Using AI Coding Agents to Integrate a Payment Vault

It’s a problem early-stage founders or small payment teams will immediately recognize: building a compliant payment vault from scratch is not a feasible option.

So you find a payment vault. And instead of handing documentation to an engineering team, you send links to your favorite AI coding agent. Claude Code, Cursor, Copilot, all ready and willing to dive in.

Shorter Integrations Without Cutting Compliance Corners 

Integrating a payment vault has never been one big task. It's dozens of small ones: embedding secure fields in a checkout form, creating tokens, scoping API keys and permissions, configuring a proxy to forward card data to a processor, and testing each step in a sandbox. None of it is especially hard. All of it requires knowing the exact endpoint, parameter, or SDK method for the job, which is where integrations have traditionally slowed down.

Coding agents are good at exactly this kind of work, as long as they have accurate context. An agent working from incomplete or ambiguous documentation guesses, and in payments, a guess means a failed request, a misconfigured permission, or a debugging session that eats the afternoon and fails to meet the need.

Basis Theory's documentation is built to give agents that context directly. Each guide walks through a complete flow with working code samples across our server-side, web, and mobile SDKs, so an agent can follow an integration end-to-end instead of piecing it together from scattered references.

Ramp saw the same effect at a much larger scale. Its engineering team implemented Basis Theory in less than a quarter, using AI to work with the documentation throughout.

"The totality of Basis Theory, from the documentation, AI-assisted development, the whole suite, it's a really good toolbox to get new work developed," says Arnold Lin, staff software engineer on Ramp's security team.

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Faster Incident Response Times 

AI changed how Ramp does troubleshooting. Instead of searching documentation manually, Lin queries it. The agent finds the relevant material, compares it against the problem, and proposes a fix.

"No need to track down which specific page applies; [the AI is] very capable of working out a possible solution," Lin says. "What used to take an hour of troubleshooting can now be done in 15 minutes."

The same applies to routine maintenance. Updating a proxy configuration or setting used to mean digging through the docs for exact call names. Today, Lin describes the outcome he wants and lets the agent find the specifics.

“With the AI’s help, I can ask it to do something, and Basis Theory’s documentation is good enough to support any of the possible use cases,” Lin says. “That slashes development time from days to hours.”

Implementation time is often why teams choose an orchestration platform over a vault. AI-assisted development changes that math, and it doesn't come at the expense of security.

Because Basis Theory Elements capture payment data before it reaches your servers, the code an agent writes works with tokens, not raw card numbers. An agent can build the integration without your systems ever handling the data PCI DSS is designed to protect.

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Use Cases to Explore 

These map to real flows in the platform.

• Integration planning by use case: Describe your type of business (vSaaS, travel agency, multi-PSP merchant) and get a recommended architecture before writing code.
• Card capture and tokenization: Elements plus token creation.
• Proxy configuration: Forwarding tokenized card data to a processor or partner endpoint.
• Writing Reactors: Custom logic running against vaulted data.
• Processor migrations: Planning a token migration from Stripe or another PSP.
• Sandbox testing: Direct an agent to generate test requests and validate a flow end to end before production.

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Setting Up Your Agent to Work With a Payment Vault 

Ramp shows what's possible. Here's how to set up your own agent to work with Basis Theory.

Start with the right context. And give your agent a specific outcome, not just general instructions. For example:

• Using the Basis Theory docs, add secure card capture to our checkout form with Elements, store the resulting token and forward it to our processor through the Basis Theory Proxy. Use our sandbox tenant.

This kind of prompt names the products, the flow and the environment, so the agent can follow a complete guide instead of guessing at pieces.

Reference documentation explains how each endpoint works. It doesn't tell an agent which approach fits your business, and that's often where integrations go sideways.

Instead of assembling an architecture from individual endpoints, an agent can start from how merchants with a similar use case have actually integrated. A few ways to keep agent-built integration secure:

• Build in the sandbox first. Test and build a full flow in this environment before anything touches production.
• Scope any API keys. Give the agent a key with only the permissions the task requires. Never put production keys in prompts, agent configuration files, or code.
• Use Elements to handle card data. When the code your agent writes is working with tokens, and not raw data, the agent never sees a card number.

After a team is comfortable building with agents, the next step is to confirm that its own product can use agents to act on sensitive data.

Basis Theory built basistheory.ai for these teams. The Agentic Payments API lets an agent pay with a scoped, single-use credential minted for a specific merchant, amount and time window. PCI Agent Runtimes let teams run their own agent code and models inside Basis Theory's PCI Level 1 environment, so the agent can work with real card data without pulling the application into PCI scope.

These are the use cases we've seen so far, and we expect customers to find more. If you're using a coding agent with Basis Theory in a way we haven't covered, we want to hear about it.

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