Agent payment authorization and spend control portal

For

Product teams and platform engineers letting AI agents pay and buy on behalf of users

Solves

Agents can act but cannot be trusted with money: card details, limits, approvals, merchant checkout and spend records sit in separate tools.

Delivers

User-authorized agent payments with a complete spend record

Built in

about 5 weeks of creation time, MVP in 6 days

Investment

$14,500 for the MVP, $49,500 for the full product

Run it

Inside your business, or as part of your offer to clients

Give agents a controlled way to pay while the user keeps authority over every transaction.

1. Register agents and issue scoped payment credentials.
2. Replace card details with tokenized credentials.
3. Enforce user spending limits and transaction rules.
4. Route transactions above thresholds to passkey-style user approval.
5. Process payments through PCI-compliant handling.
6. Provide developer SDKs for agent integration.
7. Offer a no-setup testing playground for the full flow.
8. Orchestrate existing payment processors without merchant changes.
9. Apply consented personalization to shopping requests.
10. Search merchant catalogs for live variants, pricing and checkout estimates.
11. Complete checkout on live merchant surfaces and track orders to confirmation.
12. Return normalized order data and send transaction webhooks.
13. Capture affiliate commissions on agent-driven purchases.
14. Expose an MCP server for direct agent integration.
15. Present signed agent identity for cooperative bot mitigation.
16. Support microtransactions and prevent double spending.
17. Categorize expenses and track budgets in real time.
18. Sync accounts and spend data across devices.
19. Compare the reviewed result with the recorded baseline and value assumptions.
20. Capture corrections and named-owner approval before consequential use.
21. Export a versioned user-authorized agent payments with a complete spend record with source references and unresolved questions.

Everything these tools do, in one app

• Agent-initiated payments Allows AI agents to make payments or complete purchases on behalf of users without manual intervention.Found in Prava, Visa Intelligent Commerce, CartAI and 1 more
• Tokenized card security Uses secure tokens instead of full card details to protect user information during transactions.Found in Prava, Visa Intelligent Commerce, CartAI and 1 more
• Spending limits and controls Lets users set spending limits and transaction rules to maintain oversight and prevent misuse.Found in Prava, Visa Intelligent Commerce, Fewsats
• Approval workflows Provides passkey-style approvals or other mechanisms for users to authorize agent transactions.Found in Prava
• PCI-compliant payment handling Processes payments in a PCI-compliant manner, reducing compliance burden for developers.Found in Prava, CartAI
• Developer SDKs Offers software development kits for easy integration into AI applications.Found in Prava, Fewsats
• Testing playground Provides a no-setup environment to test the full agentic payment flow.Found in Prava
• Existing payment processor orchestration Works over existing payment processors so agents can transact without merchants changing their stacks.Found in Prava
• Personalized shopping experiences Enables AI-powered personalization for shopping with user consent.Found in Visa Intelligent Commerce
• Catalog search Searches across merchants for live product variants, pricing, and checkout estimates.Found in CartAI
• Checkout completion Completes purchases on live merchant surfaces and tracks orders to confirmation.Found in CartAI
• Order data and webhooks Returns normalized order data and sends webhooks for transaction updates.Found in CartAI
• Commission capture Automatically captures affiliate commissions from agent-driven purchases.Found in CartAI
• MCP server integration Provides an open-source MCP server for direct integration into AI agents like Claude or Cursor.Found in CartAI
• Cooperative bot mitigation Uses signed agent identity and Web Bot Auth to cooperate with bot detection systems.Found in CartAI
• Microtransaction support Enables AI agents to handle numerous small payments efficiently.Found in Fewsats
• Double-spend prevention Prevents double spending and unauthorized transactions.Found in Fewsats
• Automatic expense categorization Automatically categorizes expenses to streamline budget management.Found in Walle
• Real-time financial tracking Provides real-time tracking with detailed reports and visualizations.Found in Walle
• Bank account integration Securely integrates with multiple bank accounts and payment platforms.Found in Walle
• Budget goals and alerts Allows setting customizable budget goals and alerts to maintain spending discipline.Found in Walle
• Multi-device synchronization Synchronizes financial data across smartphones, tablets, and desktops.Found in Walle

What goes in, what comes out

The workflow

1. In
   Start with

   Tokenized card credentials, user spending rules, approval decisions, merchant catalog and order data

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect tokenized card credentials

4. 3

   User spending rules

5. 4

   Approval decisions

6. 5

   Merchant catalog and order data

7. 6

   Then follow this sequence: 1

8. Out
   Finish with

   User-authorized agent payments with a complete spend record

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the three stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One sandbox merchant set and test card range; final payment authorization and dispute decisions remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Agent and credential setup, Live transaction monitor, User approval and spend review. Use a portfolio list of agents and connected accounts, a central transaction timeline, and a right-hand panel for limits, rules and approvals. Let users compare attempted, authorized and settled states side by side. Display pending, approved, declined and reversed states. Provide a user approval link with the merchant, amount and agent identity attached. Make the task-specific outcome user-authorized agent payments with a complete spend record visible beside its evidence, review state and value baseline.

Accounts and administration

Agent ownership, credential versions, user comments, approval states, usage allowances, transaction limits, download history and a rights record for supplied material. Add organization access boundaries, named reviewers, usage caps, data retention controls, export logs and explicit approval for external actions.

Integrations and data access

User-owned bank accounts, authorized merchant catalogs and permitted payment processors. Cloud credential storage, design-file import/export and publishing destinations. Start with file exchange and validate destination specifications before promising direct publishing. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   6 days

   One buyer segment, one recurring use case; first modules: register agents and issue scoped payment credentials; replace card details with tokenized credentials. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   7 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   2 weeks

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will product teams and platform engineers letting AI agents pay and buy on behalf of users use it to solve "agents can act but cannot be trusted with money: card details, limits, approvals, merchant checkout and spend records sit in separate tools"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Agree quality and outcome thresholds before the pilot using this measure: Authorized transactions completed without manual intervention and unauthorized or duplicate charges per 1,000 transactions.
4. Measure, then decide. Track authorized transactions completed without manual intervention and unauthorized or duplicate charges per 1 and 000 transactions; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. Pilot scope: One sandbox merchant set and test card range; final payment authorization and dispute decisions remain human. Implement one approved input format, a bounded representative case set and the first two task modules: register agents and issue scoped payment credentials; replace card details with tokenized credentials. Support the third module with operator review: enforce user spending limits and transaction rules. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.

After the MVP. Once paid pilots prove usefulness, automate repeatable reviewed steps and add one verified source integration. Expand supported inputs and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around user-authorized agent payments with a complete spend record. Retain the explicit scope boundary: One sandbox merchant set and test card range; final payment authorization and dispute decisions remain human.

What the build depends on. Credential upload and preview, asynchronous payment jobs, editable version history, reviewer access and tested export formats. High-fidelity production requires specialist payments QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One sandbox merchant set and test card range; final payment authorization and dispute decisions remain human.

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Test a USD 300-1,500 fixed pilot for one defined agent payment package. Offer a monthly transaction allowance after repeat demand. Quote complex merchant integrations or specialist compliance work separately. These are test prices, not market benchmarks. Package the initial sale as one bounded user-authorized agent payments with a complete spend record. Recurring fees must specify volume, review depth and integration support. For exchanges, test a disclosed coordination or successful-service fee rather than holding customer funds. Reprice only after measuring real delivery labor; platform-build cost is separate from a commercial pilot fee.

Message to test

Give agents a controlled way to pay while the user keeps authority over every transaction. Demonstrate a concrete user-authorized agent payments with a complete spend record using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Product teams and platform engineers letting AI agents pay and buy on behalf of users professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample user-authorized agent payments with a complete spend record from a small authorized input set, with a transparent calculation of authorized transactions completed without manual intervention and unauthorized or duplicate charges per 1,000 transactions and no promised savings.

The first 30 days

1. Week 1: interview five product teams and platform engineers letting AI agents pay and buy on behalf of users and inspect a recent example of agents can act but cannot be trusted with money: card details, limits, approvals, merchant checkout and spend records sit in separate tools.
2. Week 2: prepare a consented or synthetic demonstration of the three task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure authorized transactions completed without manual intervention and unauthorized or duplicate charges per 1,000 transactions, reviewer effort and repeat-purchase interest. This is a demand-validation plan, not a thirty-day full-product delivery promise.

Paid pilot

Agree quality and outcome thresholds before the pilot using this measure: Authorized transactions completed without manual intervention and unauthorized or duplicate charges per 1,000 transactions. Continue only if the buyer accepts the actual output, the intended job outcome improves without unacceptable errors, and measured delivery cost fits willingness to pay. Revise or stop if access is unavailable, qualified review cannot be provided, or apparent savings disappear after corrections and support. Use held-out cases when comparing model quality; use a properly reviewed comparison design before making causal claims. Record missing cases and negative results alongside successful outputs.

Success metrics

Authorized transactions completed without manual intervention and unauthorized or duplicate charges per 1,000 transactions; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs user-authorized agent payments with a complete spend record. Retain permissioned settings and reviewed examples, report realized value honestly, and sell increased volume or adjacent approved workflows only after contribution margin and quality remain acceptable.

Why clients would pick it

A reusable library of approved agent profiles, spend rules and review examples, together with reliable delivery for a narrow payments niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for product teams and platform engineers letting AI agents pay and buy on behalf of users. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Walle, Prava, Visa Intelligent Commerce, CartAI and Fewsats, plus manual checkout and in-house payment scripts. Compare this product with the buyer's present method on authorized transactions completed without manual intervention and unauthorized or duplicate charges per 1,000 transactions. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Payment processing attempts, sandbox merchant access, storage, reviewer hours, client revision rounds and licensed source assets. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of user-authorized agent payments with a complete spend record. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve user consent, source attribution, transaction accuracy and usage permissions. Users approve substantive changes and payment scope. One sandbox merchant set and test card range; final payment authorization and dispute decisions remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.