File size: 3,470 Bytes
b9214f5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 | ---
language: en
license: apache-2.0
tags:
- bayesian
- risk-scoring
- ai-reliability
- governance
- sre
---
# ARF Risk Scoring Model
A Bayesian risk scoring model for AI system reliability and failure prediction.
This model implements the core risk assessment logic from the [Agentic Reliability Framework (ARF)](https://huggingface.co/spaces/A-R-F/Agentic-Reliability-Framework-API).
## 📌 Problem
AI‑driven systems fail silently in production. Without a calibrated measure of failure probability, operations teams cannot decide whether to approve, deny, or escalate infrastructure changes.
## 🔍 Mathematical Formulation
Given a set of signals (telemetry, context), the risk score is defined as:
\[
\text{Risk}(x) = P(\text{Failure} \mid \text{Signals}, \text{Context})
\]
Internally, ARF combines:
- **Conjugate Beta priors** for per‑category online updates.
- **Hyperpriors** that share statistical strength across categories.
- **Hamiltonian Monte Carlo (HMC)** to capture complex patterns (time‑of‑day, user role, environment).
The final risk score is a weighted average of these three components, with weights determined by data availability.
## 🚀 Usage
You can use this model directly via the ARF API, or integrate the underlying Python library.
### Example with ARF API (Python)
```python
import requests
response = requests.post(
"https://a-r-f-agentic-reliability-framework-api.hf.space/api/v1/incidents/evaluate",
json={
"service_name": "payment-gateway",
"event_type": "latency_spike",
"severity": "high",
"metrics": {"latency_p99": 350, "error_rate": 0.12}
}
)
result = response.json()
print(f"Risk score: {result['risk_score']:.3f}")
print(f"Risk factors: {result['risk_factors']}")
print(f"Recommended action: {result['recommended_action']}")
```
### Example using the ARF Python package
```python
from agentic_reliability_framework.core.governance.risk_engine import RiskEngine
engine = RiskEngine()
risk, explanation, contributions = engine.calculate_risk(
intent=some_intent,
cost_estimate=100.0,
policy_violations=[]
)
print(f"Risk: {risk}")
```
📚 Links
--------
* **ARF Space**: [Agentic Reliability Framework (ARF) v4 API](https://huggingface.co/spaces/A-R-F/Agentic-Reliability-Framework-API)
* **GitHub Repository**: [arf-foundation/agentic-reliability-framework](https://github.com/arf-foundation/agentic-reliability-framework)
* **Documentation**: [API Docs](https://a-r-f-agentic-reliability-framework-api.hf.space/api/docs)
📊 Input / Output
-----------------
InputTypeDescriptionservice\_namestringName of the service being evaluatedevent\_typestringType of incident (e.g., latency\_spike)severitystringlow / medium / high / criticalmetricsdictTelemetry values (latency, error rate, CPU, etc.)OutputTypeDescriptionrisk\_scorefloatCalibrated failure probability (0–1)risk\_factorsdictAdditive contributions from conjugate, hyperprior, HMCrecommended\_actionstringapprove / deny / escalatedecision\_traceobjectExpected losses and variance
📄 License
----------
Apache 2.0 – See [LICENSE](https://github.com/arf-foundation/agentic-reliability-framework/blob/main/LICENSE) for details.
🤝 Contributing
---------------
Contributions are welcome! Please refer to the [contribution guidelines](https://github.com/arf-foundation/agentic-reliability-framework/blob/main/CONTRIBUTING.md) in the main repository. |