Each replay below is an animation of a real exchange pattern: a technician asks a question, the AI proves the answer on an actual machine, and the ticket moves. Client names, hosts, IPs and ticket numbers are fictional; the tool calls and outputs are shaped exactly like production.
Machine-generated ticket. Nobody has read it. The verifier reads the device before a model is involved; the procedure matches; both of its conditions pass; the ticket is closed with the reason written on it.
Procedure is approved and auto-enabled, evidence came from the device, and the disposition is benign.
Post a customer reply, or close anything whose probe returned "unknown".
Probe output, procedure id, closure reason, and zero technician minutes.
A regular ticket where the fix is code. The AI proves which layer is wrong before editing anything, works in a throwaway container that mirrors the client's stack, runs the tests, and hands back a diff with a rollback line. The technician merges; the AI never deploys to production on its own.
No SSH, no web console, no password: the hypervisor is an agent like every other device, so the AI reaches it through the same audited channel. It finds the cause inside the guest, refuses to delete anything, grows the disk only after an explicit approval, and files the fix as a procedure that still needs supervision.
Storage, guest config, guest-agent usage — all reads, all recorded.
A 40 GB grow on thin storage: no data touched, nothing deleted.
The cleanup procedure is a draft because it contains a delete.
"Everything is slow", from an address nobody recognises. The system resolves the client, finds six candidate machines, and stops — with a decision chat that already knows what it checked. Two questions later the root cause is found without asking the customer anything.