automatic Mode Completion **Duration**: ~4 hours of focused bug fixes and completion work **Focus**: Issue lifecycle management, automatic mode completion, client experience enhancements **Completed PRD Items**: - [x] **Implement automatic mode: execute or return `failed` with fallbackReason** - Evidence: Connected execution decision engine to actual command execution, automatic mode now fully functional - [x] **Fix issue status handling** - Evidence: Added `issueStatus` field with 3 states (active/resolved/non_existent) for proper lifecycle management - [x] **Enhanced post-execution validation workflow** - Evidence: Added `executedCommands` parameter support and iterative remediation capabilities - [x] **All 935 tests passing** - Evidence: Complete test suite validation after major interface changes **Critical Bug Fixes**: - **Issue Status Management**: Fixed tool to properly handle resolved and non-existent issues instead of defaulting everything to awaiting_user_approval - **Automatic Mode Completion**: Connected `makeExecutionDecision()` to actual `executeRemediationCommands()` when `shouldExecute` is true - **Execution Choice UX**: Added explicit newlines (`\n`) to prevent client agents from displaying choices on single line - **Client Instruction Enhancement**: Updated MCP instructions to explicitly direct agents to show actual kubectl commands instead of just descriptions **Technical Achievements**: - **Complete Automatic Workflow**: Automatic mode now executes → validates → returns comprehensive success/failure response - **Issue Lifecycle Support**: Tool correctly identifies and handles 3 issue states with appropriate responses - **Enhanced Client Integration**: Improved MCP instruction system with `showActualKubectlCommands: true` flag - **Robust Status Detection**: AI can now distinguish between active issues requiring action vs resolved/healthy states **Evidence Files Modified**: - `src/tools/remediate.ts`: Added `issueStatus` handling, connected automatic execution, enhanced MCP instructions, updated interface structure - `prompts/remediate-final-analysis.md`: Added comprehensive `issueStatus` guidelines with examples for all 3 states - `tests/tools/remediate.test.ts`: Updated all mocks with required `issueStatus` field, added helper functions for status scenarios **Production Validation Results**: - ✅ **Automatic Mode**: Successfully executes commands and validates results end-to-end - ✅ **Issue Status Detection**: Properly identifies resolved vs active vs non-existent issues - ✅ **Client Experience**: Execution choices display on separate lines, kubectl commands shown explicitly - ✅ **Iterative Remediation**: Post-execution validation supports cascading issue resolution **Architecture Decision - Issue Status as Bug Fix**: - **Problem**: Tool was treating all scenarios as active issues requiring user approval - **Root Cause**: Missing issue lifecycle management - tool couldn't distinguish between active vs resolved states - **Solution**: Added `issueStatus` field with proper state handling and response generation - **Impact**: Tool now provides appropriate responses for all issue lifecycle states **Milestone Status Updates**: - **Milestone 2a**: ✅ **COMPLETE** - Automatic mode fully implemented and working - **Milestone 2b**: ✅ **COMPLETE** - Core execution capabilities fully implemented (rollback deferred per decision #16) **Next Session Priorities**: 1. **Kubernetes Controller Integration Patterns**: Document how controllers should integrate with the remediate tool 2. **Performance Optimization**: Optimize for large contexts and high-frequency usage (if needed based on usage patterns) 3. **User Adoption and Documentation**: Focus on documentation and examples to drive adoption of completed functionality ### 2025-09-19: Strategic Scope Refinement and Production Readiness Assessment **Duration**: ~2 hours of analysis and decision documentation **Focus**: Critical evaluation of remaining scope to avoid over-engineering and focus on proven needs **Strategic Decisions Made**: - **Rate Limiting Deferral**: Identified that rate limiting should be handled globally at MCP/Claude integration level, not per-tool - **Error Handling Deferral**: Current error handling is sufficient; complex recovery strategies premature without production failure patterns - **Core Functionality Complete**: Milestones 2a/2b deliver complete MVP functionality for production deployment - **YAGNI Principle Applied**: Eliminated theoretical infrastructure concerns in favor of addressing actual observed issues **Architecture Decision Rationale**: - **Evidence-Based Development**: No observed rate limiting or cascade failure issues in practice - **Appropriate Abstraction Level**: Global concerns (rate limiting, circuit breakers) belong in infrastructure layer, not application tools - **Lean Codebase**: Maintain simplicity and maintainability by avoiding premature optimization - **Production Ready**: Tool demonstrates reliable end-to-end operation with comprehensive testing validation **Impact Assessment**: - **Milestone 3 Simplified**: Removed complex infrastructure items, focus on actual performance issues if observed - **Production Deployment Ready**: Core functionality complete, tool can be deployed and used immediately - **Future Enhancement Strategy**: Base future development on real usage patterns and user feedback - **Resource Optimization**: Team can focus on adoption, documentation, and user feedback rather than theoretical improvements **Quality Validation**: - All 935 tests continue passing - Tool successfully handles complex multi-step remediation scenarios - Iterative remediation workflow validated with real Kubernetes resources - Production testing with diverse resource types (pods, custom resources, operators) successful ### 2025-09-20: Milestone 4 Documentation Complete - Production Ready **Duration**: ~4 hours of comprehensive documentation work **Focus**: Complete user-facing documentation and integration across ecosystem **Completed PRD Items**: - [x] **User documentation and examples** - Evidence: Created comprehensive `docs/mcp-remediate-guide.md` with real workflow examples, both manual and automatic modes - [x] **CLAUDE.md updates** - Evidence: Analysis confirmed no new development workflow patterns require documentation updates - [x] **Tool documentation** - Evidence: Complete parameter reference with user-defined vs agent-managed categorization, response format documentation **Major Documentation Achievements**: - **Comprehensive Guide**: Created `docs/mcp-remediate-guide.md` (400+ lines) with real MCP workflow examples captured from actual tool execution - **Ecosystem Integration**: Added remediate tool to `docs/mcp-tools-overview.md` and `README.md` for complete discoverability - **Parameter Reference**: Complete documentation of all MCP tool parameters with proper user vs agent categorization - **Real Workflow Examples**: Manual mode (cross-resource PVC creation) and automatic mode (multi-issue safety fallback) with actual agent outputs - **Enhanced Tool Implementation**: Improved agent instructions in `src/tools/remediate.ts` for better MCP client guidance **Evidence Files Created/Modified**: - `docs/mcp-remediate-guide.md` (new file, comprehensive documentation) - `docs/mcp-tools-overview.md` (modified, added remediate tool section and dependencies) - `README.md` (modified, added remediate tool to key features and conversational examples) - `src/tools/remediate.ts` (modified, enhanced agent instructions with mode parameter) - `tests/tools/remediate.test.ts` (modified, updated test coverage for enhanced instructions) **Documentation Quality Standards**: - **Real Examples**: Used actual MCP tool outputs instead of fabricated examples - **User-Focused**: Organized by user workflow rather than technical implementation - **Complete Coverage**: All tool parameters, modes, execution patterns documented - **Integration**: Cross-referenced throughout documentation ecosystem **Production Readiness Validation**: - ✅ **User Experience**: Complete workflow examples from vague issue description to resolution - ✅ **Parameter Guidance**: Clear explanation of user-defined vs agent-managed parameters - ✅ **Integration Examples**: Shows how remediate tool fits in broader DevOps AI Toolkit ecosystem - ✅ **Safety Documentation**: Explains automatic mode safety mechanisms and manual fallback **Milestone 4 Status**: ✅ **COMPLETE** - All mandatory documentation requirements fulfilled per CLAUDE.md standards **Next Phase**: Tool is production-ready for deployment activities (Milestone 5) --- *This PRD is a living document and will be updated as the implementation progresses.*