A lost update between AI agents: two agents edit the same resource, both writes return success, and one is silently gone with nothing in the system saying a word. It happens when both read the same version and the last writer overwrites the rest. The fix is a pre-write compare-and-set gate, not a bigger log.
I ran it. With 5 agents committing to one shared file under a worst-case schedule, where every agent reads before anyone writes, 4 of the 5 contributions vanished from the final state, and every one of those 5 writes had been acknowledged. The final file held exactly one agent's work. The other four were paid for, ACKed, and silently overwritten. And that worst case is not rare: in a fair sample of interleavings at N=5, some write was lost in essentially 100% of runs.
This is the lost update anomaly, and it is older than AI agents by decades. What is new is that each vanished write burned real model tokens, and the loss leaves no error, no exception, no red log line. So I built a small offline simulator to make the loss countable, and to test the one class of fix that actually stops it: a version check before the write lands.
AI disclosure: I wrote lostwrite_sim.py with an AI assistant and ran it myself, offline, on Python 3.13.5, standard library only. No network, no keys, no threads, no funds. Concurrency is simulated by explicit interleaving schedules, not real parallelism, so the output is reproducible. Every number and hex string below is pasted from a real local run. Three runs produced byte-identical STDOUT with sha256 1df08c5e38894622314a2b052684303853b9b8fa9508eaabdfd877970c88983b. The linked specs and articles are other people's work.
