Detect spending anomalies in agent purchase patterns and alert in real time

Verified steps

1. Establish a baseline spending profile per agent scope (per agent, per project, per principal): expected transaction frequency, average transaction amount, merchant category distribution, and peak spending hours — computed from 30+ days of historical data.
2. Instrument your wallet service or payment middleware to compute rolling statistics on each transaction: z-score of amount vs baseline, time-since-last-transaction vs normal cadence, merchant category vs expected distribution.
3. Define anomaly thresholds: a transaction 3+ standard deviations above mean amount, more than 2x the normal daily transaction count, a merchant category never seen before, or transactions outside normal business hours for the agent's use case.
4. On threshold breach, emit an alert event to a monitoring channel (PagerDuty, Slack, email) with the anomaly type, transaction details, and agent session context; simultaneously place a soft hold on further agent spending pending human review.
5. Provide a human review interface where the operator can clear the alert (mark as expected — updating the baseline) or confirm the anomaly (triggering automatic spending suspension and incident response).
6. Retrain the baseline model monthly or after significant changes in expected agent behavior (new merchants added, budget increased) to reduce false-positive alert fatigue.