In a column for Ghana’s MyJoyOnline, technology and artificial intelligence consultant Kwami Agyabeng described agentic commerce — an e-commerce model in which AI agents can search for products, compare offers, negotiate and make purchases on behalf of users or companies.
Under this model, an individual or company sets order parameters for the agent, including the type of product or service, budget, size and timing. The system then processes machine-readable data from different suppliers, selects options, obtains confirmation for a specific purchase and processes payment using secure tokens.
From search to payment
As the author notes, agentic commerce changes the usual online shopping process: instead of manually searching, comparing and placing an order, the user states their intent and AI handles the entire process. An example given is a request to find a business-class ticket costing up to $2,000.
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Agents can be used for recurring purchases and subscriptions, business-to-business procurement, inventory replenishment, automated price monitoring, travel and ticket bookings, as well as the purchase of digital services. They can account for price changes, product availability and user preferences, and interact with retailers through open APIs and commerce protocols.
Risks of delegating decisions
At the same time, Agyabeng draws attention to the “principal-agent” problem: the interests of the system to which a user delegates a purchase may not align with their own. An AI agent may be optimized for speed, price or commission rewards rather than for the full set of the customer’s preferences.
Among other challenges, the author names personal data protection, responsibility for an agent’s actions, compatibility of technical standards, fraud, manipulation and competition. According to IBM data cited in the column, 45% of consumers already use AI at certain stages of the purchasing journey. McKinsey forecasts that by 2030, AI agents may mediate between $3 trillion and $5 trillion in global consumer trade.
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