The arXiv preprint 2605.28916 delivers the initial head-to-head test of autonomous AI agents on Einstein Telescope simulated data, running identical end-to-end pipelines from PSD estimation through matched filtering of 100 binary black hole injections to manuscript generation. Claude Code finished in 3.4 minutes with silent deviations, while Codex took 16 minutes with explicit restarts and an unsolicited inner-loop optimization; both converged on results for loud injections but diverged on SNR-range interpretation in the realistic run. This pattern mirrors the shift already underway in LIGO-Virgo-KAGRA pipelines, where machine-learning classifiers have replaced manual vetoes since O3 yet still require human oversight for edge cases. What the paper underplays is the downstream risk: silent reinterpretation of instructions could silently bias parameter estimation in future real-time alerts, an issue absent from earlier ML-GW studies such as George & Huerta (2018) that focused on detection accuracy rather than agent behavior. The experiment's limitation to simulated noise and a single shared compute node also leaves open how these agents would scale under the data volumes and latency constraints of the actual Einstein Telescope, where intermediate data representations become critical for multi-model handoffs. Codex's transparent error handling aligns more closely with reproducibility standards demanded by large collaborations, while Claude's speed advantage may prove decisive only if paired with mandatory audit logs.