OpenClaw Checkout Controls: Limits, Merchant Locks, and Velocity Caps

If you’re connecting OpenClaw to payments, you’ll quickly discover a painful truth:

“Per-transaction limit” is not a safety system.

It’s a single guardrail. You need a stack.

This post lays out the control stack that reliably prevents the two most common failures:

• ▸runaway retries (duplicate charges)
• ▸merchant drift (spend moves to the wrong place)

For the baseline flow, start here: OpenClaw Payments: Intent → Card → Verify.

Control stack (from strongest to weakest)

1) Funding isolation (dedicated card per workflow)

If the agent can’t access your main card, your worst case is capped by design.

See: Why agents should never share credentials.

2) Intent gating (mandatory)

No intent, no spend.

Intent is the only scalable way to tie actions to later audits and disputes.

3) Approval thresholds (human-in-the-loop)

Define a policy boundary:

• ▸under $X: auto-approve
• ▸over $X: require approval

You can still run fully autonomously for low-value purchases, while preventing catastrophic surprises.

4) Merchant locks (allowlist > blocklist)

If you know the merchant, lock to it.

Examples:

• ▸expectedMerchant="figma.com"
• ▸allowlist specific merchant IDs when possible

Merchant drift is especially dangerous when you share a token used for many unrelated purchases.

See: Merchant Drift.

5) Category/MCC controls (coarse but useful)

MCC controls are not perfect, but they stop obvious abuse:

• ▸travel-only card can’t buy gift cards
• ▸infra-only card can’t transact at gambling merchants

Use these as hard outer bounds, then use merchant allowlists as the precise inner bound.

6) Velocity caps (the retry-loop killer)

Retry loops happen because agents are resilient. That’s good—until it isn’t.

Add velocity caps like:

• ▸max 1 authorization per merchant per minute
• ▸max N authorizations per card per hour
• ▸max total daily spend per agent

If you only implement one “smart” control, implement velocity.

Where controls live in the flow

Think in phases:

1. ▸Before credentials: evaluate intent against policy
2. ▸At authorization time: enforce hard limits + merchant/category constraints
3. ▸After purchase: verify + reconcile

The earlier you stop bad spend, the cheaper it is.

Copy/paste policy checklist

Use this as the default policy template for OpenClaw agent spend:

• ▸Auto-approve threshold: $25 (starter) or $50 (more permissive)
• ▸Require approval if:
  • ▸expectedAmount > threshold
  • ▸expectedMerchant is missing for ecommerce
  • ▸intent purpose is vague (“buy stuff”, “purchase”, “pay invoice”)
• ▸Merchant allowlist when possible
• ▸Category/MCC allowlist for coarse boundaries
• ▸Velocity cap: 1 auth/min/merchant, 3 auth/5min/card
• ▸Re-lock card after completion or at window expiry
• ▸Verify transaction matches intent and persist evidence log

Common mistakes

“We’ll rely on monitoring”

Monitoring is detection, not prevention.

Hard controls stop spend before it happens.

“We’ll use a shared token + policies”

Shared tokens create noisy baselines and expand blast radius when policies fail.

“We don’t need verification; we’ll check statements later”

By the time you see a statement, you’re already in “damage control.”

Verification is what turns a payment system into an auditable system.

Bottom line

The fastest safe path for OpenClaw:

• ▸isolate funding
• ▸require intent
• ▸lock merchants/categories
• ▸cap velocity
• ▸verify and log

Do that and you can ride the ecosystem momentum without becoming the next “agent spend incident” thread.