How To Prepare For The ‘Agent-to-Agent’ Era Of eCommerce

We’re seeing a seismic shift in consumer behavior where support is no longer a back-office function but a real-time social interaction. Customers expect instant responses, personalized experiences, and seamless support across multiple channels. Setting the standard for these expectations is the rapid rise of direct commerce. Its fast-paced environment is shifting the standard originally set by traditional marketplaces. To attract and retain customers against the competition, sellers must understand that simple participation is not enough, the solution is smart execution.

This presents a significant challenge for the retail industry, where high-volume stores must meet these customer expectations without burning out their teams. While artificial intelligence has been used throughout the industry for several years now to assist customer support teams, this has most commonly been “bolted-on” single-channel systems, i.e., rudimentary chatbots and automated email trigger systems. It’s not enough to retain modern customers. AI-powered personalization is key and has been proven to enhance customer satisfaction by up to



20%



.

The most advanced personalization entering the market is through autonomous AI agents, and with them, semi-autonomous workers who, in addition to answering product queries, can assist in fulfilling them as they begin to learn brand logic in real-time. Crucially for human agents, this strategic expansion will be key to maintaining the value of people.

Traditional Point-And-Click Workflows VS. Conversational Interfaces

The global e-commerce market is huge and changing rapidly. For instance, sales are expected to reach



$6.3 trillion



this year, and over half (



59%



) will be through mobile commerce as customers increasingly reach out via Amazon Buyer-Seller Messaging, eBay, Shopify, email, Instagram DMs, Facebook Messenger, WhatsApp, live chat, and more. By 2028, this percentage is expected to rise to 63%.

As a result, customer support needs to work effectively across all these channels, and traditional point-and-click workflows cannot accommodate this. Rigid, predetermined paths, a lack of real-time order integration, and poor scalability for complex queries all create these challenges. Most critically, these workflows often fail to bridge the gap between the different sales channels.

Fortunately, conversational AI can address these challenges and improve the customer experience. The technology includes chatbots and agent assistants that enable businesses to engage with customers in real-time, provide personalized recommendations, handle inquiries, and facilitate transactions. This can include brand voice consistency, whereby every response is programmed to match the company’s tone, terminology, and policies. Conversational AI can not only be trained to meet specific brand standards but also help ensure customers receive consistent communication on any channels they use. This consistency is crucial to building customer trust.

As a result, conversational interfaces are increasingly replacing traditional workflows, and sellers have already begun to reap the benefits. Walmart’s CEO revealed in their latest earnings call that customers using its AI-powered shopping assistant ‘Sparky’ are making around



35%



larger orders than those who do not. Supporting this, our most recent data across 300 marketplaces, webstores, and social channels shows that businesses using AI capabilities can resolve up to 73% more customer inquiries without adding headcount. The more satisfied a customer is, the more likely they are to make purchases. Similar to Walmart, we’ve found that AI chatbots are associated with approximately 4x higher conversion rates than without AI.

AI-driven customer support is proving to be revelatory for engagement by offering seamless unification between services and sales and round-the-clock interactions.

The Consumer Agent Meets The Brand Agent

The growth of conversational AI not only covers human-to-agent interactions – where customers utilize search through systems like ChatGPT built directly into major retail and marketplace platforms – but this year, a meaningful share of customer interactions will happen agent-to-agent. As a result, the next transformative phase of both the technology and the e-commerce industry

There have been varying predictions about how AI agents will evolve in the near future. With the latest developments, we have already surpassed agents limited to reactive automation. In early 2026, most businesses and individuals are starting to grow familiar with the ‘digital intern’ agent, which functions like an employee rather than a simple assistant. But another shift is on its way, human-to-agent interactions becoming agent-to-agent. For e-commerce, this is where a customer’s own machine interacts directly with a brand’s machine.

The technology to enable this is already hitting the market. For example, both



OpenAI



and



Google



have launched their own agentic commerce tools. The technology, while still in its infancy and relatively experimental phase, goes beyond the capabilities of a generic helpdesk – limited to visual data and chats – by having the unprecedented ability to link crucial order details directly to customer support questions. It’s the “digital handshake” between consumers and brands, enabling AI agents to act as delegated shoppers. In other words, while the AI acts on and resolves issues, the human customer can sit back and await the outcomes from super-fast machine-to-machine exchanges.

Thus, the real shift happening is speed. Conversations that used to take minutes are gradually collapsing into a single, sub-second, automated exchange. For e-commerce, this will separate retailers with unified data from those still stitched together with fragmented systems, i.e., disconnected consumer-facing platforms. The first group will meet machine customers effortlessly, but the second won’t be able to participate.

The Rise Of ‘Resolution-First’ Models

The point-and-click model has dominated e-commerce for over two decades, but to keep up with modern consumer demands, this model is fatally flawed. In response, huge changes in e-commerce operations are underway. We’re now in a transition phase towards resolution-first models. Models that allow AI to check stock, confirm delivery times, and verify returns. These models also enable businesses to engage with inter-agent communication.

The customer is no longer just a human with a mouse but increasingly an AI agent. These machine customers aren’t browsing websites; they’re exchanging data. To remain visible to these shoppers, brands will need to have their own AI agents that can read order data and act instantly.

The key message here is that e-commerce teams must remain on top of innovation. More than ever, customer expectations are being shaped by AI’s capabilities: speed, personalisation, and 24/7 service. Human teams alone cannot meet these high-stakes ‘always-on’ demands. Autonomous workers and autonomous shoppers present a new digital economy, leaving behind the manual frictions that once defined the retail experience.