Which THREE of the following best describe how agentic AI is used in network automation, specifically regarding AI agents, tool-calling, and closed-loop remediation workflows?
AI agents can autonomously decide which network troubleshooting steps to perform by reasoning over available telemetry, hypotheses, and tool outputs. For example, an agent might determine that a recurring BGP flap warrants inspecting neighbor states via `show bgp summary`, then use RESTCONF to modify the `hold-time` timer and re-verify adjacency. This iterative decision-making loop—choose a diagnostic, execute via API, interpret results, and adapt—enables goal-driven troubleshooting without human prescripting of every step.
Why this answer
Options A, C, and D are correct because agentic AI in network automation involves autonomous decision-making (A), tool-calling to execute network commands or gather data (C), and closed-loop remediation that continuously monitors, diagnoses, and applies fixes automatically (D). Options B and E are incorrect because they contradict the autonomous nature of agentic AI: B describes a passive monitoring system with human-only remediation, and E states that closed-loop remediation always requires human approval, which is not true for full closed-loop automation.
Exam trap
Cisco often tests the distinction between passive monitoring and active autonomous remediation; the trap here is that candidates may confuse agentic AI with simple alerting systems, forgetting that agentic AI must include decision-making and tool execution, not just notification.
Why the other options are wrong
This option describes traditional monitoring systems that only alert humans, not agentic AI which takes autonomous actions. Agentic AI agents do not just alert; they actively diagnose and remediate issues.
Closed-loop remediation implies full automation without manual approval; requiring human approval breaks the loop and defeats the purpose of autonomous remediation. The workflow is designed to act automatically.