Agentic AI for
Network Automation Bootcamp
Five days on a live lab network, from your first LLM API call to a multi-agent change pipeline. Every change starts as a structured, validated proposal, gated for approval, deployed, then independently verified.
Cloud lab per attendee · Bring your own API key · Take-home repo
Outcomes
What you'll walk out able to do
Five days, hands on keys throughout. By the end of the week you can:
The architecture you'll build
Two triggers, one pipeline
A planned change and a 3AM incident look like different jobs. Only the trigger differs. Both end up as the same structured proposal, and from there every change takes an identical path to the network - so you build the controls once and both triggers inherit them.
Intent · planned
"Add VLAN 250 to leaf1 and leaf2, tag it on the uplinks." An engineer asks in plain English; the agent returns a structured proposal.
Incident · unplanned
Your monitoring stack POSTs an alert to a webhook. The agent reads the payload and works out what happened - no parser to write per alert source.
Curriculum
Five days. Nothing you build gets thrown away.
Each lab is a component of the final pipeline, not an exercise you bin - by the last day they're wired together.
LLM foundations & cost engineering
Typed output, tool calls, and a bill you can predict.
Lab · Config Intelligence Tool - typed configs in, misconfigurations flagged, remediation generated, cost report per run.
MCP - standardised tool access
Read your network conversationally, without handing over write access.
Lab · NetOps MCP Suite - two servers wired into Claude Code, troubleshooting a spine-leaf fabric conversationally.
RAG & knowledge graphs - local context retrieval
Grounded answers, with the sources attached.
Lab · NetOps Knowledge Assistant - routes across runbooks, the topology graph and live device state. Ask it what breaks if a device dies, and it traverses the topology to tell you.
Agents & agent graphs - reason, act, verify
Closing the loop safely. The heaviest, best day of the week.
Lab · Incident Agent - solve a BGP incident with the harness first, then build the agent graph that emits a typed proposal into a NetBox branch. It never touches a device.
Multi-agent systems, A2A & capstone
From one agent to a system serving both planned and unplanned change.
Capstone - the multi-agent NetOps pipeline, built and demoed live.
Tools you'll learn
One opinionated choice per layer, always taught after the raw concepts - so you can swap any of them later.
Model layer
Tool access
Retrieval
Harnesses
Agents
Why this, not a course you watch
The parts a demo skips
Multi-vendor devices that break, approval gates, independent verification, rollback and cost control. The unglamorous half is the half that decides whether any of this runs in production.
A live lab network
Your own cloud VM with a multi-vendor fabric, source of truth, vector store and graph database - pre-seeded and ready.
Language in, deployed change out
A pipeline that takes a plain-English request or an alert and ends in a validated, gated, verified change - or an automatic rollback.
Verification at both ends
Pre-flight before anything ships, post-flight after it does - with the verdict enforced by the source of truth, not by the agent that might bypass it.
Cost engineering as a habit
Model routing tables, token counting, caching and batch APIs. No batch operation without a cost estimate first.
A live cohort, capped at 20
Small enough that you get unstuck in minutes. Every lab is scaffolded so you write the interesting parts, not the boilerplate.
A repo you keep working in
Labs, reference solutions, shared helpers, eval harness, lab environment and slides. The starting point for your own internal tooling.
Who it's for
Network engineers who are comfortable writing basic Python and want to build AI-driven network operations tooling - not just chat with an LLM.
Not for you if you've never written Python, or if you want a vendor product demo rather than to build it yourself.
Your instructor
Rick Donato
Founder, Packet Coders
Rick has spent over twenty years in networking and network automation, and founded Packet Coders to deliver hands-on training by engineers, for engineers. He has taught Python network automation to hundreds of engineers through the Packet Coders bootcamps and tech sessions.
This bootcamp is the material he wanted when agents first landed: opinionated framework choices, real devices, and a safety model you can defend in a change review.
FAQs
How much Python do I need?+
You should be comfortable writing basic Python - functions, dictionaries, working with an API, reading a traceback. You don't need to be a software engineer.
Do I need my own lab or API key?+
The lab and development environment are fully provided - a multi-vendor fabric, source of truth, vector store and graph database, all pre-installed and pre-seeded. All you need is a Chrome browser. You bring your own LLM API key.
Will the agents make changes to a network?+
Yes - in your isolated lab, and never directly. Agents emit a typed proposal into a branch of the source of truth. It's validated before deployment, gated on a human merge, deployed with commit-confirmed so the device reverts itself if nothing confirms, then verified independently.
Is this vendor-specific?+
No - you'll perform changes against a multi-vendor lab environment. NetBox is the source of truth, sitting behind a thin adapter interface, so the patterns move to other sources of truth without rewriting the agents.
Which frameworks do you teach?+
One opinionated choice per layer, always after the raw concepts: Pydantic for structured output, FastMCP for tool servers, LlamaIndex for RAG, a graph database for topology, Pydantic AI for agents, LangGraph for agent graphs, A2A for agent-to-agent.
Can my employer pay, and do you do team rates?+
Yes. Invoices for teams are no problem, and teams booking together get a discount on future sessions. Get in touch and we'll sort it.