AgentsPay

Explainer

Tipalti MCP Server and Tipalti AI Agents With Tipalti API Payment Controls

Tipalti now documents an official MCP server that connects Claude to your payables data, and its own AI agents already approve bills and match POs inside the product. Before an AP team lets any agent near supplier payments, the controller has to answer one question in the review: what stops it paying? We read Tipalti's MCP documentation, its OAuth metadata and every published REST operation to find out.

Agent Payments Console

Pick an agent

Payment intent

intent: ▌

Policy evaluation

Human approval required

This spend is over your approval threshold. Approve it to issue a scoped card, or deny it.

Scoped virtual card issued

AgentsPay

single-use

Wallet budget

spent of

Audit trail

In short

The Tipalti MCP server is Tipalti's official, beta Model Context Protocol server at https://mcp.tipalti.com/mcp, available today for Claude Enterprise only and documented as read-only. Its OAuth server, however, already publishes 12 write scopes, including payment batches, payments and payee records. The Tipalti REST API behind it lets one call create a batch of up to 5,000 payments, and none of its 161 write fields is an amount ceiling, a budget or an approver. Tipalti's batch approval is real but optional, so a spend limit for an AI agent has to live outside the token the agent holds.

What the Tipalti MCP server does today

Tipalti runs one hosted MCP server, at https://mcp.tipalti.com/mcp. Its documentation page was last updated on 11 October 2026 and calls the product a beta that is rolling out gradually: you ask your Tipalti representative whether your account is eligible. It currently works with Claude Enterprise only, and Tipalti says other AI platforms come in a later rollout. So if your team lives in ChatGPT or Copilot, there is no Tipalti connector for you yet.

Setup is short once a Payer administrator turns on AI Integration under Administration. In Claude Code it is one line, claude mcp add --transport http --client-id mcp-public tipalti https://mcp.tipalti.com/mcp, with no client secret. Claude Desktop connects through mcp-remote with the same public client ID. In Claude.ai your IT or AI platform owner adds it as a custom connector, picks "Sign in when needed" and "Use your own OAuth client", enters mcp-public and leaves the secret blank. Each user then clicks Connect.

Once connected, Claude can answer questions about bills (details, statuses and aging), payees, payments and their statuses, virtual account balances and funding requirements, and cards with their transactions. Tipalti says every request is checked against the user's own role and permissions, the scopes the Payer administrator enabled and the account, and that the MCP "doesn't grant any access beyond what you already have in Tipalti." For a controller who wants to ask "which bills over 30 days are still unpaid," that is genuinely useful, and it is the safe place to start.

Accounts payable manager at an office desk reviewing a batch of supplier payments on a monitor before approval

How we probed Tipalti, with a control for every check

We did not rely on the announcement. On 19 October 2026 we pulled four public sources and paired each with a control that should have produced the opposite result:

  • The MCP host. GET /mcp returned 401 with WWW-Authenticate: Bearer resource_metadata=".../.well-known/oauth-protected-resource/mcp", and a JSON-RPC initialize sent by POST without a token also returned 401. A randomly generated path on the same host returned 404, and so did the root. The host is real, and it is not a catch-all that answers everything.
  • The OAuth metadata. The protected-resource document and the authorization-server document both publish the same 26 scopes: openid, offline_access, and a read and a write scope for each of twelve resources. The server supports the authorization code and refresh token grants, PKCE with S256 only, public clients with no secret, and it has no dynamic registration endpoint, so only clients Tipalti has registered (such as mcp-public) can connect.
  • The API reference. documentation.tipalti.com/llms.txt indexes the REST reference, and every reference page embeds its OpenAPI 3.0.1 definition (build 2026-08-11). We parsed 36 operations. A random path under the docs returned 404.
  • The limit vocabulary. We searched all 161 input fields on the 12 write operations for approval, limit, max, budget, threshold, cap, ceiling, daily, monthly and cumulative. The search is not blind: it fired on two fields, and we read both by hand. The section below explains why neither one limits the caller.

We did not sign in, so we cannot see the list of tools the MCP server exposes after authentication. Everything on this page comes from what Tipalti publishes without a login, which is also what anyone can re-check.

Read-only today while the OAuth server already lists write scopes

Tipalti is clear that the MCP "is read-only in this release, so it can't create or change anything in your account." We take that at face value, and it is the right call for a beta. The detail worth knowing is where that promise is enforced.

The same authorization server that issues tokens to Claude publishes 12 write scopes next to the read scopes: tipalti.api.payment.write, tipalti.api.payment-batch.write, tipalti.api.payee.write, tipalti.api.invoice.write, tipalti.api.invoice-batch.write, tipalti.api.payment-term.write, tipalti.api.gl-account.write, tipalti.api.custom-field.write, tipalti.api.tax-code.write, tipalti.api.payer-entity.write, tipalti.api.report1099.write and tipalti.api.report1042.write. In other words, read-only is a property of the tools in this release, not of the authorization layer, which already speaks the vocabulary of creating payments and editing payees.

That is not a flaw. It is a preview of where the product is heading, and Tipalti's own pricing page already describes "one agentic AI platform." The practical takeaway for a controller is timing: decide where an agent's payment limit lives before write tools ship, not after a pilot has been running on a token that can suddenly do more.

The Tipalti API can create a 5,000-payment batch with no amount cap

Most agent builders will not wait for MCP write tools. They will call the Tipalti REST API directly, which is what Tipalti's AP integrations already do. Of the 36 published operations, 24 are reads and 12 are writes (7 POST, 5 PATCH, no DELETE). The one that moves money is POST /api/v1/payment-batches, which needs the tipalti.api.payment-batch.write scope.

A single call accepts up to 5,000 payment instructions; more returns an error. Each instruction carries a payee ID, an amountSubmitted with amount and currency, a mandatory unique refCode, an optional scheduled date and a handful of tax switches. That is the whole contract. There is no field for a maximum per payment, a budget for the batch, a running total for the month or the name of an approver.

The API is also fast enough that volume is never the bottleneck. Tipalti's rate limiter allows up to 1,500 requests per minute before it returns 429. Invoice batches take up to 200 invoices per call, with up to 100 lines each. An agent that misreads a CSV, or follows an injected instruction in a vendor email, is constrained by your Tipalti balance and by whatever approval phase your account has, and by nothing in the request itself.

Two credentials can make that call. With the client credentials flow, an app holds a Client ID and Client Secret with its chosen scopes and calls the API with no user present. With the authorization code flow and PKCE, a payer admin authorizes the scopes once and the app keeps a refresh token. Either way, the limit on what the agent pays is not in the token.

isThresholdIgnored and max withholding are switches the caller holds

Our limit search returned exactly two hits across 161 write fields, both on the payment instruction, and both point the same way.

isThresholdIgnored is documented as: "In a case where the payment exceeds or doesn't meet the threshold, indicates whether the system ignores the payment threshold and executes the payment." So Tipalti does keep a payment threshold, which is a real control, and the API lets the caller tell the system to skip it for that payment. isMaxWithholdingApplied does the same for tax: when a payee is unpayable because their tax form is incomplete, it lets the caller pay them anyway at the maximum withholding rate.

Both switches exist for good reasons. Finance teams need to push an urgent payment past a minimum, or pay a contractor whose W-8 is stuck in review. The point for agent design is who holds them. Any process with the batch write scope can set either flag to true, so a threshold you rely on to stop an oversized payment is one boolean away from not applying. If an AI agent writes your batches, those two fields belong on the list of things it should never set without a person.

Supplier bank details can change through the Tipalti API

Vendor bank detail fraud is a classic business email compromise pattern, and it never needs a large payment. It needs one changed routing number. In Tipalti that change is an API call: PATCH /api/v1/payees/{id}/payment-method, under the same tipalti.api.payee.write scope that creates and updates payees.

The request accepts an ACH block with nameOnAccount, accountNumber, routingCode, bankName and accountType, an eCheck or wire block with IBAN and SWIFT, a PayPal email and a check mailing address (PayPal and check are US only, and currency cannot be changed through REST). There is no confirmation field, no second approver and no hold period in the request.

Tipalti does give you real protection around payees. Payees can sit in statuses such as pending review, pending compliance review, pending tax form approval, blocked and suspended, and a payer can require review of updates a payee makes in the iFrame. Those controls are strong for changes made by the supplier. The question for an agent rollout is narrower: should the same credential that pays suppliers also be able to repoint where they are paid? Our answer is no. Keep payee.write off any agent's token, and route bank changes to a person who calls the supplier back on a number already on file.

Controller checking a supplier bank account change request on a laptop next to printed vendor records

Tipalti batch approval is real but optional and per batch

Tipalti deserves credit here. Its payment flow guide says that "during implementation, you can choose to configure an approval phase before the payment batch is sent out. If you do, each payment batch will require approval in Tipalti Hub before it's processed." No published API operation approves a batch, so with that phase on, a person in Tipalti Hub has to release the money. That is a stronger default than several APIs we have measured, including the Mercury direct send that skips approval rules entirely, covered on our Mercury MCP server and agent cards page.

Three limits matter for agents. First, the phase is optional and chosen during implementation, so check whether your account has it before assuming it does. Second, it is per batch: a reviewer sees one batch of up to 5,000 payments and approves or declines it as a unit, which is a poor fit for an agent that submits a steady stream of small batches. Third, it knows nothing about which agent created the batch or how much that agent already spent this month.

Insufficient balance is the other built-in brake. A payment without enough funds moves to Pending funds (PENDING_PAYER_FUNDS) and Tipalti notifies you, so the account balance works as a hard ceiling. It is a ceiling on the whole company, though, not on one agent. Tipalti also documents more detailed statuses, including PENDING_APPROVAL, as arriving later.

What a finance lead adds before an agent touches Tipalti

None of this means you should keep AI out of payables. It means the controls an auditor will ask about have to be designed in, and most of them sit outside Tipalti. Before an agent writes to Tipalti, we would put these in place:

  1. Start with the MCP, read-only. Let Claude answer aging, status and funding questions for a month. It is useful on day one and cannot move money.
  2. Give each agent its own OAuth app and the narrowest scopes. An agent that pays approved bills needs payment-batch.write and reads. It does not need payee.write, payment-term.write or tax-code.write.
  3. Turn on the batch approval phase and confirm it with your Tipalti implementation contact, rather than assuming it is on.
  4. Put a per-agent budget and a per-payment ceiling in front of the API. The agent asks a policy service before it builds the batch, and the policy decides by amount, payee and month to date. Our spend controls for AI agents do exactly this.
  5. Require a named human above a threshold, per payment rather than per batch, with approval routing for agent payments that records who approved what.
  6. Never let the agent set isThresholdIgnored or isMaxWithholdingApplied. Strip them in the policy layer.
  7. Log the agent, not just the user. Tipalti records the token's actions. You also want the agent's identity, the instruction it acted on and the policy decision.

The same pattern applies across the AP stack. We measured the equivalent gaps on the Bill.com MCP server and API, where limits sit behind an admin token, and on the QuickBooks and NetSuite MCP servers, where the vendor governs which tools an agent gets but never the amount.

How AgentsPay works alongside Tipalti

AgentsPay does not replace Tipalti. Tipalti stays your AP system, supplier portal, tax engine and payment rail. AgentsPay sits between your agents and Tipalti and decides, payment by payment, whether a request goes through.

Each agent gets its own identity, a monthly budget, a per-transaction ceiling and an approval threshold with a named approver. Before the agent calls POST /payment-batches, it asks AgentsPay with the payee, amount and reason. Under the threshold and inside the budget, it proceeds. Over the threshold, the approver gets the request with the context and approves or declines. Every decision is written to an audit trail you can hand to an auditor. Because the same policy covers cards, bank transfers and agent payment protocols, a Tipalti payout and a SaaS purchase on a virtual card count against the same budget.

If you are pricing the Tipalti side of this, our Tipalti pricing breakdown covers the published plans and the per-payment fees that an agent drives. The agent side is on our AgentsPay plans and pricing page.

Whatever standard moves the money, AgentsPay is the rail-neutral control plane that keeps it governed. See how it works and the control surfaces that enforce policy, approvals, and audit on every transaction.

Original research

Tipalti API and MCP by control, read 19 October 2026

From the OpenAPI definitions on the reference pages indexed at documentation.tipalti.com/llms.txt and the MCP server's published OAuth metadata.

Surface Count Human approval Spend limit available
Published REST operations 36 No operation approves a batch None in any request
REST write operations 12 7 POST, 5 PATCH, 0 DELETE 161 input fields, 0 amount ceilings
Create payment batch 1 Only if the optional approval phase is on, per batch No, up to 5,000 payments per call
Threshold and withholding switches 2 fields No Both override a control rather than set one
Update payee payment method 1 No Changes ACH, wire, PayPal or check details
MCP OAuth scopes published 26 Not applicable 12 of them are write scopes
MCP tools in this release Read-only Not needed Cannot create or change anything
API rate limit 1,500 per minute Not applicable Not a spend control

Controls

The checks we ran so the numbers can be trusted

Each probe was paired with a control that should have produced the opposite result.

Probe Result Control Control result
GET mcp.tipalti.com/mcp 401, Bearer resource_metadata Random path on mcp.tipalti.com 404, so the host is not a catch-all
POST initialize without a token 401 Server root 404
oauth-protected-resource metadata 26 scopes, 12 write oauth-authorization-server metadata Same 26 scopes, PKCE S256, no registration endpoint
documentation.tipalti.com/llms.txt reference index 36 operations parsed Random docs path 404
Limit and approval words in 161 write fields 2 hits, read by hand Payment instruction schema Fired on isThresholdIgnored and isMaxWithholdingApplied

Comparison

What Tipalti governs and what it leaves to you

Tipalti controls from its own documentation, next to the controls a finance team needs for an AI agent.

Control Tipalti AgentsPay
Role-based permissions on data Yes, every MCP request checks role and scopes Yes, per agent
Human approval before money moves Yes if the batch approval phase is configured Yes, above a threshold you set
Approval threshold by amount No, the whole batch or nothing Yes, per payment and per agent
Per-payment ceiling No, and the payment threshold can be ignored by the caller Yes
Monthly budget per agent No, only the company balance Yes
Payee review and compliance holds Yes, strong for supplier-made changes Not needed, Tipalti does this well
Block bank detail changes by an agent Only by withholding the payee.write scope Yes, policy denies the action
One budget across Tipalti, cards and other rails No Yes

Frequently asked

Questions people ask about Tipalti MCP Server

Does Tipalti have an MCP server?

Yes. Tipalti runs an official MCP server at https://mcp.tipalti.com/mcp. It is in beta, rolling out gradually, and currently supports Claude Enterprise only. A Payer administrator turns on AI Integration first, then users connect from Claude Code, Claude Desktop or Claude.ai with the public client ID mcp-public. It reads bills, payees, payments, account balances and cards.

Can Tipalti MCP make payments?

No, not in the current release. Tipalti documents the MCP as read-only, so it cannot create or change anything in your account. Its OAuth server does already publish write scopes for payments, payment batches and payees, which suggests write tools are a matter of time. Payments today go through the Tipalti REST API or the Tipalti Hub.

How do I connect Tipalti to Claude?

Ask your Payer administrator to turn on AI Integration in Tipalti Administration and confirm your account is enrolled in the beta. In Claude Code run claude mcp add --transport http --client-id mcp-public tipalti https://mcp.tipalti.com/mcp. In Claude.ai, an admin adds a custom connector with that URL and the client ID mcp-public, with no secret.

Does Tipalti MCP work with ChatGPT?

Not yet. Tipalti says its MCP currently supports Claude Enterprise only and that support for other platforms is planned for a later rollout. Teams on ChatGPT, Copilot or Gemini today either wait, or build their own integration on the Tipalti REST API, which brings its own payment scopes and needs its own spend controls.

What are Tipalti AI agents?

Tipalti AI agents are agents built into Tipalti for reporting, bill approvals, invoice scanning, tax form scanning, ERP sync and PO matching. They work inside Tipalti's own workflow and permissions. They are different from an outside agent, such as Claude or a custom AP bot, that you connect to Tipalti through MCP or the API.

Does the Tipalti API require approval for payments?

Only if your account has an approval phase. Tipalti lets you configure one during implementation, and then each payment batch needs approval in Tipalti Hub before it is processed. No API operation approves a batch. Without that phase, a batch created through the API is validated and paid, subject to your balance.

Can an AI agent change a supplier bank account in Tipalti?

Yes, if its token has the tipalti.api.payee.write scope. PATCH /api/v1/payees/{id}/payment-method accepts a new account number and routing code, IBAN and SWIFT, PayPal email or check address, with no approver field. Keep that scope off every agent token and route bank changes to a person who verifies them with the supplier.

What scopes does the Tipalti API use?

Tipalti uses OAuth 2.0 scopes in the form tipalti.api.resource.read and .write for twelve resources: payer entities, payees, GL accounts, custom fields, payments, payment batches, payment terms, invoices, invoice batches, 1099 reports, 1042 reports and tax codes, plus openid and offline_access. Creating payments needs tipalti.api.payment-batch.write.

What is the Tipalti API rate limit?

The Tipalti API allows a maximum of 1,500 requests per minute. Above that it returns HTTP 429 Too Many Requests, and every successful response carries X-Rate-Limit-Limit, X-Rate-Limit-Remaining and X-Rate-Limit-Reset headers. One payment batch call can carry up to 5,000 payments, so the rate limit is not a practical cap on spend.

How much does Tipalti cost?

Tipalti Accounts Payable starts at 99 dollars a month and Tipalti Mass Payments starts at 249 dollars a month, both with unlimited users and no per-approver fee. Transaction pricing per invoice and per payment applies on top and is quoted, not published. Our Tipalti pricing guide walks through a full budget.

Is Tipalti MCP safe to connect to our AP data?

It is a reasonable first step. The current release is read-only and every request is checked against the user's role, the enabled scopes and the account, so Claude sees only what that person already can. Treat the read data as sensitive, since payee and payment details are exactly what a prompt-injected client could leak.

How do I cap what an AI agent can pay through Tipalti?

Put a policy check in front of every payment batch. The agent asks a spend control layer such as AgentsPay with the payee, amount and reason, and calls the Tipalti API only after approval. AgentsPay holds a per-agent monthly budget, a per-payment ceiling and an approval threshold, and blocks the threshold and withholding override flags.

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Shopify Agentic Commerce

Shopify Agentic Commerce

Shopify switched agentic commerce on by default, so your store is probably already selling to AI assistants whether or not anyone on your team configured it. Instead of restating the announcement, we checked something you can check too: on September 2, 2026 we requested the machine-readable capability file that Shopify publishes for real storefronts, on fourteen well-known US brand domains, and read what it exposes to an agent. Eleven answered correctly. The three that did not share one trait, and it is quietly costing them agent traffic.

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Web Bot Auth

Web Bot Auth

Web Bot Auth is the reason your agent either gets served or gets throttled with the scrapers. Almost everything written about it repeats the same architecture diagram, so we did something different: on September 1, 2026 we fetched the published key directories of more than twenty major AI operators and infrastructure vendors to see who is genuinely signing their traffic. Four were. The results are in the first table.

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Tempo Blockchain

the Tempo blockchain

Tempo is the payments chain Stripe and Paradigm built, and it shipped with a protocol that lets software pay for things on its own. It settles machine payments in under a second. It has nothing at all to say about whether your agent should have paid.

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AI Agent Governance

AI agent governance

Every agentic AI governance framework published so far governs the same four things: identity, tools, data and prompts. Not one of them carries a budget. Here is what the real frameworks say, which guardrails actually bind at runtime, and what to do about the last mile none of them reach.

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A2A Protocol

A2A Protocol

Most explanations of the A2A protocol stop at the sentence that agents can now talk to each other. That was true in April 2025 and it is no longer the interesting part. A2A shipped version 1.0 in April 2026 under Linux Foundation governance, it runs in production inside Azure AI Foundry and Amazon Bedrock AgentCore, and the questions engineers actually get stuck on are narrower: what an Agent Card commits you to, when to reach for MCP instead, and what happens the first time one of your agents has to pay another one for the work. That last question has a specific answer, and it is not in the core spec.

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Mastercard Agent Pay

Mastercard Agent Pay

Nearly every article about Mastercard Agent Pay is a retelling of one press release from April 2025, the one where Mastercard said AI agents would be able to shop with Agentic Tokens and named Microsoft as the first platform. That was sixteen months ago, and four more things have shipped since. Reading only the launch coverage leaves you with roughly a quarter of the picture, and the missing three quarters are the parts that decide whether you can actually put this into production.

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Visa Intelligent Commerce

Visa Intelligent Commerce

Almost everything written about Visa Intelligent Commerce is a retelling of the April 2025 announcement, when Visa said AI agents would be able to pay with a Visa credential. Three more things have shipped since, including an open agent-identity protocol built with Cloudflare that most coverage does not mention at all. This page is the current version, checked against Visa’s own developer documentation and newsroom in August 2026.

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Stripe agentic commerce

Stripe agentic commerce

Most writing about Stripe and agentic commerce is still a retelling of the September 2025 launch week, when Stripe and OpenAI shipped Instant Checkout and published the Agentic Commerce Protocol together. Stripe has built a good deal more since then, and some of it points in a direction the launch coverage never anticipated. This page is the current version, checked against Stripe’s own documentation in August 2026.

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ChatGPT Instant Checkout

ChatGPT Instant Checkout

Almost every guide to ChatGPT Instant Checkout still reads like it was written the week it launched, walking merchants through how to apply and what the fee will be. OpenAI changed course in March 2026. Here is the accurate version: what Instant Checkout was, what the numbers actually looked like, what replaced it, and which parts of the stack are still very much alive.

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Google AP2

Google AP2

Most guides to Google AP2 still describe an Intent Mandate and a Cart Mandate, because most of them are rewrites of the September 2025 launch post. The specification moved. Here is what the Agent Payments Protocol actually defines today, and the one question it deliberately does not answer.

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Human in the loop AI

Human in the Loop AI

Every guide to human in the loop AI describes the same shape: the agent pauses, a person decides, the agent continues. The shape is right. What almost none of them ask is a harder question, which is where the pause is enforced, because a pause written into the agent's own code is a pause the agent is trusted to honor.

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AI agent cost

AI Agent Cost

Every cost guide for AI agents answers the same two questions: what does it cost to build, and what does it cost to run. Both are answerable, and both are on somebody's invoice. The third question is the one that ends up in a variance report, because the agent also spends your money, and nobody sends you a bill for that.

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Agentic checkout

Agentic Checkout

Nearly every guide to agentic checkout is written for the merchant who wants to receive these orders. Far fewer are written for the company whose agents are placing them, which is odd, because agentic checkout quietly removes the one screen where spending used to get a second look.

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API monetization

API Monetization

Most guides to API monetization argue about which pricing model wins. The harder question in 2026 is who is calling. An API priced for a signed-up developer with a key behaves very differently when the caller is an agent that showed up once, wants one record, and has no account.

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x402 protocol

x402 Protocol

x402 took the one HTTP status code the web never used and turned it into a payment rail machines can drive. The protocol is elegant and genuinely small. The part it deliberately leaves to you is the budget.

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AI procurement agents

AI Procurement Agents

Every major procurement suite shipped agents during 2026. Almost none of them answer the question your controller will ask first, which is what happens when the agent is wrong about a purchase and the money has already moved.

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Agentic payments

Agentic Payments

Agentic payments move money with no human at the checkout. The rails to do it all shipped during 2026. The part most teams have not solved is deciding, before the money moves, whether the agent was allowed to spend it.

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AI agent monetization

AI Agent Monetization

Every AI agent company is rewriting its price list. The models that survive are metered. The ones that quietly fail are the ones where nobody measured what a single task costs to serve.

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Agent payment platforms

AI Agent Payment Platforms

Five different kinds of product now call themselves an AI agent payment platform, and they solve five different problems. Picking the wrong category is the expensive mistake, not picking the wrong vendor inside a category.

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Universal Commerce Protocol

the Universal Commerce Protocol (UCP)

Google and Shopify shipped UCP as an open standard so an AI agent can check out at any merchant that supports it. Here is what the specification actually defines, where it is live for US buyers, and the one thing it deliberately leaves to you.

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MCP Payments

MCP Payments

MCP payments are how an AI agent discovers a payment tool and calls it to move money. The catch: the Model Context Protocol carries the tool call, not the spending decision, so nothing in the stack asks whether the purchase should have happened.

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Visa Intelligent Commerce vs Mastercard Agent Pay

Visa Intelligent Commerce vs Mastercard Agent Pay

Visa Intelligent Commerce and Mastercard Agent Pay are the two big card networks racing to let AI agents pay. They take different routes to the same idea, and neither one decides whether a given purchase should have happened.

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Agentic Commerce Protocol

the Agentic Commerce Protocol

ACP is the open standard behind agentic checkout in ChatGPT. It tells a merchant how to sell to an AI agent. It says nothing about whether your agent should have made the purchase.

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AP2 vs ACP vs x402

AP2 vs ACP vs x402

AP2, ACP, and x402 are the three standards shaping how AI agents pay. They solve different layers of the problem, and most real systems will touch more than one.

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Machine payments protocol

Machine payments protocol

As software starts paying software, machine payments protocols define how value moves without a human at the keyboard. The harder question is how to keep that spending governed.

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Know Your Agent (KYA)

Know Your Agent

KYA, or Know Your Agent, extends the idea of customer due diligence to autonomous software. When an agent spends, you need to know which agent, on whose authority, and under what limits.

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Keep agent spending governed

Add policy, hard limits, human approval, and an immutable audit trail across any protocol or rail. Start in the sandbox today.

Never moves money without policy