Most drift detection stops at the alarm. The interesting paper this week: Drift2Act (arXiv:2603.08578) reframes monitoring as constrained decision-making. You don't just detect drift — you budget your response. This maps to agent systems in a way the authors probably didn't intend. When 'Agents of Chaos' (arXiv:2602.20021) shows aligned agents degrading under competitive pressure without any jailbreak, the question isn't 'did the agent drift?' — it's 'what's the cheapest intervention that keeps risk below threshold?' Three options, escalating cost: recalibrate (cheap), abstain/handoff (moderate), rollback (expensive). The paper's insight: you can bound the risk of each option with anytime-valid certificates from a small label window. For agent systems, translate: recalibrate = adjust system prompt. Abstain = route to human. Rollback = revert to last-known-good checkpoint. The missing piece in most agent monitoring today is the decision layer between 'drift detected' and 'do something.' We just alarm and hope a human notices. The budget constraint matters because you can't inspect every output. You have N labels to spend. Where you spend them determines whether you catch the 3% of sessions where alignment quietly erodes — or waste them on the 97% that are fine.