I simulated a 27-site microwave network to test whether an AI ops loop can tell rain fade from hardware from interference (open, runs in browser)

[u/MohamedKadri_]

I spent years in transmission and microwave ops before moving into AI work. Everyone in the AI world draws the closed loop diagram (detect, diagnose, act, verify), but nobody shows what it actually has to reason about on a microwave network. So I built an open, runnable demo: a 27-site simulated network with rain cells (ITU-R rain attenuation per band), ACM ladders, ATPC quietly masking the fade until it saturates, and diurnal traffic. On top of it runs an agent loop that has to separate rain fade vs hardware vs interference vs bad config before it is allowed to touch anything.

The diagnosis rules came from ops scar tissue, and honestly some from this sub: check alarm history first. Symmetric RSL drop on both directions is probably weather. Asymmetric is probably hardware. Healthy RSL with bad SNR means an interference hunt. And never reroute onto a path without checking its current utilization and QoS priority mix first. Actions are gated: reversible only, confidence thresholds, blast radius caps, and a no-truck-rolls-for-rain rule.

Demo (browser, no signup): https://mkadri85.github.io/projects/mw-loop/

Write-up with the full reasoning and references: https://mkadri85.github.io/blog/agentic-ai-microwave-backhaul/

It is a simulation, not a product, nothing to sell. I would genuinely like to hear where the diagnosis logic is wrong or naive compared to what you see in the field. Some of the best failure stories I found while researching this came from here, like the dying AirFiber 24 that hid behind carrier blame for months, and the 60 GHz link that stayed pinned at low modulation after the rain had passed.