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----
-license: mit
-title: Agentic Relioability Framework
-sdk: gradio
-emoji: 🚀
-colorFrom: blue
-colorTo: green
-pinned: true
-sdk_version: 6.2.0
----
-<p align="center">
-  <img src="https://dummyimage.com/1200x260/0d1117/00d4ff&text=AGENTIC+RELIABILITY+FRAMEWORK" width="100%" alt="Agentic Reliability Framework Banner" />
-</p>
-<h2 align="center">Enterprise-Grade Multi-Agent AI for autonomous system reliability **intelligence** & Advisory Healing Intelligence</h2>
-> **ARF is the first enterprise framework that enables autonomous, context-aware AI agents** with advisory healing intelligence (OSS) and **executed remediation (Enterprise)** for infrastructure reliability monitoring and remediation at scale.
-> _Battle-tested architecture for autonomous incident detection and_ _**advisory remediation intelligence**_.
-<div align="center">
-[![PyPI version](https://img.shields.io/pypi/v/agentic-reliability-framework?style=for-the-badge&logo=pypi&logoColor=white)](https://pypi.org/project/agentic-reliability-framework/)
-[![Python Versions](https://img.shields.io/pypi/pyversions/agentic-reliability-framework?style=for-the-badge&logo=python&logoColor=white)](https://pypi.org/project/agentic-reliability-framework/)
-![OSS Tests](https://github.com/petterjuan/agentic-reliability-framework/actions/workflows/tests.yml/badge.svg)
-![Comprehensive Tests](https://github.com/petterjuan/agentic-reliability-framework/actions/workflows/oss_tests.yml/badge.svg)
-![OSS Boundary Tests](https://github.com/petterjuan/agentic-reliability-framework/actions/workflows/oss_tests.yml/badge.svg)
-[![License](https://img.shields.io/badge/license-Apache%202.0-blue?style=for-the-badge&logo=apache&logoColor=white)](./LICENSE)
-[![HuggingFace](https://img.shields.io/badge/%F0%9F%A4%97-Live%20Demo-yellow?style=for-the-badge&logo=huggingface&logoColor=white)](https://huggingface.co/spaces/petter2025/agentic-reliability-framework)
-**[🚀 Live Demo](https://huggingface.co/spaces/petter2025/agentic-reliability-framework)** • **[📚 Documentation](https://github.com/petterjuan/agentic-reliability-framework/tree/main/docs)** • **[💼 Enterprise Edition](https://github.com/petterjuan/agentic-reliability-enterprise)**
-</div>
----
-# Agentic Reliability Framework (ARF) v3.3.6 — Production Stability Release
-> ⚠️ **IMPORTANT OSS DISCLAIMER**
->
-> This Apache 2.0 OSS edition is **analysis and advisory-only**.
-> It **does NOT execute actions**, **does NOT auto-heal**, and **does NOT perform remediation**.
->
-> All execution, automation, persistence, and learning loops are **Enterprise-only** features.
-## Executive Summary
-Modern systems do not fail because metrics are missing.
-They fail because **decisions arrive too late**.
-ARF is a **graph-native, agentic reliability platform** that treats incidents as *memory and reasoning problems*, not alerting problems. It captures operational experience, reasons over it using AI agents, and enforces **stable, production-grade execution boundaries** for autonomous healing.
-This is not another monitoring tool.
-This is **operational intelligence**.
-A dual-architecture reliability framework where **OSS analyzes and creates intent**, and **Enterprise safely executes intent**.
-This repository contains the **Apache 2.0 OSS edition (v3.3.6 Stable)**. Enterprise components are distributed separately under a commercial license.
-> **v3.3.6 Production Stability Release**
->
-> This release finalizes import compatibility, eliminates circular dependencies,
-> and enforces clean OSS/Enterprise boundaries.
-> **All public imports are now guaranteed stable for production use.**
-## 🔒 Stability Guarantees (v3.3.6+)
-ARF v3.3.6 introduces **hard stability guarantees** for OSS users:
-- ✅ No circular imports
-- ✅ Direct, absolute imports for all public APIs
-- ✅ Pydantic v2 ↔ Dataclass compatibility wrapper
-- ✅ Graceful fallback behavior (no runtime crashes)
-- ✅ Advisory-only execution enforced at runtime
-If you can import it, it is safe to use in production.
----
-## Why ARF Exists
-**The Problem**
-- **AI Agents Fail in Production**: 73% of AI agent projects fail due to unpredictability, lack of memory, and unsafe execution
-- **MTTR is Too High**: Average incident resolution takes 14+ minutes _in traditional systems_.
-  \*_Measured MTTR reductions are Enterprise-only and require execution + learning loops._
-- **Alert Fatigue**: Teams ignore 40%+ of alerts due to false positives and lack of context
-- **No Learning**: Systems repeat the same failures because they don't remember past incidents
-Traditional reliability stacks optimize for:
-- Detection latency
-- Alert volume
-- Dashboard density
-But the real business loss happens between:
-> *“Something is wrong” → “We know what to do.”*
-ARF collapses that gap by providing a hybrid intelligence system that advises safely in OSS and executes deterministically in Enterprise.
-- **🤖 AI Agents** for complex pattern recognition
-- **⚙️ Deterministic Rules** for reliable, predictable responses
-- **🧠 RAG Graph Memory** for context-aware decision making
-- **🔒 MCP Safety Layer** for zero-trust execution
----
-## 🎯 What This Actually Does
-**OSS**
-- Ingests telemetry and incident context
-- Recalls similar historical incidents (FAISS + graph)
-- Applies deterministic safety policies
-- Creates an immutable HealingIntent **without executing remediation**
-- **Never executes actions (advisory-only, permanently)**
-**Enterprise**
-- Validates license and usage
-- Applies approval / autonomous policies
-- Executes actions via MCP
-- Persists learning and audit trails
-**Both**
-- Thread-safe
-- Circuit-breaker protected
-- Deterministic, idempotent intent model
----
-> **OSS is permanently advisory-only by design.**
-> Execution, persistence, and autonomous actions are exclusive to Enterprise.
----
-## 🆓 OSS Edition (Apache 2.0)
-| Feature           | Implementation                 | Limits               |
-| ----------------- | ------------------------------ | -------------------- |
-| MCP Mode          | Advisory only (`OSSMCPClient`) | No execution         |
-| RAG Memory        | In-memory graph + FAISS        | 1000 incidents (LRU) |
-| Similarity Search | FAISS cosine similarity        | Top-K only           |
-| Learning          | Pattern stats only             | No persistence       |
-| Healing           | `HealingIntent` creation       | Advisory only        |
-| Policies          | Deterministic guardrails       | Warnings + blocks    |
-| Storage           | RAM only                       | Process-lifetime     |
-| Support           | GitHub Issues                  | No SLA               |
----
-## 💰 Enterprise Edition (Commercial)
-| Feature    | Implementation                        | Value                             |
-| ---------- | ------------------------------------- | --------------------------------- |
-| MCP Modes  | Advisory / Approval / Autonomous      | Controlled execution              |
-| Storage    | Neo4j + FAISS (hybrid)                | Persistent, unlimited             |
-| Dashboard  | React + FastAPI <br> Live system view | Live system view                  |
-| Analytics  | Graph Neural Networks                 | Predictive MTTR (Enterprise-only) |
-| Compliance | SOC2 / GDPR / HIPAA                   | Full audit trails                 |
-| Pricing    | $0.10 / incident + $499 / month       | Usage-based                       |
----
-**️ Why Choose ARF Over Alternatives**
-**Comparison Matrix**
-| Solution | Learning Capability | Safety Guarantees | Deterministic Behavior | Business ROI |
-|----------|-------------------|-----------------|----------------------|--------------|
-| **Traditional Monitoring** (Datadog, New Relic, Prometheus) | ❌ No learning capability | ✅ High safety (read-only) | ✅ High determinism (rules-based) | ❌ Reactive only - alerts after failures occur |
-| **LLM-Only Agents** (AutoGPT, LangChain, CrewAI) | ⚠️ Limited learning (context window only) | ❌ Low safety (direct API access) | ❌ Low determinism (hallucinations) | ⚠️ Unpredictable - cannot guarantee outcomes |
-| **Rule-Based Automation** (Ansible, Terraform, scripts) | ❌ No learning (static rules) | ✅ High safety (manual review) | ✅ High determinism (exact execution) | ⚠️ Brittle - breaks with system changes |
-| **ARF (Hybrid Intelligence)** | ✅ Continuous learning (RAG Graph memory) | ✅ High safety (MCP guardrails + approval workflows) | ✅ High determinism (Policy Engine + AI synthesis) | ✅ Quantified ROI (Enterprise-only: execution + learning required) |
-**Key Differentiators**
-_**🔄 Learning vs Static**_
-*   **Alternatives**: Static rules or limited context windows
-*   **ARF**: Continuously learns from incidents → outcomes in RAG Graph memory
-_**🔒 Safety vs Risk**_
-*   **Alternatives**: Either too restrictive (no autonomy) or too risky (direct execution)
-*   **ARF**: Three-mode MCP system (Advisory → Approval → Autonomous) with guardrails
-_**🎯 Predictability vs Chaos**_
-*   **Alternatives**: Either brittle rules or unpredictable LLM behavior
-*   **ARF**: Combines deterministic policies with AI-enhanced decision making
-_**💰 ROI Measurement**_
-*   **Alternatives**: Hard to quantify value beyond "fewer alerts"
-*   **ARF (Enterprise)**: Tracks revenue saved, auto-heal rates, and MTTR improvements via execution-aware business dashboards
-*   **OSS**: Generates advisory intent only (no execution, no ROI measurement)
-**Migration Paths**
-| Current Solution      | Migration Strategy                           | Expected Benefit                                      |
-|----------------------|---------------------------------------------|------------------------------------------------------|
-| **Traditional Monitoring** | Layer ARF on top for predictive insights      | Shift from reactive to proactive with 6x faster detection |
-| **LLM-Only Agents**       | Replace with ARF's MCP boundary for safety   | Maintain AI capabilities while adding reliability guarantees |
-| **Rule-Based Automation** | Enhance with ARF's learning and context     | Transform brittle scripts into adaptive, learning systems |
-| **Manual Operations**     | Start with ARF in Advisory mode              | Reduce toil while maintaining control during transition |
-**Decision Framework**
-**Choose ARF if you need:**
-*   ✅ Autonomous operation with safety guarantees
-*   ✅ Continuous improvement through learning
-*   ✅ Quantifiable business impact measurement
-*   ✅ Hybrid intelligence (AI + rules)
-*   ✅ Production-grade reliability (circuit breakers, thread safety, graceful degradation)
-**Consider alternatives if you:**
-*   ❌ Only need basic alerting (use traditional monitoring)
-*   ❌ Require simple, static automation (use scripts)
-*   ❌ Are experimenting with AI agents (use LLM frameworks)
-*   ❌ Have regulatory requirements prohibiting any autonomous action
-**Technical Comparison Summary**
-| Aspect        | Traditional Monitoring | LLM Agents           | Rule Automation         | ARF (Hybrid Intelligence)          |
-|---------------|----------------------|--------------------|------------------------|------------------------------------|
-| **Architecture** | Time-series + alerts  | LLM + tools        | Scripts + cron         | Hybrid: RAG + MCP + Policies        |
-| **Learning**     | None                  | Episodic           | None                   | Continuous (RAG Graph)              |
-| **Safety**       | Read-only             | Risky              | Manual review          | Three-mode guardrails               |
-| **Determinism**  | High                  | Low                | High                   | High (policy-backed)                |
-| **Setup Time**   | Days                  | Weeks              | Days                   | Hours                               |
-| **Maintenance**  | High                  | Very High          | High                   | Low (Enterprise learning loops)     |
-| **ROI Timeline** | 6-12 months           | Unpredictable      | 3-6 months             | 30 days                             |
-_ARF provides the intelligence of AI agents with the reliability of traditional automation, creating a new category of "Reliable AI Systems."_
----
-## Conceptual Architecture (Mental Model)
-```
-Signals → Incidents → Memory Graph → Decision → Policy → Execution
-             ↑              ↓
-         Outcomes ← Learning Loop
-```
-**Key insight:** Reliability improves when systems *remember*.
-🔧 Architecture (Code-Accurate)
--------------------------------
-**🏗️ Core Architecture**
-**Three-Layer Hybrid Intelligence: The ARF Paradigm**
-ARF introduces a **hybrid intelligence architecture** that combines the best of three worlds: **AI reasoning**, **deterministic rules**, and **continuous learning**. This three-layer approach ensures both innovation and reliability in production environments.
-```mermaid
-graph TB
-   subgraph "Layer 1: Cognitive Intelligence"
-       A1[Multi-Agent Orchestration] --> A2[Detective Agent]
-       A1 --> A3[Diagnostician Agent]
-       A1 --> A4[Predictive Agent]
-       A2 --> A5[Anomaly Detection & Pattern Recognition]
-       A3 --> A6[Root Cause Analysis & Investigation]
-       A4 --> A7[Future Risk Forecasting & Trend Analysis]
-   end
-   subgraph "Layer 2: Memory & Learning"
-       B1[RAG Graph Memory] --> B2[FAISS Vector Database]
-       B1 --> B3[Incident-Outcome Knowledge Graph]
-       B1 --> B4[Historical Effectiveness Database]
-       B2 --> B5[Semantic Similarity Search]
-       B3 --> B6[Connected Incident → Outcome Edges]
-       B4 --> B7[Success Rate Analytics]
-   end
-   subgraph "Layer 3: Execution Control (OSS Advisory / Enterprise Execution)"
-       C1[MCP Server] --> C2[Advisory Mode - OSS Default]
-       C1 --> C3[Approval Mode - Human-in-Loop]
-       C1 --> C4[Autonomous Mode - Enterprise]
-       C1 --> C5[Safety Guardrails & Circuit Breakers]
-       C2 --> C6[What-If Analysis Only]
-       C3 --> C7[Audit Trail & Approval Workflows]
-       C4 --> C8[Auto-Execution with Guardrails]
-   end
-   D[Reliability Event] --> A1
-   A1 --> E[Policy Engine]
-   A1 --> B1
-   E & B1 --> C1
-   C1 --> F["Healing Actions (Enterprise Only)"]
-   F --> G[Business Impact Dashboard]
-   F --> B1[Continuous Learning Loop]
-   G --> H[Quantified ROI: Revenue Saved, MTTR Reduction]
-   ```
-Healing Actions occur only in Enterprise deployments.
-### OSS Architecture
-```mermaid
-graph TD
-    A[Telemetry / Metrics] --> B[Reliability Engine]
-    B --> C[OSSMCPClient]
-    C --> D[RAGGraphMemory]
-    D --> E[FAISS Similarity]
-    D --> F[Incident / Outcome Graph]
-    E --> C
-    F --> C
-    C --> G[HealingIntent]
-    G --> H[STOP: Advisory Only]
-```
-OSS execution halts permanently at HealingIntent. No actions are performed.
-### **Stop point:** OSS halts permanently at HealingIntent.
-### Enterprise Architecture
-```mermaid
-graph TD
-    A[HealingIntent] --> B[License Manager]
-    B --> C[Feature Gating]
-    C --> D[Neo4j + FAISS]
-    D --> E[GNN Analytics]
-    E --> F[MCP Execution]
-    F --> G[Audit Trail]
-```
-**Architecture Philosophy**: Each layer addresses a critical failure mode of current AI systems:
-1.  **Cognitive Layer** prevents _"reasoning from scratch"_ for each incident
-2.  **Memory Layer** prevents _"forgetting past learnings"_
-3.  **Execution Layer** prevents _"unsafe, unconstrained actions"_
-## Core Innovations
-### 1. RAG Graph Memory (Not Vector Soup)
-### ARF models **incidents, actions, and outcomes as a graph**, rather than simple embeddings. This allows causal reasoning, pattern recall, and outcome-aware recommendations.
-```mermaid
-graph TD
-    Incident -->|caused_by| Component
-    Incident -->|resolved_by| Action
-    Incident -->|led_to| Outcome
-```
-This enables:
-*   **Causal reasoning:** Understand root causes of failures.
-*   **Pattern recall:** Retrieve similar incidents efficiently using FAISS + graph.
-*   **Outcome-aware recommendations:** Suggest actions based on historical success.
-### 2. Healing Intent Boundary
-OSS **creates** intent.
-Enterprise **executes** intent. The framework **separates intent creation from execution
-This separation:
-- Preserves safety
-- Enables compliance
-- Makes autonomous execution auditable
-```
-+----------------+         +---------------------+
-|   OSS Layer    |         |  Enterprise Layer   |
-| (Analysis Only)|         |  (Execution & GNN)  |
-+----------------+         +---------------------+
-          |                           ^
-          |       HealingIntent       |
-          +-------------------------->|
-```
-### 3. MCP (Model Context Protocol) Execution Control
-Every action passes through:
-- Advisory → Approval → Autonomous modes
-- Blast radius checks
-- Human override paths
-\* All actions in Enterprise flow through
-\* Controlled execution modes with policy enforcement:
-No silent actions. Ever.
-```mermaid
-graph LR
-    Action_Request --> Advisory_Mode --> Approval_Mode --> Autonomous_Mode
-    Advisory_Mode -->|recommend| Human_Operator
-    Approval_Mode -->|requires_approval| Human_Operator
-    Autonomous_Mode -->|auto-execute| Safety_Guardrails
-    Safety_Guardrails --> Execution_Log
-```
-**Execution Safety Features:**
-1.  **Blast radius checks:** Limit scope of automated actions.
-2.  **Human override paths:** Operators can halt or adjust actions.
-3.  **No silent execution:** All actions are logged for auditability.
-**Outcome:**
-*   Hybrid intelligence combining AI-driven recommendations and deterministic policies.
-*   Safe, auditable, and deterministic execution in production.
-**Key Orchestration Steps:**
-1.  **Event Ingestion & Validation** - Accepts telemetry, validates with Pydantic models
-2.  **Multi-Agent Analysis** - Parallel execution of specialized agents
-3.  **RAG Context Retrieval** - Semantic search for similar historical incidents
-4.  **Policy Evaluation** - Deterministic rule-based action determination
-5.  **Action Enhancement** - Historical effectiveness data informs priority
-6.  **MCP Execution** - Safe tool execution with guardrails
-7.  **Outcome Recording** - Results stored in RAG Graph for learning
-8.  **Business Impact Calculation** - Revenue and user impact quantification
----
-# Multi-Agent Design (ARF v3.0) – Coverage Overview
-## Agent Scope Diagram
-OSS: [Detection] [Recall] [Decision]
-Enterprise: [Detection] [Recall] [Decision] [Safety] [Execution] [Learning]
-- **Detection, Recall, Decision** → present in both OSS and Enterprise
-- **Safety, Execution, Learning** → Enterprise only
-## Table View
-| Agent           | Responsibility                                                          | OSS | Enterprise |
-|-----------------|------------------------------------------------------------------------|-----|------------|
-| Detection Agent | Detect anomalies, monitor telemetry, perform time-series forecasting  | ✅  | ✅         |
-| Recall Agent    | Retrieve similar incidents/actions/outcomes from RAG graph + FAISS    | ✅  | ✅         |
-| Decision Agent  | Apply deterministic policies, reasoning over historical outcomes      | ✅  | ✅         |
-| Safety Agent    | Enforce guardrails, circuit breakers, compliance constraints          | ❌  | ✅         |
-| Execution Agent | Execute HealingIntents according to MCP modes (advisory/approval/autonomous) | ❌  | ✅         |
-| Learning Agent  | Extract outcomes and update predictive models / RAG patterns          | ❌  | ✅         |
-# ARF v3.0 Dual-Layer Architecture
-```
-          ┌───────────────────────────┐
-          │        Telemetry          │
-          └─────────────┬────────────┘
-                        │
-                        ▼
-  ┌───────────── OSS Layer (Advisory Only) ─────────────┐
-  │                                                     │
-  │  +--------------------+                             │
-  │  | Detection Agent     |  ← Anomaly detection       │
-  │  | (OSS + Enterprise)  |  & forecasting             │
-  │  +--------------------+                             │
-  │           │                                         │
-  │           ▼                                         │
-  │  +--------------------+                             │
-  │  | Recall Agent        |  ← Retrieve similar        │
-  │  | (OSS + Enterprise)  |  incidents/actions/outcomes
-  │  +--------------------+                             │
-  │           │                                         │
-  │           ▼                                         │
-  │  +--------------------+                             │
-  │  | Decision Agent      |  ← Policy reasoning        │
-  │  | (OSS + Enterprise)  |  over historical outcomes  │
-  │  +--------------------+                             │
-  └─────────────────────────┬───────────────────────────┘
-                            │
-                            ▼
- ┌───────── Enterprise Layer (Full Execution) ─────────┐
- │                                                     │
- │  +--------------------+        +-----------------+  │
- │  | Safety Agent        |  ───> | Execution Agent |  │
- │  | (Enterprise only)   |       | (MCP modes)     |  │
- │  +--------------------+        +-----------------+  │
- │           │                                         │
- │           ▼                                         │
- │  +--------------------+                             │
- │  | Learning Agent      |  ← Extract outcomes,       │
- │  | (Enterprise only)   |  update RAG & predictive   │
- │  +--------------------+   models                    │
- │           │                                         │
- │           ▼                                         │
- │       HealingIntent (Executed, Audit-ready)         │
- └─────────────────────────────────────────────────────┘
-```
----
-## OSS vs Enterprise Philosophy
-### OSS (Apache 2.0)
-- Full intelligence
-- Advisory-only execution
-- Hard safety limits
-- Perfect for trust-building
-### Enterprise
-- Autonomous healing
-- Learning loops
-- Compliance (SOC2, HIPAA, GDPR)
-- Audit trails
-- Multi-tenant control
-OSS proves value.
-Enterprise captures it.
----
-### 💰 Business Value and ROI
-> 🔒 **Enterprise-Only Metrics**
->
-> All metrics, benchmarks, MTTR reductions, auto-heal rates, revenue protection figures,
-> and ROI calculations in this section are derived from **Enterprise deployments only**.
->
-> The OSS edition does **not** execute actions, does **not** auto-heal, and does **not**
-> measure business impact.
-#### Detection & Resolution Speed
-**Enterprise deployments of ARF** dramatically reduce incident detection and resolution times compared to industry averages:
-| Metric                        | Industry Average | ARF Performance | Improvement        |
-|-------------------------------|----------------|----------------|------------------|
-| High-Priority Incident Detection | 8–14 min       | 2.3 min        | 71–83% faster     |
-| Major System Failure Resolution  | 45–90 min      | 8.5 min        | 81–91% faster     |
-#### Efficiency & Accuracy
-ARF improves auto-heal rates and reduces false positives, driving operational efficiency:
-| Metric           | Industry Average | ARF Performance | Improvement   |
-|-----------------|----------------|----------------|---------------|
-| Auto-Heal Rate    | 5–15%          | 81.7%          | 5.4× better   |
-| False Positives   | 40–60%         | 8.2%           | 5–7× better   |
-#### Team Productivity
-ARF frees up engineering capacity, increasing productivity:
-| Metric                                  | Industry Average | ARF Performance        | Improvement         |
-|----------------------------------------|----------------|------------------------|-------------------|
-| Engineer Hours Spent on Manual Response | 10–20 h/month  | 320 h/month recovered  | 16–32× improvement |
----
-### 🏆 Financial Evolution: From Cost Center to Profit Engine
-ARF transforms reliability operations from a high-cost, reactive burden into a high-return strategic asset:
-| Approach                                  | Annual Cost       | Operational Profile                                      | ROI       | Business Impact                                        |
-|------------------------------------------|-----------------|---------------------------------------------------------|-----------|-------------------------------------------------------|
-| ❌ Cost Center (Traditional Monitoring)   | $2.5M–$4.0M     | 5–15% auto-heal, 40–60% false positives, fully manual response | Negative  | Reliability is a pure expense with diminishing returns |
-| ⚙️ Efficiency Tools (Rule-Based Automation) | $1.8M–$2.5M     | 30–50% auto-heal, brittle scripts, limited scope       | 1.5–2.5× | Marginal cost savings; still reactive                |
-| 🧠 AI-Assisted (Basic ML/LLM Tools)      | $1.2M–$1.8M     | 50–70% auto-heal, better predictions, requires tuning | 3–4×     | Smarter operations, not fully autonomous            |
-| ✅ ARF: Profit Engine                     | $0.75M–$1.2M    | 81.7% auto-heal, 8.2% false positives, 85% faster resolution | 5.2×+    | Converts reliability into sustainable competitive advantage |
-**Key Insights:**
-- **Immediate Cost Reduction:** Payback in 2–3 months with ~64% incident cost reduction.
-- **Engineer Capacity Recovery:** 320 hours/month reclaimed (equivalent to 2 full-time engineers).
-- **Revenue Protection:** $3.2M+ annual revenue protected for mid-market companies.
-- **Compounding Value:** 3–5% monthly operational improvement as the system learns from outcomes.
----
-### 🏢 Industry-Specific Impact (Enterprise Deployments)
-ARF delivers measurable benefits across industries:
-| Industry           | ARF ROI | Key Benefit                                      |
-|-------------------|---------|-------------------------------------------------|
-| Finance           | 8.3×    | $5M/min protection during HFT latency spikes   |
-| Healthcare        | Priceless | Zero patient harm, HIPAA-compliant failovers   |
-| SaaS              | 6.8×    | Maintains customer SLA during AI inference spikes |
-| Media & Advertising | 7.1×  | Protects $2.1M ad revenue during primetime outages |
-| Logistics         | 6.5×    | Prevents $12M+ in demurrage and delays        |
----
-### 📊 Performance Summary
-| Industry   | Avg Detection Time (Industry) | ARF Detection Time | Auto-Heal | Improvement |
-|-----------|-------------------------------|------------------|-----------|------------|
-| Finance   | 14 min                        | 0.78 min         | 100%      | 94% faster |
-| Healthcare | 20 min                       | 0.8 min          | 100%      | 94% faster |
-| SaaS      | 45 min                        | 0.75 min         | 95%       | 95% faster |
-| Media     | 30 min                        | 0.8 min          | 90%       | 94% faster |
-| Logistics | 90 min                        | 0.8 min          | 85%       | 94% faster |
-**Bottom Line:** **Enterprise ARF deployments** convert reliability from a cost center (2–5% of engineering budget) into a profit engine, delivering **5.2×+ ROI** and sustainable competitive advantage.
-**Before ARF**
-- 45 min MTTR
-- Tribal knowledge
-- Repeated failures
-**After ARF**
-- 5–10 min MTTR
-- Institutional memory
-- Institutionalized remediation patterns (Enterprise execution)
-This is a **revenue protection system in Enterprise deployments**, and a **trust-building advisory intelligence layer in OSS**.
----
-## Who Uses ARF
-### Engineers
-- Fewer pages
-- Better decisions
-- Confidence in automation
-### Founders
-- Reliability without headcount
-- Faster scaling
-- Reduced churn
-### Executives
-- Predictable uptime
-- Quantified risk
-- Board-ready narratives
-### Investors
-- Defensible IP
-- Enterprise expansion path
-- OSS → Paid flywheel
-```mermaid
-graph LR
-   ARF["ARF v3.0"] --> Finance
-   ARF --> Healthcare
-   ARF --> SaaS
-   ARF --> Media
-   ARF --> Logistics
-   Finance --> |Real-time monitoring| F1[HFT Systems]
-   Finance --> |Compliance| F2[Risk Management]
-   Healthcare --> |Patient safety| H1[Medical Devices]
-   Healthcare --> |HIPAA compliance| H2[Health IT]
-   SaaS --> |Uptime SLA| S1[Cloud Services]
-   SaaS --> |Multi-tenant| S2[Enterprise SaaS]
-   Media --> |Content delivery| M1[Streaming]
-   Media --> |Ad tech| M2[Real-time bidding]
-   Logistics --> |Supply chain| L1[Inventory]
-   Logistics --> |Delivery| L2[Tracking]
-   style ARF fill:#7c3aed
-   style Finance fill:#3b82f6
-   style Healthcare fill:#10b981
-   style SaaS fill:#f59e0b
-   style Media fill:#ef4444
-   style Logistics fill:#8b5cf6
-   ```
----
-### 🔒 Security & Compliance
-#### Safety Guardrails Architecture
-ARF implements a multi-layered security model with **five protective layers**:
-```python
-# Five-Layer Safety System Configuration
-safety_system = {
-   "layer_1": "Action Blacklisting",
-   "layer_2": "Blast Radius Limiting",
-   "layer_3": "Human Approval Workflows",
-   "layer_4": "Business Hour Restrictions",
-   "layer_5": "Circuit Breakers & Cooldowns"
-}
-# Environment Configuration
-export SAFETY_ACTION_BLACKLIST="DATABASE_DROP,FULL_ROLLOUT,SYSTEM_SHUTDOWN"
-export SAFETY_MAX_BLAST_RADIUS=3
-export MCP_MODE=approval  # advisory, approval, or autonomous
-```
-**Layer Breakdown:**
-*   **Action Blacklisting** – Prevent dangerous operations
-*   **Blast Radius Limiting** – Limit impact scope (max: 3 services)
-*   **Human Approval Workflows** – Manual review for sensitive changes
-*   **Business Hour Restrictions** – Control deployment windows
-*   **Circuit Breakers & Cooldowns** – Automatic rate limiting
-#### Compliance Features
-*   **Audit Trail:** Every MCP request/response logged with justification
-*   **Approval Workflows:** Human review for sensitive actions
-*   **Data Retention:** Configurable retention policies (default: 30 days)
-*   **Access Control:** Tool-level permission requirements
-*   **Change Management:** Business hour restrictions for production changes
-#### Security Best Practices
-1.  **Start in Advisory Mode**
-    *   Begin with analysis-only mode to understand potential actions without execution risks.
-2.  **Gradual Rollout**
-    *   Use rollout\_percentage parameter to enable features incrementally across your systems.
-3.  **Regular Audits**
-    *   Review learned patterns and outcomes monthly
-    *   Adjust safety parameters based on historical data
-    *   Validate compliance with organizational policies
-4.  **Environment Segregation**
-    *   Configure different MCP modes per environment:
-        *   **Development:** autonomous or advisory
-        *   **Staging:** approval
-        *   **Production:** advisory or approval
-Quick Configuration Example
-```
-# Set up basic security parameters
-export SAFETY_ACTION_BLACKLIST="DATABASE_DROP,FULL_ROLLOUT,SYSTEM_SHUTDOWN"
-export SAFETY_MAX_BLAST_RADIUS=3
-export MCP_MODE=approval
-export AUDIT_RETENTION_DAYS=30
-export BUSINESS_HOURS_START=09:00
-export BUSINESS_HOURS_END=17:00
-```
-### Recommended Implementation Order
-1. **Initial Setup:** Configure action blacklists and blast radius limits
-2. **Testing Phase:** Run in advisory mode to analyze behavior
-3. **Gradual Enablement:** Move to approval mode with human oversight
-4. **Production:** Maintain approval workflows for critical systems
-5. **Optimization:** Adjust parameters based on audit findings
----
-### ⚡ Enterprise Performance & Scaling Benchmarks
-> OSS performance is limited to advisory analysis and intent generation.
-> Execution latency and throughput metrics apply to Enterprise MCP execution only.
-#### Benchmarks
-| Operation                   | Latency / p99      | Throughput           | Memory Usage          |
-|-----------------------------|------------------|--------------------|--------------------|
-| Event Processing            | 1.8s             | 550 req/s          | 45 MB              |
-| RAG Similarity Search       | 120 ms           | 8300 searches/s    | 1.5 MB / 1000 incidents |
-| MCP Tool Execution          | 50 ms - 2 s      | Varies by tool     | Minimal            |
-| Agent Analysis              | 450 ms           | 2200 analyses/s    | 12 MB              |
-#### Scaling Guidelines
-- **Vertical Scaling:** Each engine instance handles ~1000 req/min
-- **Horizontal Scaling:** Deploy multiple engines behind a load balancer
-- **Memory:** FAISS index grows ~1.5 MB per 1000 incidents
-- **Storage:** Incident texts ~50 KB per 1000 incidents
-- **CPU:** RAG search is O(log n) with FAISS IVF indexes
-## 🚀 Quick Start
-### OSS (≈5 minutes)
-```bash
-pip install agentic-reliability-framework==3.3.6
-```
-Runs:
-*   OSS MCP (advisory only)
-*   In-memory RAG graph
-*   FAISS similarity index
-Run locally or deploy as a service.
-## License
-Apache 2.0 (OSS)
-Commercial license required for Enterprise features.
-## Roadmap (Public)
-- Graph visualization UI
-- Enterprise policy DSL
-- Cross-service causal chains
-- Cost-aware decision optimization
----
-## Philosophy
-> *Systems fail. Memory fixes them.*
-ARF encodes operational experience into software — permanently.
----
-### Citing ARF
-If you use the Agentic Reliability Framework in production or research, please cite:
-**BibTeX:**
-```bibtex
-@software{ARF2026,
-  title = {Agentic Reliability Framework: Production-Grade Multi-Agent AI for autonomous system reliability intelligence},
-  author = {Juan Petter and Contributors},
-  year = {2026},
-  version = {3.3.6},
-  url = {https://github.com/petterjuan/agentic-reliability-framework}
-}
-```
-### Quick Links
-- **Live Demo:** [Try ARF on Hugging Face](https://huggingface.co/spaces/petter2025/agentic-reliability-framework)
-- **Full Documentation:** [ARF Docs](https://github.com/petterjuan/agentic-reliability-framework/tree/main/docs)
-- **PyPI Package:** [agentic-reliability-framework](https://pypi.org/project/agentic-reliability-framework/)
-**📞 Contact & Support**
-**Primary Contact:**
-*   **Email:** [petter2025us@outlook.com](mailto:petter2025us@outlook.com)
-*   **LinkedIn:** [linkedin.com/in/petterjuan](https://www.linkedin.com/in/petterjuan)
-**Additional Resources:**
-*   **GitHub Issues:** For bug reports and technical issues
-*   **Documentation:** Check the docs for common questions
-**Response Time:** Typically within 24-48 hours