ObserveX — The AI Reliability Engineer that explains incidents before users notice them.
This repository contains a working prototype developed for the LOOP Hackathon.
ObserveX demonstrates the concept of an AI-driven observability and reliability platform designed to simplify incident detection, analysis, and resolution.
This version focuses on:
- Demonstrating architecture
- AI-assisted incident understanding
- Real-time observability workflows
- Hackathon-scale implementation
Modern production systems generate massive telemetry data, yet engineers still spend hours diagnosing incidents manually.
ObserveX transforms traditional monitoring into intelligent observability by:
- Collecting metrics, logs, and traces
- Detecting anomalies automatically
- Generating AI-powered incident explanations
- Visualizing failure propagation
- Translating technical issues into business impact
Instead of dashboards that only display data, ObserveX interprets system behavior.
Production systems face:
- 200+ incidents/month on average
- ~4 hour Mean Time To Resolution (MTTR)
- Reactive monitoring (alerts after failure)
- Complex debugging across tools
ObserveX aims to:
✅ Detect issues early
✅ Explain root causes automatically
✅ Reduce debugging effort
✅ Improve system reliability visibility
Telemetry Sources ↓ Telemetry Ingestion (Edge Functions) ↓ Database + Realtime Engine ↓ AI Correlation & Analysis ↓ Incident Story Engine ↓ ObserveX Dashboard
Architecture Type:
- Event-driven
- Serverless backend
- Realtime streaming UI
- Explainable AI layer
- React (Vite)
- TypeScript
- Tailwind CSS
- shadcn/ui
- Framer Motion
- Recharts (metrics visualization)
- React Flow (service maps)
- PostgreSQL Database
- Edge Functions
- Realtime Subscriptions
- Authentication
- Gemini / LLM API
- Correlation Engine
- Root Cause Analysis pipeline
- Metrics
- Logs
- Distributed Traces
- Critical Path Detection
- Error Propagation Visualization
- Live system health dashboard
- CPU, memory, latency, error metrics
- Service dependency visualization
- Root cause explanation
- Confidence scoring
- Evidence-backed reasoning
- Suggested remediation steps
Auto-generated timeline showing:
Change detected → Latency spike → Errors propagate → Alert triggered → AI analysis
- Waterfall trace visualization
- Latency breakdown
- Critical path detection
- Trace sampling strategy
- Automated incident creation
- Predictive failure detection
- Risk scoring
- MTTR saved estimation
- Cost impact analysis
- Reliability summary reports
| Module | Description |
|---|---|
| Dashboard | System health overview |
| Service Map | Dependency visualization |
| Metrics | Time-series analytics |
| Logs | Structured log viewer |
| Traces | Distributed tracing UI |
| Incidents | Incident lifecycle management |
| Incident Story | AI-generated narrative debugging |
| Alerts | Alert rules & status |
| Executive Summary | AI reliability reports |
- Supabase Authentication
- Role-Based Access Control (Admin / Viewer)
- Row-Level Security (RLS)
- API Key isolation
- Audit logging
ObserveX uses database change streams to push updates instantly:
Database Event → Realtime Channel → UI Update
No polling required.
Telemetry Signals ↓ Correlation Engine ↓ Context Builder ↓ LLM Analysis ↓ Explainable Incident Output
AI decisions are supported by:
- correlated metrics
- trace evidence
- timestamp validation
A controlled simulation demonstrating:
- System anomaly
- Incident creation
- AI analysis
- Failure visualization
- Suggested fixes
Designed specifically for hackathon demonstrations.
This prototype simulates telemetry ingestion and focuses on:
- UX validation
- AI reasoning workflows
- Observability concepts
- Architectural feasibility
Production-scale ingestion (Kafka/OpenTelemetry) is conceptual.
- OpenTelemetry integration
- Kubernetes monitoring agents
- Autonomous remediation engine
- Streaming pipeline (Kafka/Flink)
- Multi-region deployment
Built as part of the LOOP Hackathon innovation challenge.
Prototype for educational and hackathon demonstration purposes.
ObserveX reimagines observability as:
From monitoring systems → to understanding systems.