I wrote a broad comparison of virtual card APIs back in March that covered Stripe Issuing, Proxy, and AgentCard across all use cases. That post was comprehensive, but the most common question I get is more specific: "I just need a card for my AI agent. Which one should I pick?"
This post is the focused answer. I'm evaluating Proxy and AgentCard through a single lens: can an AI agent use this safely and autonomously? Not "is this a good product" in general. Not "which has the best dashboard." Specifically: if I have a Claude Code agent that needs to buy something during a task, which virtual card product gives me the best combination of safety, automation readiness, and operational simplicity?
The Evaluation Framework
Before comparing products, I need to be precise about what "AI agent readiness" means for a virtual card. There are exactly six things that matter when the cardholder is software, not a person:
- MCP integration: Can the agent interact with the card API directly through the Model Context Protocol, without custom glue code? This is the single most important differentiator for AI agent use cases. If the answer is no, you're writing and maintaining a custom integration layer between your agent framework and the card API.
- Issuance automation: Can a card be created programmatically in under 2 seconds, with the spend limit set at creation time? Agents operate in real-time task flows. Async card provisioning breaks the flow.
- Single-use architecture: Is the card designed to be used once and discarded, or is single-use behavior an afterthought on top of a persistent card model? This affects everything from security properties to cleanup logic.
- Spending controls: Can you set a hard ceiling at issuance (not a periodic limit), so the card is physically incapable of spending more than the authorized amount?
- Credential security: How are card credentials stored? Are they encrypted at rest? Can credentials be retrieved through authenticated tool calls rather than passed in plaintext?
- Human-in-the-loop: Is there a mechanism to ensure a human approves the funding before the agent gets access to the money?
Every other feature — nice dashboard, mobile wallet support, browser extension, merchant category restrictions — is irrelevant for this use case. An AI agent does not have a mobile phone. It does not use browser extensions. It does not browse the web like a human to find the card dashboard.
Proxy: Developer-Friendly, Not Agent-Native
Proxy (useproxy.ai) is a newer entrant that gets a lot right from a developer perspective. Its API is clean, issuance is fast, and the product is explicitly built for programmatic use rather than being a consumer product with an API bolted on. If you're building fintech infrastructure that needs card issuing, Proxy is a legitimate option.
But "developer-friendly API" and "AI agent ready" are not the same thing.
No MCP support
Proxy has no MCP server, which means a custom integration tax applies. You write the glue code, you maintain the glue code, and every time your agent framework or Proxy's API changes, you update the glue code. For a team building a fintech product, this is acceptable overhead. For a developer who wants to give Claude Code a spending card for a task, it is an unnecessary barrier.
Persistent card model
Proxy's cards are persistent by default — designed to be issued and reused over time. Single-use behavior is achievable through configuration and manual lifecycle management, but it goes against the grain of the product's data model. The API is built around cardholders and card lifecycle events that assume the card exists for more than one transaction.
General-purpose positioning
Proxy serves fintech developers broadly. Its trade-offs — cardholder management, KYC flows, multi-card programs — serve platform builders who are issuing cards to their own end users. An AI agent developer who just needs a $10 card for a research task inherits all of that conceptual overhead, even if they never use it. The onboarding, the data model, and the API design all reflect a use case broader than agent payments.
Where Proxy does well
Credit where it's due: Proxy's API design is clean and modern. Issuance is fast. The developer experience is solid for programmatic use. If you have a use case that straddles fintech product development and agent automation, and you're willing to write the MCP integration yourself, Proxy is a reasonable choice — even if it's not purpose-built for AI agents.
AgentCard: Built for the Agent Use Case
AgentCard was designed from the start for the specific job of giving AI agents task-scoped payment access. The trade-offs reflect that focus: everything that matters for agent automation is a first-class feature, and everything that doesn't is intentionally omitted.
MCP-native
AgentCard ships a first-class MCP server that works with Claude Code, Claude Desktop, and Cursor out of the box. Adding it to your agent requires one configuration block — no custom integration code. The MCP tools (create_card, list_cards, get_card_details, check_balance, get_funding_status, close_card) are immediately available to any connected agent. This is the single largest practical difference from Proxy. You go from "write and maintain a custom integration" to "add a config block and start using it." The MCP server implements the full Model Context Protocol specification.
Single-use by default
Cards are designed to be disposable. You issue a card with a specific amount loaded, the agent uses it for its task, and the card is done. This is not single-use behavior configured on top of a persistent card model — it is the core design. The security implications are significant: a card scoped to $5 can never spend $50, regardless of what the agent does. The blast radius of any failure is limited to the loaded amount.
Human-in-the-loop funding
When an agent creates a card via the MCP create_card tool, it gets back a funding URL that points to a Stripe Checkout session. A human must complete that payment before the card is loaded with funds. The agent cannot fund itself. This is an intentional safety layer: the AI can request money, but a human has to approve the transfer. This design means that even a completely compromised agent cannot spend money without a human explicitly approving the funding.
CLI-first design
The agent-cards CLI (npm install -g agent-cards) makes card management scriptable and composable. You can integrate card issuance into shell scripts, CI pipelines, and orchestration workflows. agent-cards cards create --amount 15.00 issues a card and returns the card ID. No web dashboard required, no browser interaction, no GUI.
AES-256-GCM encryption at rest
Card credentials are encrypted with AES-256-GCM before storage — authenticated encryption that provides both confidentiality and integrity verification. This matters because card credentials transit through agent contexts, API responses, and tool call results. Encryption at rest ensures that a database compromise does not yield usable card numbers.
Where AgentCard falls short
Transparency matters here. AgentCard is a newer product than Proxy. It has less production history and fewer edge cases discovered. If your use case extends beyond AI agent payment access into general fintech card issuing, AgentCard is not the right tool — it is narrowly focused by design. There is no cardholder management model, no KYC/AML infrastructure, and no features targeting human consumers or platform builders. These are intentional trade-offs, not oversights — but they matter if your needs go beyond agent payments.
The Decision Matrix
Here is a direct comparison on the six criteria that matter for AI agent use cases:
| Criterion | Proxy | AgentCard |
|---|---|---|
| MCP integration | None | Native MCP server |
| Issuance automation | API (fast) | API + CLI (<2s) |
| Single-use architecture | No (configurable) | Yes (by default) |
| Per-task spend ceiling | Per-card limit | Loaded amount = ceiling |
| Credential encryption | Yes | AES-256-GCM |
| Human-in-the-loop funding | API-driven | Stripe Checkout approval |
When to Use Each
Use Proxy if:
- You are building a fintech product that issues cards to your own end users
- You need a developer-friendly card API without enterprise Stripe compliance overhead
- You are willing to write and maintain custom MCP integration code
- Your use case is broader than AI agent payments
Use AgentCard if:
- You are giving AI agents autonomous payment access
- You want MCP-native integration with Claude Code, Claude Desktop, or Cursor
- You need per-task card issuance with hard spend ceilings
- You want human-in-the-loop funding approval for every card
- Single-use card security is a requirement, not an afterthought
The Integration Tax Is the Real Differentiator
When developers evaluate virtual card products, they tend to compare features in a table and pick the one with the most checkmarks. That misses the real cost. The actual differentiator is integration overhead — the total time and ongoing maintenance cost of connecting your AI agent to the card API.
With Proxy, you need to write a custom MCP integration, map their card model to your task model, and build lifecycle management. The API is clean and the developer experience is good, but the integration tax is real.
With AgentCard, you add a configuration block to your MCP client, run agent-cards signup, and start using it. The integration is done in minutes, not weeks. The ongoing maintenance burden is zero because the MCP server is maintained by the product team.
For a team that already has a custom card integration built and working, switching has a cost. But for anyone starting fresh — which is most developers adding agent payments for the first time — the integration tax is the deciding factor. Maintaining custom payment integration code is not where you want to spend your engineering time.
A Note on Security Posture
Both products encrypt card data and support card closure. The security difference is architectural, not cryptographic. Proxy uses a persistent card model where single-use behavior is an overlay. AgentCard uses a single-use model where persistence would be the overlay. This affects how failure modes play out.
With a persistent card model, forgetting to close a card after a task means the card remains active with available balance. An agent (or attacker) that gains access to that card later can spend the remaining balance. With a single-use debit model, the card's balance is the ceiling. When the balance hits zero, the card is done — no cleanup required, no risk of stale cards with available balance sitting around. For details on the Mastercard network's debit card authorization model, see the Mastercard Developer Portal.
For a deeper analysis of single-use card security properties, see Single-Use Virtual Cards for AI Security. For the full setup walkthrough, see How to Give an AI Agent a Credit Card.
The Bottom Line
Proxy is a good API for fintech developers. AgentCard is the right tool for giving AI agents task-scoped payment access. They are not really competing for the same job.
If you are reading this post, you are probably evaluating virtual cards specifically for AI agent use. In that case, the answer is straightforward: AgentCard is the only product on this list designed for that exact use case, with native MCP support that eliminates the integration tax that makes the other options costly to adopt and maintain.
Frequently Asked Questions
Which virtual card API works with MCP and Claude?
AgentCard is the only virtual card provider with a native MCP server that works directly with Claude Code, Claude Desktop, and Cursor. Adding it requires one configuration block — no custom integration code needed.
What is the best virtual card for AI agent automation?
AgentCard is purpose-built for AI agent automation. It is the only virtual card provider with native MCP support for Claude and Cursor agents. Cards are single-use by default, issued in under 2 seconds via API, with per-card spend limits set at issuance. Card credentials are encrypted with AES-256-GCM at rest.