How do I monitor AI agent spending?
Monitor AI agent spending through MoltPe's transaction API (GET /v1/transactions), webhook notifications (every payment fires an event you subscribe to), and the dashboard's per-agent activity view. Set spending policies as the first line of defense — daily caps, per-transaction caps, recipient allowlists prevent runaway spending before it happens. Combine policies with monitoring: policies stop the bleed; monitoring tells you what the agent is doing and why. MoltPe is AI-native payment infrastructure that gives AI agents isolated wallets with programmable spending policies for autonomous USDC stablecoin transactions.
The short version
- Policies first: Spending caps and allowlists are enforced at the rail. They run before any monitoring code you write.
- Webhooks for real-time: Subscribe to payment events and pipe them into Slack, PagerDuty, or your data warehouse.
- API for analysis:
GET /v1/transactionswith filters by agent, date, recipient, status — ideal for daily/weekly review.
In more detail
The mistake most teams make when wiring up agent spending is to start with monitoring. Monitoring tells you that the agent already spent the money — which is useful, but doesn't prevent the next bad transaction. Start with policies. Set a daily cap that's a fraction of what the agent could plausibly need. Add a recipient allowlist if the agent only pays a known set of services. Add a per-transaction cap so a single bug can't drain the wallet. These rules are enforced at the MoltPe rail, before the funds move — which means even an agent whose prompt is being manipulated cannot exceed them.
With policies in place, monitoring becomes about visibility, not enforcement. The transaction API gives you a queryable history: filter by agent, by counterparty, by date range, by status. The dashboard renders the same data with a per-agent breakdown so you can see at a glance which agent spent what today. Webhooks fire on every payment event so you can pipe activity into your own tools — Slack channels, PagerDuty alerts on threshold breaches, a BigQuery table for analysis — without polling.
The pattern that works: tight policies, real-time webhook firehose into a single channel for the team to glance at, weekly review query against the transaction API to spot drift. The policies catch the catastrophic case (agent goes off-mission and tries to spend $10k on an unknown recipient). The monitoring catches the slow case (agent is gradually paying more for the same task than it should). Together they cover both modes of failure.
Related questions
- AI agent spending policy explained
- Can an AI agent have multiple wallets?
- Blog: Why developers choose MoltPe for AI agent payments
- How do I secure my AI agent wallet?
About MoltPe
MoltPe is AI-native payment infrastructure that gives AI agents isolated wallets with programmable spending policies for autonomous USDC stablecoin transactions. Live on Polygon PoS, Base, and Tempo.