The biggest challenges that most of us networking people have are around velocity (how fast we can build and scale networks) and how effectively we can operate them (avoid defects, fix them fast when something breaks).
LLMs are great in both areas. AI helps with deployment challenges by speeding up tooling development and the creation of workflows on orchestration platforms. A manual process step today, say - reserving an IP address in an IP DB — is automated the next day instead of on a backlog for years. This post is an example of that (config-gen/config-deploy).
Operations use-cases are more interesting, IMO, and address the “too many signals” problems that we face. Network substrate telemetry, overlay telemetry, service host metrics, service metrics, customer metrics, recent change data, prior alarms - the list goes on. Being a network operator is not for the faint of heart and is under-mentioned on high stress job lists. AI makes AMAZINGLY good network operations triage agents, since they are able to immediately process so many signals.
Exciting times!
I switched recently to OpenWrt from MT, which code agents are also good at. I'd wager most issues are going to be related to the user not specifying what they want clearly enough. The translation from network concepts to RouterOS config is pretty 'fat-free', so there's not much room for hallucinations beyond syntax errors, which can be verified via the API.
You can take this one step further and have the agent write Terraform configs [1]. I did this (including having the agent import all the initial resources from the live device), works great and is generally more robust than a script.
[1] https://github.com/terraform-routeros/terraform-provider-rou...
I can’t see any reason to have agents do what a script can do. If the operation is deterministic then why pay every time it gets done? This is why MCP seems so pointless to me.
In other news, Meraki has an AI assistant feature now.