Most teams are moving fast on agents, but very few are governing them well. In production, the real problem is not capability — it is control: how to evaluate risk, bound autonomy, preserve auditability, and keep systems stable under uncertainty. In this talk, I will introduce the architecture behind ARF (Agentic Reliability Framework), a governance layer for agentic infrastructure that converts probabilistic AI outputs into deterministic, auditable decisions. The session explores Bayesian risk scoring, expected-loss decisioning, bounded memory systems, and calibrated escalation mechanisms designed to make autonomous AI survivable inside real enterprise environments.
Juan Petter is an AI Engineer and founder building the Agentic Reliability Framework (ARF), a governance and control plane for agentic infrastructure. His work focuses on making AI systems provably safe, auditable, and operationally viable in production environments. He has experience across cloud infrastructure, technical support, full-stack engineering, and AI systems design.
