Use case

Agent to Agent Payments - Settle Between Agents Safely

Name: Agentspay
Brand: Agentspay

Agents are starting to hire other agents. When one agent pays another, you need identity, limits, and a record on both sides of the transaction.

In short

Agent to agent payments are transactions where one autonomous agent pays another for a service or result. Agentspay settles them under policy: the paying agent draws from a capped wallet, the receiving agent gets a payout into its own wallet, and both sides carry a named identity and owner. The settlement is written to a shared audit trail so the spend and the earning reconcile against each other.

The challenge

When agents transact with each other, an unidentified or unbudgeted counterparty can drain a wallet with no record of who got paid or why.

How Agentspay handles it

Agentspay attaches a named identity and human owner to every agent, so each side of a settlement is accountable. The paying agent is held to its wallet caps and policy, while the receiving agent gets earnings as a payout under the same controls. Both legs are recorded in the audit trail, giving you matching records for the payment and the payout.

Further reading: how AI agents pay each other, agent identity and Know Your Agent, and how to monetize an AI agent.

The control surfaces

The controls behind it

The surfaces this use case leans on, each enforced before any money moves.

AI agent wallet

Agent wallet

An AI agent wallet is a funded, budgeted account that belongs to a single agent. You top it up to a set amount, attach spend limits and rules, and the agent draws from it. Because the wallet is capped and policy-gated, the agent cannot exceed its budget or spend outside the rules you wrote.

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Audit trail

An audit trail for AI agent spending is an immutable, append-only record of every payment an agent made. Each row links the spend to the intent that triggered it, the agent that requested it, the human who owns the agent, the policy verdict, and the approver. It gives you cost attribution, reconciliation, and a record you can defend.

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Spend controls

AI agent spend controls are rules that limit what an agent can buy: per-transaction, daily, and total caps, merchant and category allowlists, and velocity limits. Agentspay evaluates them at the API boundary, so a hallucinating or compromised agent is declined before it can overspend, not flagged after.

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Human approvals

Human in the loop agent payments means an agent's transaction pauses for human sign-off when it crosses a threshold you set. The agent waits, the request is sent to Slack, email, or a webhook, and nothing settles until a person approves or denies. The decision and approver are recorded in the audit trail.

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See the whole flow in how it works, or check what it costs on the pricing page.

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Refund agents

A support agent that can refund a customer resolves tickets faster, but an agent with an open refund button is a liability. Cap each refund, cap the day, and make anything large wait for a person.

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Let this agent spend safely

Fund a capped wallet, write a policy, and issue a scoped virtual card today. Sandbox-first and rail-neutral.

Never moves money without policy