Originally published on AI Tech Connect.
The failure that looks like a success Picture a settlement agent. It serves a subscription business with customers in Bengaluru and Birmingham, and once a month it works through a list of accounts. For each account it reads the usage, decides the amount, calls the payment provider, writes an invoice row, and emails a receipt. It is a perfectly ordinary agent: a handful of nodes, a model call in the middle to handle the awkward proration cases, four tools, one loop. At 02:14 the host running it is replaced. A node pool scales in, or a deploy rolls, or the kernel decides it has had enough. The process disappears somewhere after the payment call has gone out and before the invoice row has been written. Nobody notices, because the system is built for this: there is a checkpointer, the thread…
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