Stop guessing. Govern every AI decision.

ARF is the only governance layer that turns probabilistic AI into deterministic, auditable action – reducing MTTR by up to 85%* and ensuring compliance in regulated environments.

🔥 Enterprise early access: Only 50 spots left before price increase.

⚡ Includes 1,000 free evaluations/month – no commitment.

★★★★★ (Rated 5/5 by early users)
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* MTTR reduction based on internal benchmarks with simulated incidents.

⚠️ Problem

AI systems fail silently in production, creating costly, untraceable operational risk and compliance exposure.

🔧 Solution

ARF introduces a deterministic governance layer that evaluates, constrains, and logs AI decisions using probabilistic risk modeling and enforced execution policies.

📈 Outcome

Improved operational reliability, reduced incident resolution time, and full auditability of autonomous actions.

* Estimates based on industry studies and ARF internal testing. Actual results may vary.

How ARF Works

Bayesian risk fusion → Expected loss minimisation → Approve/Deny/Escalate

Key Capabilities

Bayesian Risk Scoring

Conjugate priors + hyperpriors + HMC for calibrated uncertainty.

Uses conjugate Beta priors per action category for fast online updates, optional hierarchical hyperpriors (Pyro) to share strength across categories, and an offline HMC logistic regression model that learns complex patterns (time of day, user role, environment). The final risk is a weighted average, with weights determined by the amount of historical data.

Semantic Memory

FAISS‑based retrieval of similar past incidents.

Stores incident embeddings in a FAISS index for fast similarity search. When a new incident occurs, ARF retrieves the most similar past incidents and their outcomes to inform the current risk assessment.

Expected Loss Minimisation

Bayesian fusion + CVaR for approve/deny/escalate.

Combines conjugate priors (online), hyperpriors (hierarchical), and HMC (offline) into a weighted risk score. Chooses the action that minimises expected loss, optionally using Conditional Value at Risk (CVaR) to account for tail risk. This replaces old fixed thresholds (0.2/0.8) with a data‑driven decision boundary.

Multi‑Agent Orchestration

Anomaly detection, root cause, forecasting.

Coordinates multiple agents to detect anomalies, find root causes, and forecast future reliability. Each agent specialises in a different aspect of the infrastructure, and they collaborate to form a comprehensive picture.

Why SRE teams switch to ARF Enterprise

Audit‑ready logs

Every decision recorded for compliance (SOC2, ISO).

99.9% uptime SLA

Guaranteed by our control plane.

24/7 priority support

With < 15 min response time.

Start for free, scale with confidence

$0
OSS / Free tier
  • ✓ 1,000 evaluations/month
  • ✓ Community support
$99/mo
Pro
  • ✓ 10,000 evaluations/month
  • ✓ Email support, audit logs
$999
Enterprise
  • ✓ Unlimited + SSO + SLA
  • ✓ Custom pricing – contact sales

Try It Now

curl -X POST https://a-r-f-agentic-reliability-framework-api.hf.space/v1/incidents/evaluate \
  -H "Content-Type: application/json" \
  -d '{"service_name":"api","event_type":"latency","severity":"high","metrics":{"latency_ms":450}}'

Returns a full HealingIntent with Bayesian‑fused risk score, risk factors (conjugate / hyperprior / HMC), and a recommended action based on expected loss minimisation.

🎁 Refer a friend – both get 500 extra free evaluations!

Ecosystem Overview

Research

Mathematical foundations of hybrid Bayesian inference

Published papers and collaborations with academic institutions on Bayesian methods for reliability engineering. Our research focuses on scalable inference for cloud infrastructure.

OSS Engine

Core Bayesian models, memory, and governance loop

The heart of ARF – implements conjugate priors, HMC sampling, and the semantic memory graph. Fully open‑source under Apache 2.0.

API Control Plane

FastAPI service exposing the framework

Production‑ready REST API with automatic docs, rate limiting, and CORS. Serves as the bridge between the core engine and frontend applications.

Frontend UI

Next.js dashboard for visualizing risk

Interactive dashboard built with Next.js and Tailwind CSS. Features real‑time risk charts, memory statistics, and the incident evaluation form you're using now.

Enterprise

Advanced compliance, audit trails, and support

For organizations requiring SLAs, SSO, and advanced audit capabilities. Includes priority support and custom integrations.

Live Demos

Repository Links