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README.md
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# Agentic Reliability Framework (ARF) v4
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|--------|------|-------------|
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| `GET` | `/api/health` | Health check |
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| `GET` | `/api/v1/get_risk` | Current system risk (demo) |
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| `GET` | `/api/v1/history` | Recent decisions |
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| `POST` | `/api/v1/incidents/evaluate` | Evaluate an incident (placeholder) |
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| `POST` | `/api/v1/feedback` | Record outcome of a decision |
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- **Gradio dashboard**: [https://a-r-f-agentic-reliability-framework-api.hf.space/](https://a-r-f-agentic-reliability-framework-api.hf.space/)
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##
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```
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```
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📚 About ARF
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------------
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# Agentic Reliability Framework (ARF) v4.2.0 – AI Reliability & Self-Healing Control Plane
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**Problem:** Most AI‑driven governance systems fail silently in production, leading to outages, security breaches, and compliance violations.
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**Solution:** ARF turns probabilistic AI into deterministic, auditable action using Bayesian inference, semantic memory, and deterministic probability thresholds (DPT).
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**Outcome:** Reduce MTTR by up to 85% with self‑healing systems, backed by fully explainable risk scores.
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---
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## 🚀 Start Here
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|--|--|
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| **📚 API Docs** | [https://a-r-f-agentic-reliability-framework-api.hf.space/api/docs](https://a-r-f-agentic-reliability-framework-api.hf.space/api/docs) |
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| **🧪 Live Demo** | [Gradio Dashboard](https://a-r-f-agentic-reliability-framework-api.hf.space/) |
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| **📦 GitHub** | [github.com/arf-foundation/agentic-reliability-framework](https://github.com/arf-foundation/agentic-reliability-framework) |
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| **📅 Book a Call** | [Calendly](https://calendly.com/petter2025us/30min) |
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---
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## 🔍 Quick Example
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```python
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import requests
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response = requests.post(
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"https://a-r-f-agentic-reliability-framework-api.hf.space/api/v1/incidents/evaluate",
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json={
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"service_name": "payment-gateway",
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"event_type": "latency_spike",
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"severity": "high",
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"metrics": {"latency_p99": 350, "error_rate": 0.12}
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}
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)
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print(response.json())
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```
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The response includes a full HealingIntent with:
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* risk\_score: calibrated failure probability
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* risk\_factors: additive contributions from conjugate prior, hyperprior, and HMC
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* recommended\_action: approve, deny, or escalate
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* decision\_trace: expected losses and variance
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🧠 Key Capabilities
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-------------------
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* **Bayesian Risk Scoring** – Conjugate priors + HMC for calibrated uncertainty.
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* **Semantic Memory** – FAISS‑based retrieval of similar past incidents.
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* **DPT Thresholds** – Deterministic approve/deny/escalate (0.2/0.8).
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* **Multi‑Agent Orchestration** – Anomaly detection, root cause, forecasting.
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📊 Architecture
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---------------
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```text
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User Request → Policy Evaluation → Cost Estimation → Risk Scoring
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↓
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HealingIntent ← Decision (Bayesian Loss)
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```
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All decisions are immutable, signed, and fully traceable via ancestor\_chain and infrastructure\_intent fields.
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🔧 Local Development
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--------------------
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```bash
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docker build -t arf-api .
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docker run -p 7860:7860 arf-api
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```
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Then open [http://localhost:7860](http://localhost:7860/) for the Gradio UI and [http://localhost:7860/api/docs](http://localhost:7860/api/docs) for the API.
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📚 About ARF
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------------
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