GoKawiilA harness is the prompt + tools + scaffolding around an LLM agent. A meta-harness is the loop that improves the harness automatically: a proposer agent reads the diagnostic logs of prior candidates, spots a failure mode, and writes a better harness. Yoonho Lee’s framing of the idea makes one sharp claim — the bottleneck is diagnostic context: most optimizers compress prior runs into summary statistics, while meta-harness gives the proposer up to 10M tokens of raw execution traces to grep through.
That claim is only useful if the runtime can produce, store, and serve those traces cheaply. We show that Islo (docs) sandboxes already do. The key primitives map 1:1 onto what meta-harness needs: islo snapshot save for reproducible eval environments, islo use --snapshot for cheap parallel forks per candidate, and islo logs for durable diagnostic traces. We wire these together in a ~200-line bash orchestrator with a deterministic offline simulator (so the loop is observable in seconds without burning agent credits) and a pattern-matching proposer that demonstrates the optimization signal end-to-end. The same orchestrator swaps to a real Claude/Islo backend with three line changes.