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openshift-resource-governance/AIAgents-Support.md

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AI Agents Support - OpenShift Resource Governance Tool

📋 Project Status Overview

Current State: ✅ PRODUCTION READY - Application is fully functional and cluster-agnostic

Last Updated: 2025-09-30 Current Version: 1.0.0 Deployment Status:

  • ✅ OCP 4.18: Working
  • ✅ OCP 4.19: Working

🎯 Project Description

OpenShift Resource Governance Tool is a comprehensive web application that analyzes Kubernetes/OpenShift cluster resource usage, validates resource requests and limits against Red Hat best practices, and provides historical analysis using Prometheus metrics.

Core Features

  • Resource Analysis: Real-time analysis of CPU/memory requests and limits
  • Smart Problem Detection: Identifies workloads without requests/limits and provides detailed analysis
  • Modal-based Analysis: Professional interface with detailed pod and container analysis
  • Historical Analysis: Workload-based historical resource usage (1d, 7d, 30d)
  • VPA Integration: Vertical Pod Autoscaler recommendations (planned)
  • Export Reports: Generate reports in XLS, CSV, PDF formats
  • Cluster Agnostic: Works on any OpenShift cluster without configuration

🏗️ Architecture

Backend (FastAPI)

  • Main App: app/main.py - FastAPI application with lifespan management
  • API Routes: app/api/routes.py - REST endpoints for cluster data
  • Core Services:
    • app/core/kubernetes_client.py - K8s/OpenShift API client
    • app/core/prometheus_client.py - Prometheus metrics client
    • app/services/validation_service.py - Resource validation rules
    • app/services/historical_analysis.py - Historical data analysis
    • app/services/report_service.py - Report generation
  • Models: app/models/resource_models.py - Pydantic data models

Frontend (HTML/CSS/JavaScript)

  • Static Files: app/static/index.html - Single-page application
  • Features:
    • Pragmatic dashboard with single view
    • Modal-based detailed analysis for namespace problems
    • Problem Summary table showing namespace issues
    • Real-time cluster data display
    • Professional interface without browser alerts
    • Responsive design with Bootstrap

Infrastructure

  • Container: Docker with Python 3.11
  • Deployment: Kubernetes/OpenShift with rolling updates
  • Monitoring: Prometheus integration for metrics
  • Security: RBAC with cluster-monitoring-view permissions

🚀 Current Deployment Status

Working Clusters

  1. OCP 4.18: resource-governance.apps.shrocp4upi418ovn.lab.upshift.rdu2.redhat.com
  2. OCP 4.19: resource-governance-route-resource-governance.apps.shrocp4upi419ovn.lab.upshift.rdu2.redhat.com

Deployment Process

# Quick deploy (recommended)
./scripts/deploy-complete.sh

# Manual deploy
./scripts/build-and-push.sh
oc apply -f k8s/

✅ Completed Features

1. Core Application

  • FastAPI backend with async support
  • Kubernetes/OpenShift API integration
  • Prometheus metrics collection
  • Resource validation with Red Hat best practices
  • Real-time cluster status dashboard

2. Smart Resource Analysis

  • Problem identification for namespaces with resource issues
  • Detailed pod and container analysis
  • Modal-based detailed view with recommendations
  • Issue categorization (missing requests, missing limits, wrong ratios)
  • Clear recommendations for each problem

3. UI/UX

  • Pragmatic dashboard with single view
  • Modal-based detailed analysis
  • Problem Summary table showing namespace issues
  • Professional interface without browser alerts
  • Responsive design with Bootstrap
  • Real-time data updates

4. Deployment & Infrastructure

  • Cluster-agnostic deployment
  • SSL/TLS support with fallback
  • RBAC configuration
  • Rolling update strategy
  • Route exposure for internet access
  • Docker Hub image publishing

5. Documentation & Localization

  • Complete translation from Portuguese to English
  • All comments, docstrings, and strings translated
  • README.md, DOCUMENTATION.md, AIAgents-Support.md in English
  • Clean documentation structure with only current files

🔧 Technical Implementation Details

Key Files Modified

  • app/core/kubernetes_client.py - SSL fallback for cluster compatibility
  • app/core/prometheus_client.py - ServiceAccount token authentication
  • app/services/validation_service.py - Enhanced resource validation engine
  • app/static/index.html - Pragmatic dashboard with modal-based analysis
  • app/models/resource_models.py - Updated models for container data structure
  • k8s/deployment.yaml - Cluster-agnostic security context
  • k8s/route.yaml - Dynamic hostname generation

Critical Fixes Applied

  1. SSL Connection: Fallback to disable SSL verification when CA cert is empty
  2. SCC Compatibility: Removed hardcoded UIDs, let OpenShift assign them
  3. Route Agnostic: Removed hardcoded hostname, let OpenShift generate it
  4. Image Pull: Docker Hub secret configuration
  5. Prometheus Integration: ServiceAccount token authentication
  6. Data Structure Fix: Updated PodResource model to handle container dictionaries
  7. Validation Engine: Fixed container resource access in validation_service.py
  8. UI/UX: Replaced browser alerts with professional modals

🐛 Known Issues

1. Historical Analysis Data

Status: ⚠️ SHOWING ZEROS Issue: Prometheus queries return zero values for CPU/memory usage Location: app/services/historical_analysis.py Impact: Historical analysis appears empty Next Steps: Debug PromQL queries and metric availability

2. Export Functionality

Status: ⚠️ NEEDS TESTING Issue: Export functionality needs validation with current implementation Location: app/services/report_service.py Impact: Users may not get proper export files Next Steps: Test and fix file download mechanism

📋 Roadmap & Next Steps

🎯 PRAGMATIC ROADMAP - Resource Governance Focus

Core Mission: List projects without requests/limits + provide smart recommendations based on historical analysis + VPA integration

Phase 1: Enhanced Validation & Categorization (IN PROGRESS 🔄)

1.1 Smart Resource Detection

  • Enhanced Validation Engine

    • Better categorization of resource issues (missing requests, missing limits, wrong ratios)
    • Severity scoring based on impact and risk
    • Detailed analysis of pod and container resource configurations

  • Workload Analysis System

    • Problem Identification: Namespaces with resource configuration issues
    • Detailed Analysis: Pod-by-pod breakdown with container details
    • Issue Categorization: Missing requests, missing limits, wrong ratios
    • Recommendations: Clear guidance on how to fix each issue

1.2 Historical Analysis Integration

  • Smart Historical Analysis
    • Use historical data to suggest realistic requests/limits
    • Calculate P95/P99 percentiles for recommendations
    • Identify seasonal patterns and trends
    • Flag workloads with insufficient historical data

Phase 2: Smart Recommendations Engine (SHORT TERM - 2-3 weeks)

2.1 Recommendation Dashboard

  • Dedicated Recommendations Section
    • Replace generic "VPA Recommendations" with "Smart Recommendations"
    • Show actionable insights with priority levels
    • Display estimated impact of changes
    • Group by namespace and severity

2.2 Recommendation Types

  • Resource Configuration Recommendations

    • "Add CPU requests: 200m (based on 7-day P95 usage)"
    • "Increase memory limits: 512Mi (current usage peaks at 400Mi)"
    • "Fix CPU ratio: 3:1 instead of 5:1 (current: 500m limit, 100m request)"

  • VPA Activation Recommendations

    • "Activate VPA for new workload 'example' (insufficient historical data)"
    • "Enable VPA for outlier workload 'high-cpu-app' (unpredictable usage patterns)"

2.3 Priority Scoring System

  • Impact-Based Prioritization
    • Critical: Missing limits on high-resource workloads
    • High: Missing requests on production workloads
    • Medium: Suboptimal ratios on established workloads
    • Low: New workloads needing VPA activation

Phase 3: VPA Integration & Automation (MEDIUM TERM - 3-4 weeks)

3.1 VPA Detection & Management

  • VPA Status Detection
    • Detect existing VPAs in cluster
    • Show VPA health and status
    • Display current VPA recommendations
    • Compare VPA suggestions with current settings

3.2 Smart VPA Activation

  • Automatic VPA Suggestions
    • Suggest VPA activation for new workloads (< 7 days)
    • Recommend VPA for outlier workloads
    • Provide VPA YAML configurations
    • Show estimated benefits of VPA activation

3.3 VPA Recommendation Integration

  • VPA Data Integration
    • Fetch VPA recommendations from cluster
    • Compare VPA suggestions with historical analysis
    • Show confidence levels for recommendations
    • Display VPA update modes and policies

Phase 4: Action Planning & Implementation (LONG TERM - 4-6 weeks)

4.1 Action Plan Generation

  • Step-by-Step Action Plans
    • Generate specific kubectl/oc commands
    • Show before/after resource configurations
    • Estimate implementation time and effort
    • Provide rollback procedures

4.2 Implementation Tracking

  • Progress Monitoring
    • Track which recommendations have been implemented
    • Show improvement metrics after changes
    • Alert on new issues or regressions
    • Generate implementation reports

4.3 Advanced Analytics

  • Cost Optimization Insights
    • Show potential cost savings from recommendations
    • Identify over-provisioned resources
    • Suggest right-sizing opportunities
    • Display resource utilization trends

Phase 5: Enterprise Features (FUTURE - 6+ weeks)

5.1 Advanced Governance

  • Policy Enforcement
    • Custom resource policies per namespace
    • Automated compliance checking
    • Policy violation alerts
    • Governance reporting

5.2 Multi-Cluster Support

  • Cross-Cluster Analysis
    • Compare resource usage across clusters
    • Centralized recommendation management
    • Cross-cluster best practices
    • Unified reporting

🎯 IMMEDIATE NEXT STEPS (This Week)

Priority 1: Enhanced Validation Engine

  1. Improve Resource Detection

    • Better categorization of missing requests/limits
    • Add workload age detection
    • Implement severity scoring

  2. Smart Categorization

    • New workloads (< 7 days) → VPA candidates
    • Established workloads (> 7 days) → Historical analysis
    • Outlier workloads → Special attention needed

Priority 2: Recommendation Dashboard

  1. Create Recommendations Section

    • Replace generic VPA section
    • Show actionable insights
    • Display priority levels

  2. Historical Analysis Integration

    • Use Prometheus data for recommendations
    • Calculate realistic resource suggestions
    • Show confidence levels

Priority 3: VPA Integration

  1. VPA Detection

    • Find existing VPAs in cluster
    • Show VPA status and health
    • Display current recommendations

  2. Smart VPA Suggestions

    • Identify VPA candidates
    • Generate VPA configurations
    • Show estimated benefits

🔍 Development Guidelines

Code Standards

  • Language: English only (no Portuguese)
  • Comments: Comprehensive docstrings
  • Error Handling: Proper exception handling with logging
  • Testing: Use Playwright for UI testing

Git Workflow

  • Commits: Descriptive messages without emojis
  • Branches: Feature branches for major changes
  • Releases: Tag stable versions

Deployment Checklist

  1. Test in development environment
  2. Build and push Docker image
  3. Deploy to test cluster
  4. Verify all functionality
  5. Deploy to production
  6. Update documentation

🛠️ Troubleshooting Guide

Common Issues

  1. SSL Certificate Errors: Check kubernetes_client.py fallback logic
  2. SCC Permission Denied: Verify deployment.yaml security context
  3. Image Pull Errors: Check Docker Hub secret configuration
  4. Route Not Accessible: Verify route hostname generation
  5. Prometheus Connection: Check ServiceAccount token and RBAC

Debug Commands

# Check pod logs
oc logs -f deployment/resource-governance -n resource-governance

# Check service status
oc get svc -n resource-governance

# Check route
oc get route -n resource-governance

# Test API
curl -k https://<route-url>/api/v1/health

# Test cluster status
curl -k https://<route-url>/api/v1/cluster/status

# Check deployment status
oc rollout status deployment/resource-governance -n resource-governance

📞 Support Information

Key Contacts

Resources

  • Main Documentation: README.md
  • Documentation Index: DOCUMENTATION.md
  • AI Agents Support: AIAgents-Support.md (this file)
  • Deployment Scripts: scripts/ directory
  • Kubernetes Manifests: k8s/ directory

🎯 Current Session Context

Last Action: Implemented modal-based detailed analysis and professional interface Current Focus: Enhanced validation engine with detailed pod/container analysis Next Priority: Implement smart recommendations dashboard and VPA integration Status: Phase 1 in progress - Enhanced Validation & Categorization partially completed

Recent Achievements:

  • ✅ Modal-based detailed analysis for namespace problems
  • ✅ Professional interface without browser alerts
  • ✅ Problem Summary table with namespace issues
  • ✅ Detailed pod and container analysis with recommendations
  • ✅ Clear issue categorization and recommendations

Note: This file should be updated after each significant change to maintain project context for AI agents.

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