Lilly Tech SystemsBest Practices Advanced
AI-driven network automation has enormous potential, but it also carries risk. These best practices ensure you deploy automation safely, build team confidence, and scale effectively.
Safety Guardrails
- Blast radius limits — Limit how many devices can be changed in a single automation run
- Rate limiting — Cap the number of automated changes per hour/day
- Dry-run mode — Always support a mode that shows what would change without executing
- Rollback timers — Automatically revert changes if post-checks fail within a timeout
- Kill switch — One-click mechanism to disable all automated changes instantly
Testing AI Automation
| Test Type | Purpose | Environment |
|---|---|---|
| Unit Tests | Verify individual automation functions | Local development |
| Integration Tests | Test automation against mock network devices | CI/CD pipeline |
| Digital Twin Tests | Run automation against full network simulation | Digital twin environment |
| Canary Tests | Execute on a small production subset first | Production (limited scope) |
| Chaos Tests | Verify automation handles unexpected failures | Staging or production |
Change Management Integration
Automation and ITIL: AI-driven automation should integrate with your change management process. Low-risk, pre-approved change types can be executed automatically. Higher-risk changes should generate change requests for review.
Measuring Success
- MTTR reduction — Mean time to resolve incidents with vs. without automation
- Change success rate — Percentage of automated changes that complete without rollback
- Incidents prevented — Issues caught by predictive automation before user impact
- Engineer time saved — Hours freed from repetitive tasks for higher-value work
Course Complete!
You have completed the AI-Driven Network Automation course. Continue with Network Digital Twins to learn about simulation and testing.
Next Course: Network Digital Twins →