State of Pay: Agentic Commerce, Payments, and the Edge
Blog post from Fastly
AI agents are increasingly taking on roles traditionally handled by humans in online commerce, such as searching, comparing, recommending, negotiating, and completing transactions, which introduces a new dimension to ecommerce termed "agentic commerce." This shift necessitates a reevaluation of web protocols to accommodate AI-driven transactions, requiring standardized methods for expressing intent, identity, authorization, and payment. Unlike machine-to-machine payments, which involve straightforward transactions between systems, agentic commerce encompasses a broader scope, including understanding user preferences and managing the entire shopping journey. The evolution of agentic commerce could transform ecommerce by shifting the shopping process to automated layers, reducing direct consumer interaction with merchant sites and challenging traditional methods of building brand loyalty. Edge computing is positioned as a critical enforcement point to manage agent-driven transactions by verifying identity, authorization, and payment, ensuring compliance, and preventing fraud before requests reach the origin. Several protocols are emerging to support this ecosystem, such as the Universal Commerce Protocol (UCP) and Agentic Commerce Protocol (ACP), which aim to streamline AI interactions with commerce systems. Payment and authorization protocols like Visa's TAP and Mastercard's Agent Pay are also being developed to ensure secure and authenticated transactions. As the agentic commerce ecosystem continues to take shape, edge platforms like Fastly are adapting to provide the necessary infrastructure to support these new traffic patterns, enabling merchants to maintain control and deliver seamless customer experiences.
| Trend | Post Mentions | Total Month Mentions | Posts | Companies | MoM |
|---|---|---|---|---|---|
| AI Agents | 12 | 744 | 142 | 68 | -87% |
| MCP | 3 | 726 | 75 | 54 | -89% |
| Observability | 1 | 154 | 55 | 44 | -96% |
| Real-time | 1 | 568 | 168 | 74 | -91% |
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