| --- |
| 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. |
