Watchtower is an AI-assisted cloud observability and incident intelligence platform focused on early detection, fast diagnosis, and reliable resolution of production incidents.
Instead of static dashboards, Watchtower models how real SRE and on-call systems are designed and operated in production environments.
The system demonstrates:
- Real-time metrics ingestion and streaming
- Deterministic incident lifecycle management
- Signal-driven incident detection
- Metric, event, and deployment correlation
- AI-assisted root-cause analysis
This project is built to mirror real-world observability and reliability tooling, not toy dashboards.
Production systems fail continuously due to:
- Traffic spikes
- Memory leaks
- Slow queries
- Misconfigured deployments
- Cascading service failures
Traditional monitoring answers what is broken, not why.
Watchtower is designed to reduce Mean Time To Resolution (MTTR) by converting telemetry into incident intelligence.
| Guarantee | Implementation |
|---|---|
| Real-time visibility | WebSocket-based metric streaming |
| Explicit incident lifecycle | Finite State Machine |
| Actionable signals | Threshold + anomaly detection |
| Incident traceability | Timestamped event timelines |
| Faster diagnosis | AI-generated root-cause hypotheses |
| Failure tolerance | Backpressure handling + retries |
Event-Driven Observability Platform
Incidents follow a strict lifecycle:
Rules
- No implicit transitions
- Every state change is recorded
- Incidents are immutable once resolved
This mirrors real on-call and SRE workflows.
Metrics are ingested and streamed live:
- CPU, memory, latency, error rates
- Per-service and per-environment views
- Zero polling, push-based updates
This enables immediate detection of production anomalies.
When an incident is created, Watchtower:
- Analyzes metric deviations
- Correlates recent deployments and events
- Generates:
- Root cause hypotheses
- Debugging suggestions
- Risk assessment
AI is used as decision support, not blind automation.
Each incident maintains a structured timeline:
- Metric threshold breaches
- Deployments
- Manual annotations
- State transitions
This enables accurate post-incident analysis and learning.
Watchtower is designed assuming failures are normal:
- Metric ingestion retries
- Graceful degradation when AI services fail
- Explicit error handling instead of silent drops
This reflects production-grade reliability thinking.
Watchtower prioritizes correctness over UI coverage:
- Incident state transition tests
- Threshold breach simulations
- Streaming reconnection tests
- AI fallback behavior tests
- ✔ Distributed systems thinking
- ✔ Event-driven architecture
- ✔ State machine modeling
- ✔ Real-time data pipelines
- ✔ Meaningful AI integration
- ✔ SRE / observability domain knowledge
This project is intentionally designed to reflect real infrastructure problems, not frontend demos.
MIT License © 2026 Watchtower