Auditable cloud governance powered by Bayesian intelligence. Build reliable, observable, and self-healing AI systems for real-world infrastructure.

Live Dashboard: ARF Dashboard

ARF is designed to make AI operations provably safe, auditable, and fully transparent. Our core mission:

• ✅ Enable provably safe AI operations in cloud, hybrid, and multi-agent environments.
• 🧮 Provide mathematically rigorous advisory engines for deterministic and probabilistic governance.
• 🌐 Build a community of engineers, researchers, and practitioners shaping next-generation reliable AI systems.
• 🔍 Offer full traceability of decisions through auditable logs, semantic memory, and hybrid Bayesian inference.

• Bayesian Risk Scoring: Conjugate priors + HMC for calibrated, probabilistic uncertainty.
• Semantic Memory: FAISS-based retrieval for context-aware decision-making.
• Deterministic Probability Thresholds (DPT): Approve (<0.2), Deny (>0.8), Escalate (0.2–0.8).
• Multi-Agent Orchestration: Automated anomaly detection, root cause analysis, forecasting.
• Policy Composability: AND/OR/NOT combinators to define complex advisory rules.
• Traceability & Audit: Each decision is fully auditable, stored, and queryable.

• OSS Demo: v4 Hugging Face Space – Interactive Bayesian risk dashboard.
• API Demo: v4 API

Example API call:

curl -X POST https://huggingface.co/spaces/A-R-F/Agentic-Reliability-Framework-API/api/v1/incidents/evaluate \
-H "Content-Type: application/json" \
-d '{"incident": {"type": "access_request", "user_role": "dev"}}'

The ARF Dashboard provides real-time, interactive governance visuals, multi-agent orchestration, and detailed risk metrics. It is designed for both technical operators and executive stakeholders to monitor, evaluate, and guide AI operations with confidence.

Key Features:

💡 Psychological Triggers:

• Color-coded risk levels for immediate visual prioritization.
• Hoverable tooltips for deep technical insights without overwhelming the interface.
• Notification badges for escalations, anomalies, or policy violations.

Access Dashboard: ARF Dashboard

We welcome developers, researchers, and AI enthusiasts to collaborate with the ARF ecosystem. Contributions accelerate reliability, expand capabilities, and improve overall AI governance.

How to Contribute:

1. Fork & Submit Pull Requests

   • Clone any repository, make improvements, and submit a PR.
   • Provide clear descriptions, references to issues, and tests where applicable.

2. Report Issues or Feature Requests

   • Use the Discussions tab for ideation, bug reporting, and feature requests.
   • Prioritize reproducibility and provide sample inputs/outputs if possible.

3. Follow Contribution Guidelines

   • Adhere to coding standards, testing requirements, and review protocols.
   • Ensure all new code is compatible with Python 3.10+, FastAPI, and Next.js environments.

4. Feedback on Dashboard & API

   • Evaluate dashboards, API outputs, and risk scoring models.
   • Suggest improvements to enhance user experience, model calibration, or observability.

Why Contribute?

• Your contributions directly impact provably safe AI operations.
• Collaborators are recognized in release notes and GitHub contributors graphs.
• Gain hands-on experience with hybrid Bayesian inference, multi-agent orchestration, and enterprise-grade AI governance.

Reach out to join, collaborate, or explore integrations:

Join us to make ARF the standard for reliable AI operations. Contribute, experiment, and shape the future of auditable, self-healing AI systems.

• License: Apache 2.0
• Full Documentation & Specifications: ARF Spec

All modules, dashboards, and APIs are open-source, fully auditable, and built with hybrid Bayesian inference and deterministic probability thresholds at their core.