Network Automation with AI: A Practical Guide

June 18, 2026 · View on GitHub

Deploy, Secure, and Monitor Enterprise Networks Using AI Agents, Python, and Modern DevOps Tools

132 pages | 29 chapters | 99 code examples | All tested in production

About

Written by an industrial automation engineer who spent 4 months building an AI system from scratch (and documented every mistake along the way), this book bridges the gap between "I know networking" and "I automate networking."

What's Inside

Part I: AI Tools for Network Engineers

Full 2026 comparison: Claude Code vs Cursor vs Copilot vs Windsurf vs Codex
Benchmarks (LMSYS Arena, SWE-bench, Terminal-Bench) with links
Pricing breakdown — one developer saved $15K vs API pricing
Free & open-source tools: Ollama, n8n, CrewAI, LangChain, Dify

Part II: Network Automation Stack

Python: Netmiko, NAPALM, Nornir (100x faster than Ansible)
Ansible: 4 production-ready playbooks (backup, hardening, VLANs, provisioning)
Terraform: AWS VPC, Palo Alto firewall examples
Ollama: Run AI locally with zero cloud cost

Part III: Building an AI Expert (Genesis Project)

Novel Cascade MoE architecture (patent filed May 2026)
10,800+ expert routes, 12,651 neurons, one-shot learning
14 documented mistakes with exact cost of each

Part IV: Enterprise Deployment

Corporate network from scratch: DNS, DHCP, AD, firewall, VPN, monitoring
Zero Trust architecture step-by-step
SD-WAN vs MPLS migration strategy
IPv6 transition guide
Security automation: nftables, Suricata IDS, fail2ban, automated incident response

Part V: Real World

4 case studies (Princeton -95% time, Intel -70% incidents)
8 troubleshooting scenarios with diagnosis commands
Network automation horror stories (real incidents)
ROI calculator for management
Free lab setup (Containerlab, GNS3, EVE-NG, Cisco DevNet Sandbox)
7 hands-on lab exercises

14 Mistakes I Made Building an AI for Network Automation

The most valuable chapter in the book — so you don't repeat them.

Mistake 1: Wrong Token IDs (Cost: 2 Days)

Model produced garbage during inference. BOS token ID was set to 1 (UNK) instead of 2. Lesson: Always verify special token IDs through server code, not documentation.

Mistake 2: Dataset Imbalance x25 (Cost: 10,000 Steps)

Model answered everything with "saving VPN passwords." One dataset (40 examples) was weighted x25 = 1,000 effective examples. Lesson: New datasets start at x10-15 max. No single category > 15%.

Mistake 3: Memory Pollution (Cost: Days)

Test runs filled memory.json (5.2 MB) with garbage, injected into every prompt. Lesson: Clear runtime files before every test.

Mistake 4: Full Backup Restore — THE BIG ONE (Cost: $100 + 90K Steps)

Instead of fixing one broken line, restored ENTIRE file from month-old backup. Missing 100+ datasets, correct multipliers, critical fixes. Quality dropped 95% → 15%. Lesson: NEVER restore from old backups. Fix the specific line. cp file file.pre_edit before any edit.

Mistake 5: "Wait and See" Mentality (Cost: Weeks)

Lesson: See a problem? Act IMMEDIATELY. Never "wait and check later."

...and 9 more mistakes in the full book.

Quick Start: Your First Network Automation

# Install tools
pip install netmiko ansible
npm install -g @anthropic-ai/claude-code

# Connect to a Cisco switch with Python
python3 << 'EOF'
from netmiko import ConnectHandler

device = {
    'device_type': 'cisco_ios',
    'host': '192.168.1.1',
    'username': 'admin',
    'password': 'secret',
}
conn = ConnectHandler(**device)
print(conn.send_command('show ip interface brief'))
conn.disconnect()
EOF

# Or use AI to generate an Ansible playbook
claude "Write an Ansible playbook to configure VLANs 10,20,30
on Cisco switches with trunk ports on Gi0/1"

Free Resources

Tool	Purpose	Install
Ollama	Run AI locally	`curl -fsSL https://ollama.com/install.sh \| sh`
Containerlab	Network lab	`bash -c "$(curl -sL https://get.containerlab.dev)"`
Cisco DevNet	Free Cisco labs	Always-on, no signup needed
n8n	Workflow automation	`docker run -d -p 5678:5678 n8nio/n8n`
CrewAI	Multi-agent teams	`pip install crewai`

Benchmark Sources

LMSYS Chatbot Arena — Human preference voting (6M+ votes)
Artificial Analysis — Quality + Speed + Price for 188 models
LLM Stats — 300+ models ranked

Author

Alexander Larionov — Industrial Automation & AI Engineer with 15+ years in SCADA/DCS, metro systems, and enterprise networks. Creator of the Genesis Cascade MoE architecture (patent filed May 2026).

ISNI: 0000 0005 3040 9357