Spend Anomaly
Spend anomaly — a transaction or pattern that deviates from what is expected for a given agent. A spike in volume, an unknown merchant, an off-hours purchase, or an unusually large single transaction. Anomalies are not always malicious, but they are always worth catching — ideally before money moves.
Definition
A spend anomaly is any deviation from an agent's expected spending pattern. What counts as "expected" depends on the agent's normal behavior, which is defined by the rules you configure. Common anomaly types:
| Anomaly type | Example | Rule that catches it |
|---|---|---|
| Velocity spike | 40 transactions in 90 seconds (retry loop) | Velocity limit |
| Unknown merchant | Spend at a vendor never seen before | Merchant allowlist |
| Off-hours activity | A purchase at 3am when the agent normally runs 9–5 | Time window |
| Unusual amount | A single transaction 10× the agent's typical spend | Per-transaction cap / approval threshold |
| Category surprise | A coding agent suddenly buying advertising | Category limit |
Detection: reactive vs proactive
There are two approaches to spend-anomaly detection, and they are not interchangeable:
Reactive (observability)
Tools like Helicone and Langfuse detect anomalies after they happen. They flag spikes in the dashboard, alert you that a threshold was crossed, and let you investigate post-mortem. Useful for understanding what went wrong; not useful for preventing it.
Proactive (pre-spend firewall)
sipi.bot blocks or flags anomalies before money moves. The velocity rule kills the retry loop on the 11th attempt. The merchant allowlist blocks the unknown vendor. The time window flags the 3am purchase. The approval threshold routes the large transaction to a human. The anomaly is caught at evaluation time, not after settlement.
FAQ
What is a spend anomaly?
A transaction or pattern that deviates from expected — a velocity spike, an unknown merchant, an off-hours purchase, or an unusually large amount. Not always malicious, but always worth catching.
How do you detect spend anomalies in AI agents?
Reactive tools (Helicone, Langfuse) flag anomalies after they happen. A pre-spend firewall (sipi.bot) blocks or flags them before money moves, using velocity, allowlist, time-window, and approval-threshold rules.