π§ Agentic Reliability Framework β Live Demo
AI that detects failures before they happen. Systems that explain themselves and heal automatically. Reliability that compounds revenue.
Why this matters
Most AI systems can think. Few stay reliable under real traffic, model drift, and cascading failures. Production incidents silently erode revenue and trust. ARF is an agentic system built to see, reason, and act β reducing detection time from hours to milliseconds and recovery time from minutes to seconds.
What this demo shows
- Real-time anomaly detection powered by adaptive embeddings & FAISS
- LLM-backed root-cause explanations in plain language
- Predictive failure forecasts and time-to-failure estimates
- Policy-driven automated recovery with circuit breakers & cooldowns
How it works β simple
- Ingest signals (logs, metrics, traces, model outputs)
- Embed behavior with SentenceTransformers β FAISS index
- Detect anomalies, reason about root cause, and score risk
- Trigger automated remediation actions & persist learnings
Try the demo
Trigger anomalies, watch the Detective & Diagnostician agents, inspect FAISS memory neighbors, and see the policy engine heal the system β all in real time.
Who this is for
Engineers, SREs, founders, and platform teams who treat reliability as a strategic advantage. If uptime matters to your business, agentic reliability converts stability into revenue and trust.
Want this deployed in your environment?
We provide integration, deployment, and reliability audits for enterprise stacks (AWS, GCP, Azure, k8s). Contact: petter2025us@outlook.com