title: Agentic Reliability Framework
emoji: 🧠
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 4.44.1
app_file: app.py
pinned: false
license: mit
short_description: AI-powered reliability with multi-agent anomaly detection
🧠 Agentic Reliability Framework
AI-Powered System Reliability with Multi-Agent Anomaly Detection & Auto-Healing
🚀 Live Demo
Try it now! Enter system telemetry data and watch specialized AI agents analyze, diagnose, and recommend healing actions in real-time.
🎯 What It Does
This framework transforms traditional monitoring into autonomous reliability engineering:
- 🤖 Multi-Agent AI Analysis: Specialized agents work together to detect and diagnose issues
- 🔧 Automated Healing: Policy-based auto-remediation for common failures
- 💰 Business Impact: Real-time revenue and user impact calculations
- 📚 Learning System: FAISS-powered memory learns from every incident
- ⚡ Production Ready: Circuit breakers, adaptive thresholds, enterprise features
🛠️ Quick Start
1. Select a Service
Choose from: api-service, auth-service, payment-service, database, cache-service
2. Adjust Metrics
- Latency P99: Alert threshold >150ms (adaptive)
- Error Rate: Alert threshold >0.05 (5%)
- Throughput: Current requests per second
- CPU/Memory: Utilization (0.0-1.0 scale)
3. Submit & Analyze
Click "Submit Telemetry Event" to see AI agents in action!
📊 Example Test Cases
🚨 Critical Failure
Component: api-service Latency: 800ms Error Rate: 0.25 CPU: 0.95 Memory: 0.90
text Expected: CRITICAL severity, circuit_breaker + scale_out actions
⚠️ Performance Issue
Component: auth-service Latency: 350ms Error Rate: 0.08 CPU: 0.75 Memory: 0.65
text Expected: HIGH severity, traffic_shift action
✅ Normal Operation
Component: payment-service Latency: 120ms Error Rate: 0.02 CPU: 0.45 Memory: 0.35
text Expected: NORMAL status, no actions needed
🔧 Technical Features
Multi-Agent Architecture
- 🕵️ Detective Agent: Anomaly detection & pattern recognition
- 🔍 Diagnostician Agent: Root cause analysis & investigation
- 🤖 Orchestration Manager: Coordinates all agents in parallel
Smart Detection
- Adaptive thresholds that learn from your environment
- Multi-dimensional anomaly scoring (0-100% confidence)
- Correlation analysis across metrics
- FAISS vector memory for incident similarity
Business Intelligence
- Real-time revenue impact calculations
- User impact estimation
- Severity classification (LOW, MEDIUM, HIGH, CRITICAL)
🎮 Try These Scenarios
Test 1: Resource Exhaustion
Set CPU to 0.95 and Memory to 0.95 - watch scale_out actions trigger
Test 2: High Latency + Errors
Set Latency to 500ms and Error Rate to 0.15 - see circuit breaker activation
Test 3: Gradual Degradation
Start with normal values and slowly increase latency/errors to see adaptive thresholds
🚨 Default Alert Thresholds
| Metric | Warning | Critical |
|---|---|---|
| Latency P99 | >150ms | >300ms |
| Error Rate | >0.05 | >0.15 |
| CPU Utilization | >0.8 | >0.9 |
| Memory Utilization | >0.8 | >0.9 |
🔮 Roadmap
- Predictive anomaly detection
- Multi-cloud coordination
- Advanced root cause analysis
- Automated runbook execution
- Team learning and knowledge transfer
💡 Why This Matters
"The most reliable system is the one that fixes itself before anyone notices there was a problem."
This framework represents the evolution from reactive monitoring to proactive, autonomous reliability engineering.
🛠️ Technical Stack
- Backend: Python, FastAPI, Sentence Transformers
- AI/ML: FAISS, Hugging Face, Custom Agents
- Frontend: Gradio
- Storage: FAISS vector database, JSON metadata
Built with ❤️ by Juan Petter
AI Infrastructure Engineer | Building Self-Healing Agentic Systems