The debate over “AI vs. human” expertise is settled. Instead, let’s talk about where the real opportunity lies—in combining your technical know-how with AI’s capabilities. It’s about amplifying your impact, not replacing your role. The most valuable engineers will be those who can orchestrate and manage intelligent AgenticOps systems.

Whether you’re a command-line interface (CLI) veteran or just beginning your CCNA studies, here are five technical skills to power your career in the age of AgenticOps—along with the training and certifications to get them.

What is AgenticOps?

Let’s first define AgenticOps. Agentic operations (AgenticOps) is a new operating model for IT, one that’s agent-first, purpose-built for autonomous action with oversight, and designed to unify the experience for both humans and machines.

Unlike traditional AIOps, which focuses on alerts and recommendations, AgenticOps leverages AI agents that reason through problems and act at machine speed. These agents don’t just detect issues—they proactively resolve them, often before they impact business operations.

In AgenticOps, humans remain actively involved, collaborating with AI agents through a unified workspace rather than managing fragmented tools.

Why AgenticOps now?
With the complexity of modern IT environments, reactive approaches are no longer enough. AgenticOps enables proactive, intelligent operations, driving efficiency, resilience, and a unified experience for teams and customers alike.

1. Enhance your knowledge of network programmability and APIs

Why it matters:
A programmable network is a prerequisite for any AI-driven operation—if your infrastructure isn’t accessible via APIs, it isn’t ready for AI.

In the old days (read: last year), we’d SSH into a box, run some show commands, and manually tweak the config. Agentic systems have evolved well beyond that. While some agents — like Claude Code or GPT Codex — still live in the CLI, SSHing into devices and rendering information via bash scripts, modern agentic workflows increasingly rely on APIs for speed, scale, and structured data exchange.

Programmable infrastructure is the bedrock of AI. If the AI can’t talk to your controllers (like Cisco Catalyst Center or Meraki) via REST APIs and JSON, it can’t do its job.

Key focus areas:

• REST APIs, JSON data structures
• Secure API authentication and authorization

Training resources:

• Automating and Programming Cisco Enterprise Solutions | ENAUTO (Learning Path)
• Designing, Deploying, and Managing Network Automation Systems | AUTOCOR (Learning Path)
• Beyond the CLI: Supercharging IT with AI (Tutorial)

2. Develop automation & scripting skills (especially Python)

Why it matters:
Automation bridges the gap between AI “intent” and real-world outcomes, driving efficiency and reliability at scale.

Think of it this way: AI decides, but automation executes. AgenticOps needs a way to turn a “thought” into a workflow. That’s where Python comes in. Python remains the scripting language of choice for network automation, enabling engineers to parse telemetry, orchestrate workflows, and drive rapid change.

You don’t need to be a software developer, but you do need to understand how to use Python to parse telemetry and trigger API calls. Scripting frameworks allow you to deploy and troubleshoot at the speed of AI.

Key Focus:

•  Python basics
• Version control (Git)
• Telemetry parsing and workflow automation

Training resources:

• • Design, Deploy, and Manage Network Automation Systems | AUTOCOR (Learning Path) Track 1; special release
  • Automating Cisco Data Center Networking Solutions | DCNAUTO (Learning Path) Track 1; special release
  • Free option: Understanding Cisco Network Automation Essentials | DEVNAE (Learning Path)

3. Leverage wireless telemetry and experience analytics

Why it matters:
Wireless is the richest source of network telemetry. Understanding and acting on this data is essential for supporting AI optimization.

Wireless environments are messy, dynamic, and generate massive amounts of real-time data. This is where AI-driven optimization (like RRM) really shines.

To manage an AI-driven network, you need to understand the data coming off the airwaves. You need to know what “good” looks like in terms of client health and telemetry.

Key Focus:

• RF fundamentals
• AI-driven RRM
• Interpreting telemetry data for real-time decision-making

Training resources

• • Implementing and Operating Cisco Wireless Core Technologies | WLCOR:  (Learning Path) Track 1; special release
  • Cisco Silicon One for AI Networking | DCSOAI (Learning Path)

4. Build secure networking foundations

Why it matters:
AI needs clear rules and boundaries to operate safely and deliver value—engineers define and enforce those guardrails.

AI is powerful, but it needs guardrails. You wouldn’t let a self-driving car on the road without traffic laws; you shouldn’t let AgenticOps run wild without a solid policy framework.

Engineers define the intent. You design the segmentation and access controls that keep the AI operating safely and securely.

• Key Focus: Network policy design, segmentation, and SLA definition.
• Training resources.
  • Cisco Foundation AI Security Model: Getting Started (Free intermediate tutorial): Learn how to run the Cisco Foundation AI Security Model (short name: Foundation-Sec-8B), an open LLM specializing in cybersecurity tasks, such as streamlining security operations.
  • Introduction to Cybersecurity (NetAcad)

5. Advance data interpretation and AI literacy

Why it matters:
Engineers who can validate and contextualize AI recommendations will lead teams and ensure successful adoption.

You don’t need to become a data scientist, but you do need to be the “human in the loop.” The engineer who can validate AI outputs will lead the team.

You need to be able to review an AI recommendation, detect a false positive, and interpret telemetry trends to ensure the machine is actually doing what it’s supposed to.

Key focus areas:

• Validating AI recommendations
• Basic anomaly detection
• Trend interpretation and decision support

Training resources:

Cisco Splunk for AI Operations | CAIOP (Learning Path)

Spin Up AI—Launch OpenShift AI (Tutorial)

Which certifications support AgenticOps skills?

Many certifications build the overlapping core competencies needed for AgenticOps roles. Here are the top credentials to consider as you expand your skills:

Cisco Certified Networking Associate (CCNA) Automation

For network programmability, APIs, and automation with Python. Formerly DevNet.

Cisco Certified Networking Professional (CCNP) Automation
For advanced automation, programmability, and software development in network environments.

CCNA
For foundational networking knowledge, including automation concepts.

CCNP Enterprise
For advanced enterprise networking and wireless, including telemetry and analytics.

Cisco Certified Specialist – Enterprise Wireless Implementation
For deep dives into wireless environments and real-time data analytics.

Cisco AI Technical Practitioner
For foundational knowledge of AI operations, security, and policy frameworks.

CCNA Cybersecurity (formerly CyberOps Associate)
For operational security, data interpretation, and anomaly detection.

The future of networking is autonomous. Are you ready to power it?

Tell us what actions you’ll take to learn AgenticOps in the comments!

 

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