“The transition from traditional web or app search to agent-led commerce represents the next great evolution in retail. We aren’t just watching the shift, we are driving it.” – John Furner – President, CEO Walmart US
When John Furner speaks about the shift from traditional web or app search to agent-led commerce, he is putting words to a structural change that has been building at the intersection of artificial intelligence, retail strategy and consumer behaviour for more than two decades. His quote does not describe a marginal optimisation of online shopping; it points to a reconfiguration of how demand is discovered, shaped and fulfilled in the digital economy.
John Furner: An operator at the centre of AI-led retail
John Furner built his leadership reputation inside one of the most operationally demanding businesses in the world. Before being named President and CEO of Walmart U.S., and then incoming President and CEO of Walmart Inc., he held a series of roles that grounded him in the realities of store operations, merchandising and labour-intensive retail at scale.1,4 That background matters to the way he talks about AI.
Unlike many technology narratives that begin in the lab, Walmart’s AI story has been forged in distribution centres, supercentres and neighbourhood markets. Under Doug McMillon, and increasingly under Furner, Walmart framed AI not as a side project but as a new backbone for the business.1 Analysts note that as Furner steps into the global CEO role, the board describes the next chapter as one “fueled by innovation and AI”.1 His quote about agent-led commerce sits squarely in that strategic context.
Furner has consistently emphasised pragmatic, measurable outcomes from technology adoption: better inventory accuracy, improved shelf availability, faster fulfilment and fewer customer headaches.1,4 He has also been explicit that every job in the company will change in some way under AI – from collecting trolleys in car parks to technology development and leadership roles.4 In other words, for Furner, agent-led commerce is not simply a new consumer interface; it is a catalyst for rethinking work, operations and value creation across the retail stack.
The specific context of the quote: Walmart, Google and Gemini
The quote originates in the announcement of a partnership between Walmart and Google to bring Walmart and Sam’s Club product discovery directly into Google’s Gemini AI environment.2,3,5 Rather than treating AI search as an external channel to be optimised, the collaboration embeds Walmart’s assortment, pricing and fulfilment options into an intelligent agent that can converse with customers inside Gemini.
In this setting, Furner’s words perform several functions:
- They frame the shift from keyword-driven search (type an item, browse lists) to goal- or task-based interaction (“help me plan a camping trip”), where an agent orchestrates the entire shopping journey.2,3
- They signal that Walmart is not content to be a passive catalogue inside someone else’s interface, but intends to shape the emerging standards for “agentic commerce” – an approach where software agents work on behalf of customers to plan, select and purchase.2,3,4
- They reassure investors and partners that the company sees AI as a core strategic layer, not as an optional experiment or promotional gimmick.1,4,6
The Walmart – Google experience is designed to allow a shopper to ask broad, life-context questions – for example, how to prepare for spring camping – and receive curated product bundles drawn from Walmart and Sam’s Club inventory, updated dynamically as the conversation unfolds.2,3 The system does not simply return search results; it proposes solutions and refines them interactively. The agent becomes a kind of digital retail concierge.
Technically, this is underpinned by the pairing of Gemini’s foundation models with Walmart’s internal data on assortment, pricing, local availability and fulfilment options.3 Strategically, it positions Walmart to participate in – and influence – the universal protocols that might govern how agents transact across merchants, platforms and services in the coming decade.
From web search to agent-led commerce: why this is a step-change
To understand why Furner describes this as “the next great evolution in retail”, it is useful to place agent-led commerce in a longer history of digital retail evolution.
1. Catalogue search and the era of the query box
The first wave of e-commerce was built around catalogue search: customers navigated static product hierarchies or typed keywords into a search box. Relevance was determined by text matching and basic filters. Power resided in whoever controlled the dominant search interface or marketplace.
This model mapped well onto traditional retail metaphors – aisles, departments, categories – and it assumed that the customer knew roughly what they were looking for. Retailers competed on breadth of assortment, price transparency, delivery speed and user interface design.
2. Personalisation and recommendation
The second wave saw retailers deploy recommendation engines, collaborative filtering and behavioural targeting to personalise product suggestions. Here, algorithmic theories drawn from machine learning and statistics began to shape retail experiences, but the core unit remained the search query or product page.
Recommendations were adaptively presented around known products and purchase history, nudging customers to complementary or higher-margin items. Many of the leading ideas came from research in recommender systems, one of the most commercially influential branches of applied machine learning.
3. Conversational interfaces and agentic commerce
Agent-led commerce represents a third wave. Instead of asking customers to break down their needs into discrete product searches, it allows them to:
- Express goals (“host a birthday party for ten-year-olds”), constraints (“under £100, dietary restrictions, limited time”) and context (“small flat, no oven”).
- Delegate the planning and selection process to an AI agent that operates across categories, channels and services.
- Iterate interactively, with the agent updating recommendations and baskets as the conversation evolves.
In this model, the agent becomes a co-pilot for both discovery and decision-making. It can optimise not only for price and relevance, but also for timing, delivery logistics, dietary requirements, compatibility across items and even sustainability preferences, depending on the data and constraints it is given. The underlying technologies draw on advances in large language models, planning algorithms and multi-agent coordination.
For retailers, the shift is profound:
- It moves the locus of competition from web page design and keyword bidding to who supplies the most capable and trustworthy agents.
- It elevates operational capabilities – inventory accuracy, fulfilment reliability, returns processing – because an agent that cannot deliver on its promises will quickly lose trust.
- It opens the door to autonomous or semi-autonomous shopping flows, such as automatic replenishment, anticipatory shipping or continuous cart management, where the agent monitors needs and executes under defined guardrails.
Furner’s assertion that Walmart is “driving” the shift needs to be understood against this backdrop. Internally, Walmart has already invested in a family of “super agents” for shoppers, associates, partners and developers, including Sparky (customer assistant), My Assistant (associate productivity), Marty (partner and advertising support) and WIBEY (developer tooling).1,4 Externally, initiatives like integrating with ChatGPT for “instant checkout” and partnering with Google on Gemini experiences demonstrate a strategy of meeting customers inside the agents they already use.1,3,4
Agent-led commerce inside Walmart: from vision to practice
Agent-led commerce is not just a phrase in a press release for Walmart. The company has been progressively building the capabilities required to make it a practical reality.
AI-native shopping journeys
Walmart has rolled out AI-powered search experiences that allow customers to describe occasions or problems rather than individual items – for example, planning a party or organising a kitchen.1 The system then infers needs across multiple categories and pre-populates baskets or recommendations accordingly.
At the same time, the company has been piloting “replenishment” features that create suggested baskets based on past purchases, letting customers approve, modify or decline the auto-generated order.1 This is an early expression of agentic behaviour: the system anticipates needs and does the heavy lifting of basket formation.
Super agents as an organisational pattern
Internally, Walmart has articulated a vision of multiple domain-specific “super agents” that share core capabilities but specialise in particular user groups.1,4
- Sparky supports customers, operating as a front-end conversational assistant for shopping journeys.
- My Assistant helps associates draft documents, summarise information and interact with data, freeing them from repetitive tasks.1,4
- Marty works with partners and increasingly underpins the advertising business, helping brands navigate Walmart’s ecosystem.4
- WIBEY accelerates developer productivity, contributing to the internal fabric of AI tooling.4
Additionally, Walmart has built a generative AI assistant called Wally for merchandising tasks, using AI to support complex assortment, pricing and space decisions.4
Operational AI as the foundation
Critically, Walmart has recognised that agent-led commerce cannot function if the operational substrate is weak. AI agents that promise two-hour delivery on items that are out of stock will immediately erode trust. As a result, the company has deployed AI and automation deep into its supply chain and fulfilment network.1,4
This includes large-scale investment in warehouse automation (for example, through partnerships with Symbotic), sensor-based tracking to improve inventory accuracy, and forecasting models that help move products closer to expected demand.1 The philosophy is that data quality is strategy: without reliable, granular data about where products are and how they move, agentic experiences will fail at the last mile.
The intellectual backstory: the theorists behind agents, recommendations and AI commerce
While Walmart and Google are prominent practitioners, the transition Furner describes rests on decades of work by researchers and theorists in several overlapping fields: information retrieval, recommender systems, artificial intelligence agents, behavioural economics and commerce design. A brief backstory of these fields helps illuminate what is now converging under the label “agent-led commerce”.
Information retrieval and the search paradigm
The idea of representing information needs through queries and ranking results based on relevance traces back to mid-20th century information retrieval research. Early work by scholars such as Gerard Salton introduced the vector space model of documents and queries, which underpinned term-weighting schemes like tf-idf (term frequency – inverse document frequency). These ideas influenced both academic search engines and, eventually, commercial web search.
As web content exploded, researchers in IR refined ranking algorithms, indexing structures and relevance feedback mechanisms. The prevailing paradigm assumed that users could express needs in terms of keywords or structured queries, and that the system’s job was to approximate relevance as accurately as possible given those inputs.
Agent-led commerce departs from this model by treating language not as a set of keywords but as an interface for describing goals, constraints and preferences in natural form. Instead of mapping queries to documents, agents must map intentions to actions and sequences of actions – choose, bundle, schedule, pay, deliver.
Recommender systems and personalisation pioneers
The science of recommending products, films or content to users based on their behaviour has roots in the 1990s and early 2000s. Key theorists and practitioners include:
- John Riedl and colleagues, whose work on collaborative filtering and the GroupLens project showed how crowd data could be used to predict individual preferences.
- Yehuda Koren, whose contributions to matrix factorisation methods during the Netflix Prize competition demonstrated the power of latent factor models in recommendation.
- Joseph Konstan and others who explored user experience and trust in recommender systems, highlighting that perceived transparency and control can be as important as accuracy.
These researchers established that it is possible – and commercially powerful – to infer what customers might want, even before they search. Their theories informed the design of recommendation engines across retail, streaming and social platforms.
Agent-led commerce builds on this tradition but extends it. Instead of recommending within a narrow context (“people who bought this also bought”), agents must manage multi-step goals, cross-category constraints and time-sensitive logistics. This requires integrating recommender logic with planning algorithms and conversational interfaces.
Software agents and multi-agent systems
The concept of a software agent – an autonomous entity that perceives its environment, makes decisions and acts on a user’s behalf – has deep roots in AI research. Theorists in this area include:
- Michael Wooldridge, whose work on multi-agent systems formalised how agents can reason, cooperate and compete in complex environments.
- Nick Jennings, who explored practical applications of autonomous agents in business, including negotiation, resource allocation and supply chain management.
- Stuart Russell and Peter Norvig, whose widely adopted AI textbook set out the rational agent framework, defining intelligent behaviour as actions that maximise expected utility given beliefs about the world.
In this tradition, agents are not simply chat interfaces; they are decision-making entities with objectives, models of the environment and policies for action. Many of the recent ideas around “agentic” systems – where software components can autonomously plan, call tools, execute workflows and coordinate with other agents – derive conceptually from this line of research.
In retail, agentic commerce can be seen as a large-scale deployment of these ideas: shopper-facing agents negotiate between customer preferences, product availability, pricing, promotions and logistics, while back-end agents manage inventory, routing and labour scheduling.
Conversational AI and natural language understanding
The move from query-driven search to conversational agents has been enabled by advances in natural language processing (NLP), particularly large language models (LLMs). Theorists and practitioners in this domain include researchers who developed transformer architectures, attention mechanisms and large-scale pre-training techniques.
These models provide the linguistic and semantic fluency required for agents to engage in open-ended dialogue. However, in commerce they must be grounded in reliable data and constrained by business rules. Walmart’s AI strategy, for example, combines general-purpose language models with retail-specific systems like Wallaby, which is tuned to Walmart’s own data on catalogues, substitutions and seasonality.1
Behavioural economics and choice architecture
The design of agent-led experiences also draws on insights from behavioural economics and psychology. Researchers such as Daniel Kahneman, Amos Tversky, Richard Thaler and Cass Sunstein have shown how framing, defaults and choice architecture influence decisions.
In an agentic commerce environment, the agent effectively becomes the architect of the customer’s choice set. It decides which alternatives to present, how to explain trade-offs and what defaults to propose. The ethical and strategic implications are significant: the same technologies that can reduce friction and cognitive load can also be used to steer behaviour in subtle ways.
Leading thinkers in digital ethics and AI governance have therefore argued for transparency, contestability and human oversight in agentic systems. For retailers, this becomes a trust question: customers need to believe that the agent is working in their interests, not solely maximising short-term conversion or margin.
Google, Gemini and open standards for agentic commerce
On the technology platform side, Google has been a central theorist and practitioner in both search and AI. With Gemini, its family of multimodal models, Google is positioning AI not just as a backend enhancement to search results but as a front-end conversational partner.
In the joint Walmart – Google initiative, the companies highlight a “Universal Commerce Protocol” designed to let agents interact with merchants in a standardised way.3 While technical details continue to evolve, the ambition reflects a broader movement towards open or semi-open standards for how agents discover, price, bundle and purchase across multiple commerce ecosystems.
Sundar Pichai, Google’s CEO, has spoken of AI improving every step of the consumer journey, from discovery to delivery, and has explicitly framed the Walmart partnership as a step toward making “agentic commerce” a reality.3 This aligns with the longer arc of Google’s evolution from ten blue links to rich results, shopping tabs and now conversational, transaction-capable agents.
Strategic implications: trust, control and the future of retail interfaces
Furner’s quote hints at the strategic contest that agent-led commerce will intensify. Key questions include:
- Who owns the interface? If customers increasingly begin journeys inside a small number of dominant agents (Gemini, ChatGPT, other assistants), traditional notions of direct traffic, branded apps and search engine optimisation will be reconfigured.
- Who sets the rules? Universal protocols for agentic commerce could distribute power more widely, but the entities that define and maintain those protocols will have disproportionate influence.
- How is trust earned and maintained? Mistakes in retail – wrong products, failed deliveries, billing errors – have tangible consequences. Agent-led systems must combine probabilistic AI outputs with robust guardrails, validation checks and escalation paths to humans.
- How does work change? As McMillon has noted, and Furner will now operationalise, AI will touch every job in the organisation.4 Theorists of work and automation have long debated the balance between augmentation and substitution; agentic commerce will be one of the most visible test cases of those theories in practice.
Walmart’s own AI roadmap suggests a disciplined approach: build AI into the fabric of operations, prioritise store-first use cases, move carefully from assistants to agents with strict guardrails and develop platforms that can be standardised and scaled globally.1 Furner’s quote can thus be read as both a declaration of intent and a statement of competitive philosophy: in a world where AI agents mediate more and more of daily life, retailers must choose whether to be controlled by those agents or to help design them.
For customers, the promise is compelling: less time on search and comparison, more time on what the purchases enable in their lives. For retailers and technologists, the challenge is to build agents that are not only powerful and convenient but also aligned, transparent and worthy of long-term trust. That is the deeper context behind Furner’s assertion that the move from web and app search to agent-led commerce is not just another technology upgrade, but the “next great evolution in retail”.
References
2. https://pulse2.com/walmart-and-google-turn-ai-discovery-into-effortless-shopping-experiences/
4. https://www.digitalcommerce360.com/2026/01/08/how-walmart-is-using-ai/
6. https://www.emarketer.com/content/walmart-tech-first-strategy-shapes-growth
7. https://www.futurecommerce.com/podcasts/predictions-2026-prepare-for-the-age-of-autonomy
