AI for Network Engineers: Automation, Troubleshooting & Ops

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Network engineers have always automated themselves out of repetitive work — from typing commands, to Expect scripts, to Jinja2 templates, to model-driven configuration. Large Language Models are the next layer in that same line, and this course teaches you to use them as a serious working tool: not a chatbot novelty, but a co-worker that reads logs faster than you can, drafts configuration in your house style, and turns a wall of show output into a plain-English answer — without ever letting a probabilistic tool make an unsupervised change to your network.

This is a hands-on, code-first course. Almost every lecture ends in something you can actually run, and the six sections build toward three complete, deployable projects. You will work with both Cisco IOS/NX-OS and Juniper Junos throughout, because to a language model both are simply text, and most real networks are mixed. Every script is written so you can point it at a cloud API (Claude/OpenAI-compatible) or a local model running on your own hardware via Ollama by changing a single line.

What the course covers, section by section:

• Foundations — what actually changed with LLMs, tokens and context windows, why models hallucinate (and the five levers that stop it), setting up a reusable Python toolkit, and writing prompts that reliably return clean, structured JSON.

• Core Use Cases — generating and validating configuration with a safe apply loop (diff, dry-run, human gate), filtering and triaging syslog at scale, running a stateful troubleshooting co-pilot grounded in live device output, automating documentation and diagrams-as-code, and getting an AI-assisted second opinion on a network design.

• Building with Python & APIs — production-grade API calls with retries and cost tracking, feeding the model properly parsed multi-vendor data, building a retrieval-augmented ("ask your own network") pipeline over your own documents, and building a bounded, tool-calling agent with hard guardrails.

• Tools, Models & Operations — mapping the AI-for-networking tooling landscape, running capable models entirely on your own hardware, and the security, privacy, and cost controls that make AI safe to run at organisational scale.

• End-to-End Projects — three complete tools you build and could genuinely deploy: a production syslog triage bot, a fleet-wide configuration compliance checker with CI integration, and an interactive, read-only troubleshooting assistant.

• LLM Agents & MCP Servers — the Model Context Protocol standard for tool integration, building a production-ready MCP server in Python that exposes your network toolkit, connecting Claude Desktop and Claude Code to your own tools over stdio, and writing a persistent chat agent that discovers tools dynamically instead of hard-coding them — cloud or local reasoning, same guardrails, one protocol.

Every lecture that has runnable code also includes a practical, hands-on exercise, and most come with a downloadable solution file so you can check your work.

The idea that runs through the whole course: ground the model in real data instead of letting it recall, treat every output as a draft, and let a deterministic check — a diff, a dry-run, an allow-list, or a human — decide what actually touches your network. Hold that, and everything else is detail.

The course contains the use of AI.