As AI systems become increasingly embedded in production infrastructure, the next step is moving beyond systems that simply detect and diagnose failures toward systems that can safely remediate them. Giving AI agents the ability to act on production environments introduces a new set of challenges around reliability, security, observability, and control. This talk explores how to design self-healing AI infrastructure that can move through a closed-loop workflow of detect, diagnose, plan, act, and verify. We’ll examine how agents can correlate signals across logs, metrics, traces, deployments, and infrastructure state; interact with operational tools; and choose remediation actions while operating within deterministic guardrails. Attendees will leave with a practical architectural framework for evolving from AI-assisted incident response toward reliable, policy-controlled autonomous remediation.