A-R-F/arf-risk-scoring-model

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).

📌 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)

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

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

• GitHub Repository: arf-foundation/agentic-reliability-framework

• Documentation: 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 for details.

🤝 Contributing

Contributions are welcome! Please refer to the contribution guidelines in the main repository.