Over the past year, the technology sector has moved from the era of the “chatbot” to that of the “do-bot.” What it essentially means is, instead of merely
suggesting products, an agent can monitor inventory, compare prices, place orders, and manage recurring purchases based on a user’s preferences and approval settings. A consumer could simply instruct an agent to keep their pantry stocked within a set budget, while the system handles the search, selection, and purchase process.
The market is responding accordingly. According to Precedence Research, the global AI agents market is expected to grow from approximately USD 7.9 billion in 2025 to nearly USD 12 billion by the end of 2026. The appeal is straightforward: consumers increasingly want outcomes, not just recommendations.
The Protocols Enabling Agentic Commerce
To support this new model, technology companies are developing common standards that enable AI agents to connect with external tools, access product information, discover merchants, and complete transactions securely. These include Anthropic’s Model Context Protocol (MCP), which connects agents to external tools and data; the Agentic Commerce Protocol (ACP) from OpenAI and Stripe, which enables product discovery and transactions within AI interfaces; the Agent Payments Protocol (AP2) led by Google, which governs user consent and payment authorisation; and the Universal Commerce Protocol (UCP) from Google and Shopify, which seeks to standardise the entire shopping journey from discovery to post-purchase support.
For businesses, the implications are significant. Products and services must be structured in ways that AI systems can easily understand and evaluate. In an increasingly agent-driven marketplace, visibility will depend not only on consumer awareness but also on machine readability.
What This Means for Brands
This presents a new challenge for brands. While traditional marketing relies on packaging, advertising, and emotional appeal to influence consumers, AI agents are more likely to evaluate structured product data, specifications, pricing, reviews, and trust signals. In an agent-driven marketplace, a brand that is not machine-readable may simply not appear in the decision-making process. Structured data, well-organised websites, and interoperable product information will therefore become critical for ensuring that AI systems can accurately discover, understand, and evaluate a brand’s offerings.
Beyond discoverability, brands will also need to strengthen credibility through authentic reviews, transparent product information, and consistently positive customer experiences. Success will depend not only on being visible to AI agents but also on being selected by them. In the agent era, winning algorithmic preference may become just as important as winning consumer preference.
Conclusion
The brands that thrive in the agent era are unlikely to be those with the largest advertising budgets. They will be the ones that make it easy for an algorithm to choose them, while remaining compelling enough for a human to instruct their agent to remember.
This article is authored by Dr. Aditya Nittur, Chief Technology Officer and Co-founder, FlowBlinq
Disclaimer: This article has been authored by a guest contributor. The views, opinions and information expressed in this article are those of the author and do not necessarily reflect the views of the publication or its editorial team.
