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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>