Runtime Monitoring

Runtime monitoring refers to the continuous observation of an AI system while it is live and handling real inputs, checking its outputs, behavior, and resource use against defined rules or expected patterns. For governance teams, it is the mechanism that catches problems the pre-deployment testing missed, such as harmful or inaccurate responses, misuse by users, or drift in how the model performs over time. Organizations often rely on runtime monitoring to produce the audit trails and incident records that regulators and internal risk committees increasingly expect.

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Research2026-10-05

Red Hat's Production Agent Guide Sets a De Facto Control Baseline

Red Hat published guidance on deploying AI agents safely in production. It covers isolation of agent environments, tool-level access controls, and identity management for agents. It also addresses continuous behavioral monitoring and emergency stop capabilities. The guidance does not carry regulatory weight but reflects an infrastructure vendor codifying what production-ready agent governance looks like. Compliance teams can expect auditors and counterparties to reference this kind of vendor framing when assessing agent deployments.

Red Hat agentic AI agent identity runtime monitoring deployment governance emergency stop AI agent security