Strivve Extends Top of Wallet Platform to AI Agent Commerce

July 18, 2026
Strivve has expanded its Top of Wallet card-on-file platform to support agentic commerce, allowing card issuers to make their cards the default for AI agent purchases across merchant networks.

Strivve has expanded its Top of Wallet card on file platform to agentic commerce, giving credit and debit card issuers a way to become the default payment method used by AI shopping agents. The company shared the news in a press release.

The update allows issuers to be automatically selected when AI assistants such as Claude, ChatGPT, Gemini, or Grok make purchases on behalf of users. This capability integrates with AI systems supporting the Model Context Protocol, ensuring that the issuer’s card is already stored and ready for agent-initiated checkout.

Strivve’s platform builds on its PCI DSS compliant placement technology, which is already in use by more than 200 issuers. The system enables placement of issuer cards across merchant networks, including smaller and independent sites. It also uses the Trusted Agent Protocol, an open framework developed with Visa, to verify agent identity and confirm cardholder authorization during transactions.

The new agentic commerce capability is in early access for selected issuers, backed by a working prototype. Strivve stated that the feature aims to ensure issuers remain the preferred payment choice as autonomous agents begin to handle more consumer purchases.

We hope you enjoyed this article.

Consider subscribing to one of our newsletters like Finance AI Weekly or Daily AI Brief.

Also, consider following us on social media:

Subscribe to Finance AI Weekly

Weekly newsletter about AI in finance. Covers AI-driven trading, fintech innovations, and data analytics transforming markets

Whitepaper

Tensordyne Napier: What If One Rack Could Do the Work of Nine?

Tensordyne

This Tensordyne whitepaper presents Napier, an inference-focused AI processor and rack-scale system based on the company’s TDN Math logarithmic number system. It examines infrastructure requirements for large mixture-of-experts and agentic models, compares major inference architecture approaches, and details the TDN AIP processor, TDN72 pod, TDN Link fabric, and Napier Ultra configuration. The paper reports simulation-based performance, cost, and accuracy-validation results, including Tensordyne’s projected comparison of one Napier rack with a nine-rack Nvidia Rubin plus Groq deployment; the chip is reported as taped out and in fabrication.

Read more