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README.md

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+<p align="center">
+  <img src="https://dummyimage.com/1200x260/000/fff&text=AGENTIC+RELIABILITY+FRAMEWORK" width="100%" alt="Agentic Reliability Framework Banner" />
+</p>
+
+<h1 align="center">⚙️ Agentic Reliability Framework</h1>
+
+<p align="center">
+  <strong>Adaptive anomaly detection + policy-driven self-healing for AI systems</strong><br>
+  Minimal, fast, and production-focused.
+</p>
+
+<p align="center">
+  <a href="https://www.python.org/"><img src="https://img.shields.io/badge/python-3.10+-blue" alt="Python 3.10+"></a>
+  <a href="#"><img src="https://img.shields.io/badge/status-MVP-green" alt="Status: MVP"></a>
+  <a href="#"><img src="https://img.shields.io/badge/license-MIT-lightgrey" alt="License: MIT"></a>
+</p>
+
+## 🧠 Agentic Reliability Framework
+
+**Autonomous Reliability Engineering for Production AI Systems**
+
+Transform reactive monitoring into proactive, self-healing reliability. The Agentic Reliability Framework (ARF) is a production-grade, multi-agent system that detects, diagnoses, predicts, and resolves incidents automatically in under 100ms.
+
+## ⭐ Key Features
+
+- **Real-time anomaly detection** across latency, errors, throughput & resources
+- **Root-cause analysis** with evidence correlation
+- **Predictive forecasting** (15-minute lookahead)
+- **Automated healing policies** (restart, rollback, scale, circuit break)
+- **Incident memory** with FAISS for semantic recall
+- **Security hardened** (all CVEs patched)
+- **Thread-safe, async, process-pooled architecture**
+- **Sub-100ms end-to-end latency** (p50)
+
+## 🔐 Security Hardening (v2.0)
+
+| CVE | Severity | Component | Status |
+|-----|----------|-----------|--------|
+| CVE-2025-23042 | 9.1 | Gradio Path Traversal | ✅ Patched |
+| CVE-2025-48889 | 7.5 | Gradio SVG DOS | ✅ Patched |
+| CVE-2025-5320 | 6.5 | Gradio File Override | ✅ Patched |
+| CVE-2023-32681 | 6.1 | Requests Credential Leak | ✅ Patched |
+| CVE-2024-47081 | 5.3 | Requests .netrc Leak | ✅ Patched |
+
+### Additional Hardening
+
+- SHA-256 hashing everywhere (no MD5)
+- Pydantic v2 input validation
+- Rate limiting (60 req/min/user)
+- Atomic operations w/ thread-safe FAISS single-writer pattern
+- Lock-free reads for high throughput
+
+## ⚡ Lock-Free Reads for High Throughput
+
+By restructuring the internal memory stores around lock-free, single-writer / multi-reader semantics, the framework delivers deterministic concurrency without blocking. This removes tail-latency spikes and keeps event flows smooth even under burst load.
+
+### Performance Impact
+
+| Metric | Before | After | Δ |
+|--------|--------|-------|---|
+| Event Processing (p50) | ~350ms | ~100ms | ⚡ 71% faster |
+| Event Processing (p99) | ~800ms | ~250ms | ⚡ 69% faster |
+| Agent Orchestration | Sequential | Parallel | 3× throughput |
+| Memory Behavior | Growing | Stable / Bounded | 0 leaks |
+
+## 🧩 Architecture Overview
+
+### System Flow
+
+```
+Your Production System
+(APIs, Databases, Microservices)
+           ↓
+  Agentic Reliability Core
+  Detect → Diagnose → Predict
+           ↓
+        Agents:
+  🕵️ Detective Agent – Anomaly detection
+  🔍 Diagnostician Agent – Root cause analysis
+  🔮 Predictive Agent – Forecasting / risk estimation
+           ↓
+    Policy Engine (Auto-Healing)
+           ↓
+    Healing Actions:
+    • Restart
+    • Scale
+    • Rollback
+    • Circuit-break
+```
+
+## 🏗️ Core Framework Components
+
+### Web Framework & UI
+
+- **Gradio 5.50+** - High-performance async web framework serving both API layer and interactive observability dashboard (localhost:7860)
+- **Python 3.10+** - Core implementation with asynchronous, thread-safe architecture
+
+### AI/ML Stack
+
+- **FAISS-CPU 1.13.0** - Facebook AI Similarity Search for persistent incident memory and vector operations
+- **SentenceTransformers 5.1.1** - Neural embedding framework using MiniLM models from Hugging Face Hub for semantic analysis
+- **NumPy 1.26.4** - Numerical computing foundation for vector operations and data processing
+
+### Data & HTTP Layer
+
+- **Pydantic 2.11+** - Type-safe data modeling with frozen models for immutability and runtime validation
+- **Requests 2.32.5** - HTTP client library for external API communication (security patched)
+
+### Reliability & Resilience
+
+- **CircuitBreaker 2.0+** - Circuit breaker pattern implementation for fault tolerance and cascading failure prevention
+- **AtomicWrites 1.4.1** - Atomic file operations ensuring data consistency and durability
+
+## 🎯 Architecture Pattern
+
+ARF implements a **Multi-Agent Orchestration Pattern** with three specialized agents:
+
+- **Detective Agent** - Anomaly detection
+- **Diagnostician Agent** - Root cause analysis
+- **Predictive Agent** - Future risk forecasting
+
+All agents run in **parallel** (not sequential) for **3× throughput improvement**.
+
+### ⚡ Performance Features
+
+- Native async handlers (no event loop overhead)
+- Thread-safe single-writer/multi-reader pattern for FAISS
+- RLock-protected policy evaluation
+- Queue-based writes to prevent race conditions
+- Sub-100ms p50 latency at 100+ events/second
+
+The framework combines **Gradio** for the web/UI layer, **FAISS** for vector memory, and **SentenceTransformers** for semantic analysis, all orchestrated through a custom multi-agent Python architecture designed for production reliability.
+
+## 🧪 The Three Agents
+
+### 🕵️ Detective Agent — Anomaly Detection
+
+Real-time vector embeddings + adaptive thresholds to surface deviations before they cascade.
+
+- Adaptive multi-metric scoring
+- CPU/mem resource anomaly detection
+- Latency & error spike detection
+- Confidence scoring (0–1)
+
+### 🔍 Diagnostician Agent (Root Cause Analysis)
+
+Identifies patterns such as:
+
+- DB connection pool exhaustion
+- Dependency timeouts
+- Resource saturation
+- App-layer regressions
+- Misconfigurations
+
+### 🔮 Predictive Agent (Forecasting)
+
+- 15-minute risk projection
+- Trend analysis
+- Time-to-failure estimates
+- Risk levels: low → critical
+
+## 🚀 Quick Start
+
+### 1. Clone
+
+```bash
+git clone https://github.com/petterjuan/agentic-reliability-framework.git
+cd agentic-reliability-framework
+```
+
+### 2. Create environment
+
+```bash
+python3.10 -m venv venv
+source venv/bin/activate     # Windows: venv\Scripts\activate
+```
+
+### 3. Install
+
+```bash
+pip install -r requirements.txt
+```
+
+### 4. Start
+
+```bash
+python app.py
+```
+
+**UI:** http://localhost:7860
+
+## 🛠 Configuration
+
+Create `.env`:
+
+```env
+HF_TOKEN=your_token
+DATA_DIR=./data
+INDEX_FILE=data/incident_vectors.index
+LOG_LEVEL=INFO
+HOST=0.0.0.0
+PORT=7860
+```
+
+**Note:** `HF_TOKEN` is optional and used for downloading SentenceTransformer models from Hugging Face Hub.
+
+## 🧩 Custom Healing Policies
+
+```python
+custom = HealingPolicy(
+    name="custom_latency",
+    conditions=[PolicyCondition("latency_p99", "gt", 200)],
+    actions=[HealingAction.RESTART_CONTAINER, HealingAction.ALERT_TEAM],
+    priority=1,
+    cool_down_seconds=300,
+    max_executions_per_hour=5,
+)
+```
+
+## 🐳 Docker Deployment
+
+Dockerfile and docker-compose.yml included.
+
+```bash
+docker-compose up -d
+```
+
+## 📈 Performance Benchmarks
+
+**On Intel i7, 16GB RAM:**
+
+| Component | p50 | p99 |
+|-----------|-----|-----|
+| Total End-to-End | ~100ms | ~250ms |
+| Policy Engine | 19ms | 38ms |
+| Vector Encoding | 15ms | 30ms |
+
+**Stable memory:** ~250MB  
+**Throughput:** 100+ events/sec
+
+## 🧪 Testing
+
+### Production Dependencies
+
+```bash
+pip install -r requirements.txt
+```
+
+### Development Dependencies
+
+```bash
+pip install pytest pytest-asyncio pytest-cov pytest-mock black ruff mypy
+```
+
+### Run Tests
+
+```bash
+pytest tests/ -v --cov
+```
+
+**Coverage:** 87%
+
+Includes:
+- Unit tests
+- Thread-safety tests
+- Stress tests
+- Integration tests
+
+### Code Quality
+
+```bash
+# Format code
+black .
+
+# Lint code
+ruff check .
+
+# Type checking
+mypy app.py
+```
+
+## 🗺 Roadmap
+
+### v2.1
+
+- Distributed FAISS
+- Prometheus / Grafana
+- Slack & PagerDuty integration
+- Custom alerting DSL
+
+### v3.0
+
+- Reinforcement learning for policy optimization
+- LSTM forecasting
+- Dependency graph neural networks
+
+## 🤝 Contributing
+
+Pull requests welcome.
+
+Please run tests before submitting.
+
+## 📬 Contact
+
+**Author:** Juan Petter (LGCY Labs)
+
+- 📧 [petter2025us@outlook.com](mailto:petter2025us@outlook.com)
+- 🔗 [linkedin.com/in/petterjuan](https://linkedin.com/in/petterjuan)
+- 📅 [Book a session](https://calendly.com/petter2025us/30min)
+
+## ⭐ Support
+
+If this project helps you:
+
+- ⭐ Star the repo
+- 🔄 Share with your network
+- 🐛 Report issues
+- 💡 Suggest features
+
+<p align="center">
+  <sub>Built with ❤️ for production reliability</sub>
+</p>