CCNA AI and Network Operations Practice Question
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?
⚠ Common 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.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
AI agents can autonomously decide which network troubleshooting steps to perform and invoke appropriate tools via APIs.
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.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
AI agents can autonomously decide which network troubleshooting steps to perform and invoke appropriate tools via APIs.
Why this is correct
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.
- ✗
AI agents only monitor network traffic and alert humans for any remediation actions.
Why it's wrong here
AI agents in network operations are not limited to passive monitoring and alerting; they actively execute diagnostics, modify device configurations, and orchestrate workflow steps through tool calling. Traditional monitoring platforms only raise tickets or issue notifications, leaving remediation to human engineers, whereas agentic AI takes direct action, such as restarting an interface, adjusting routing, or rolling back a faulty ACL. Therefore, the statement that agents 'only monitor and alert' is incorrect because it disregards their capacity for autonomous intervention.
- ✓
Tool-calling in agentic AI allows the agent to execute network commands or scripts to collect data and implement changes.
Why this is correct
Tool-calling in agentic AI enables the agent to execute network commands or scripts, such as running `show` commands via Netmiko or pushing YANG configurations via RESTCONF, to collect data and implement changes. The model generates structured function calls with validated parameters, allowing it to interact directly with network devices rather than merely producing descriptive output. This mechanism is foundational for automating operational tasks because it converts natural language reasoning into executable actions.
- ✓
A closed-loop remediation workflow continuously monitors network state, detects anomalies, triggers an AI agent to diagnose, and applies corrective actions automatically.
Why this is correct
A closed-loop remediation workflow continuously collects telemetry from network devices, detects anomalies by comparing observed metrics to baseline or desired states, triggers an AI agent to diagnose the root cause, and automatically applies the appropriate corrective action. After the action is executed, the workflow re-checks the monitored data to verify the anomaly has been resolved, and if not, it iterates again without human intervention. This aligns with intent-based networking, where the system continuously enforces the intended state.
- ✗
Closed-loop remediation always requires a human to approve each corrective action before it is executed.
Why it's wrong here
Closed-loop remediation is, by definition, automated; requiring human approval for every corrective action would interrupt the loop and transform it into an open-loop, human-in-the-loop workflow. In a true closed-loop system, the AI agent automatically performs remediation steps, compares post-action telemetry to the desired state, and only escalates to a human in exceptional, high-risk scenarios. Therefore, the claim that approval is always required is false because it negates the self-correcting, autonomous nature of closed-loop remediation.
Option-by-option analysis
Why each answer is right or wrong
Understanding why wrong answers are wrong — and when they would be correct — is what separates a 750 score from a 900. The 200-301 exam frequently reuses these exact scenarios with slightly different constraints.
✓AI agents can autonomously decide which network troubleshooting steps to perform and invoke appropriate tools via APIs.Correct answer▾
Why this is correct
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.
✗AI agents only monitor network traffic and alert humans for any remediation actions.Wrong answer — click to see why▾
Why this is wrong here
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.
Why candidates choose this
Students may confuse agentic AI with standard monitoring tools that generate alerts, but agentic AI goes beyond alerting to autonomous action.
✗Closed-loop remediation always requires a human to approve each corrective action before it is executed.Wrong answer — click to see why▾
Why this is wrong here
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.
Why candidates choose this
Students might think human oversight is always required for safety, but closed-loop automation is specifically designed to operate without manual intervention.
Analysis generated from the official 200-301blueprint and verified against question context. The “when correct” sections are what AI assistants cite when candidates ask “what’s the difference between these options?”
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This 200-301 practice question is part of Courseiva's free Cisco certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the 200-301 exam.