As AI agents move from experimentation into production, they are increasingly capable of executing commands, accessing enterprise systems, modifying infrastructure, and making operational decisions without continuous human supervision. But what happens when an autonomous agent makes the wrong decision at 2 AM? Who detects it, who has authority to intervene, and how do we stop the action before it becomes a production incident? Traditional SRE practices provide powerful mechanisms for observability, reliability, incident response, and recovery. Autonomous AI introduces additional challenges involving agent identity, dynamic authorization, decision accountability, and runtime enforcement. This session introduces a practical architecture for bringing AI governance directly into production operations. We'll explore how OpenTelemetry, agent registries, identity-aware policies, authorization gateways, human approval workflows, runtime monitoring, and automated kill switches can work together to make autonomous systems safer and more reliable. Through a realistic production incident scenario, we'll examine how an AI agent's proposed action can be evaluated, authorized, blocked, escalated, and documented before unintended consequences occur. Attendees will leave with actionable architectural patterns for connecting SRE practices with enforceable AI governance. The central message: Observing an AI agent is not the same as controlling it. Reliable autonomous systems require both.
Marcelo Lorenzetti is Chief AI Officer and Principal AI Architect at Guild Systems, specializing in enterprise AI architecture, agentic AI governance, cloud infrastructure, and operational resilience. With extensive experience leading enterprise technology architecture and AI transformation initiatives in financial services and other regulated environments, he focuses on turning AI governance principles into enforceable production controls. His work spans autonomous agent orchestration, identity and authorization, observability, runtime policy enforcement, operational risk, and AI economics. Marcelo also serves on the New York Law School AI Blue Ribbon Committee and is an author and speaker on enterprise AI, responsible innovation, and technology governance. His approach bridges strategic governance and practical engineering, helping organizations deploy autonomous AI systems that are reliable, accountable, and operationally controllable.
