In this case, “agentic” means a model that plans and then acts toward a goal you set. You give it an objective (e.g., “triage this alert”), along with a small set of tools, such as an asset inventory lookup, a case history query, and a pod metadata reader. From there, the agent chooses which tool to use and in what order, looks closely at what result comes back, and uses that information to shape its next move. That planning loop is what makes an agent useful. A rules engine or a SOAR playbook can only follow the path defined ahead of time, so the moment an alert steps off that path, it freezes (secure and deterministic by design). A chatbot will think out loud about the alert, but it won’t check anything for itself in your systems (limited correlation capabilities).