Splitit: payments infrastructure for agentic commerce

Agentic AI will fundamentally reshape how consumers and businesses buy. Splitit's mission is to ensure that seamless, transparent installments are built into this new paradigm from day one, not bolted on later.

Ran Landau, CTO, Splitit

Designing for delegation: the three waves of agentic commerce

Splitit has positioned itself at the intersection of payments and AI-driven commerce, recognizing that the shift to autonomous shopping agents requires fundamental infrastructure changes, not just interface improvements.

The company's approach centers on three phases of evolution. First, agentic checkout remains narrow, with payments functioning as trust infrastructure. Second, orchestration emerges, where agents manage complexity and installments transition from checkout features to decision variables. Third, agents become shopping operating systems, evaluating purchases as monthly obligations with associated protections rather than single prices.

Splitit has embedded this framework into its platform development:

• Agentic Commerce Partner Program: Launched in Q4 2025, the program embeds card-linked installment capabilities directly into AI shopping agents. Registered agents can request real-time installment options within merchant checkout flows through existing payment rails without redirecting users or requiring new credit lines.
• Open standards alignment: Splitit's solution aligns with Google's Agent Payments Protocol (AP2) and OpenAI's Agentic Commerce Protocol, ensuring flexibility and interoperability across future agent ecosystems.
• Autonomous risk agents:Motive Create developed AI agents that detect early issues and automate chargeback management, alongside a shopper risk scoring engine to predict payment behavior. The company also launched autonomous agents that improve checkout performance and streamline engineering workflows.

Splitit's infrastructure treats payments as a control point rather than a terminal step, positioning installments as constraint solvers within agent-driven purchasing decisions.

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