Agentic commerce operates on two parallel tracks, each delivering transformative benefits. Consumer-facing agents automate shopping tasks on behalf of individual shoppers, while retailer-facing agents help commerce teams work smarter by automating operations, surfacing insights, and optimizing experiences. Together, they’re reshaping the entire e-commerce ecosystem.
Consumer-Facing Agents: Shopping Assistants That Act Autonomously
Dramatic time savings represent the most immediate benefit for shoppers. Tasks that previously required 20-30 minutes of browsing, comparing, and evaluating can be completed in seconds. A consumer agent can simultaneously check inventory across dozens of retailers, compare specifications, read hundreds of reviews, calculate total costs including shipping and taxes, and identify the optimal purchase-all without manual input.
Hyper-personalized cross-retailer shopping becomes possible when agents maintain persistent memory of individual preferences, purchase history, size requirements, budget constraints, and brand affinities. Unlike traditional personalization engines confined to a single retailer’s ecosystem, consumer agents can compare options across the entire market based on true personal fit rather than what’s in stock at one store.
Reduced decision fatigue addresses overwhelming choice. Instead of presenting thousands of options, AI agents act as intelligent filters, curating selections based on nuanced understanding of individual needs and context. This cognitive offloading transforms shopping from exhausting to effortless.
Proactive fulfillment eliminates the mental burden of remembering to reorder essentials. Agents monitor consumption patterns, anticipate depletion, compare current prices against historical data, and autonomously reorder at optimal times-ensuring consumers never run out while maximizing value.
Retailer-Facing Agents: Operations and Experience Optimization
While consumer agents change how people shop, retailer-facing agents transform how commerce teams operate-reducing manual work, accelerating execution, and improving customer experiences at scale.
Automated workflow execution liberates teams from repetitive tasks. Instead of manually creating merchandising rules, analyzing performance reports, or configuring A/B tests, retailers can delegate these tasks to AI agents that suggest and execute actions under human supervision. This shifts teams from tactical execution to strategic leadership.
Proactive intelligence and insights emerge continuously as agents analyze commerce data to surface growth opportunities brands might miss: high-value customer segments, optimized product bundles, underperforming categories, search terms that need attention, and conversion bottlenecks. Rather than waiting for humans to ask the right questions, agents identify opportunities autonomously.
The Agent Orchestrator: Unifying Intelligence
Enhanced personalization at scale becomes achievable when a central AI agent orchestrates a network of specialized agents to handle complex personalization tasks. This network can dynamically adjust merchandising rules based on real-time performance, personalize search results using deep behavioral understanding, and optimize recommendation strategies for individual shoppers-all simultaneously across thousands of interactions, ensuring that strategy is executed with superhuman consistency.
Faster time-to-value on strategic initiatives occurs when agents handle technical implementation. Commerce teams can describe what they want in natural language-“optimize our winter collection merchandising” or “identify our most valuable customer segments”-and agents translate those goals into executed actions, eliminating the traditional bottleneck between strategy and implementation.
Reduced dependency on technical resources empowers merchandisers, marketers, and analysts to accomplish tasks that previously required developer support. Agents can create templates, configure integrations, generate analytics reports, and optimize technical configurations through conversational interfaces, democratizing capabilities across the organization.
The Convergence: Better Experiences Drive Better Business Outcomes
The Convergence: Intelligence as a Unified Backbone
The true power of agentic commerce emerges when both types of agents are powered by a unified, proprietary commerce intelligence model. This model acts as a single source of truth, trained on billions of behavioral and transactional data points. Retailer-facing agents then optimize inventory and merchandising based on this intelligence, while consumer-facing agents efficiently navigate these optimized experiences to find the best matches for individual shoppers, ensuring relevance across every touchpoint.
Agent-ready infrastructure as competitive advantage: Retailers who prepare for both agent types will win. This means structuring product data for agent consumption, optimizing APIs for agent queries, building trust signals that agents recognize, and ensuring your commerce systems can respond in real-time to agent requests. Early adopters gain disproportionate visibility in agent-mediated discovery.
Enhanced operational efficiency meets higher conversion: When internal agents handle routine optimization and external agents eliminate shopping friction, retailers achieve simultaneous improvements in team productivity and customer conversion rates-creating compounding advantages over competitors using traditional approaches.
Access to new customer segments: Agentic commerce opens markets that traditional e-commerce struggles to serve: busy professionals who lack time for browsing, elderly consumers who find digital interfaces challenging, accessibility-focused users who benefit from conversational shopping, and sophisticated shoppers who want AI to handle research while they focus on decisions that matter.
Reduced platform dependency: As AI agents mediate both operations and discovery, retailers become less vulnerable to changes in search engine algorithms, social media feed prioritization, or marketplace ranking systems. Agent-compatible businesses can optimize experiences internally and reach consumers directly through conversational AI.
Strategic Implications: What Retailers Must Do Now
The shift to agentic commerce is accelerating rapidly. Traffic to U.S. retail sites from GenAI browsers and chat services increased 4,700% year-over-year in July 2025, and by 2030, U.S. B2C retail could see up to $1 trillion in orchestrated revenue from agentic commerce. The potential impact rivals that of the web and mobile revolutions, and the transition could unfold even faster this time.
The message is clear: retailers must act now or risk being sidelined in an agent-mediated marketplace.
The Disintermediation Threat
As AI agents take on more of the discovery process, retailers face a significant threat of disintermediation. The familiar model of driving traffic to a website through organic and paid search is becoming less effective. Gartner forecasts a 25% reduction in search volumes by 2026 due to AI-driven discovery.
When consumers delegate shopping to AI agents, the traditional customer journey disappears. Discovery and decision-making can now happen before, and even instead of, a visit to your website. Retailers who fail to optimize for agent discoverability risk becoming invisible-their products never surfacing in agent recommendations, their brands never entering consideration.
The New Playbook: Generative Engine Optimization (GEO)
To stay relevant, brands must show up where discovery begins-on AI platforms. Just as SEO helped brands gain visibility on search engines, generative engine optimization, or GEO, is becoming essential.
GEO requires fundamentally rethinking how product information is structured and presented:
- Structured, agent-readable product data: Making sure your product content is structured in a way that AI agents can easily understand and use becomes non-negotiable. This means rich, detailed attributes, comprehensive specifications, and semantic markup that agents can parse efficiently.
- Enhanced content beyond basic descriptions: Go beyond simple descriptions. Provide detailed attributes, how-to guides and rich media. Agents prioritize products with comprehensive information that enables confident recommendations.
- Review optimization: AI agents are highly sensitive to product reviews. Positive reviews can significantly increase the chances of your product being recommended, even at a higher price point. Actively managing review quality and volume becomes a critical competitive factor.
- Operational Excellence and AI Transparency as Ranking Signals: Operational performance (timely shipping, low returns) remains critical, as AI agents factor this into recommendations. Crucially, the system must provide merchandisers with transparency into the AI’s decision-making process, showing exactly how factors like performance rules, keyword matches, and relevancy scoring influence product visibility. This human oversight ensures AI-driven strategy aligns with business goals.
Build or Partner: The Agent Strategy Decision
Retailers face a fundamental strategic choice: optimize for third-party agents, build proprietary agents, or pursue both approaches simultaneously.
Optimizing for third-party agents ensures visibility in the dominant discovery channels-large consumer platforms, and emerging platforms. This requires technical readiness: APIs that agents can query, product catalogs structured for agent consumption, and real-time inventory and pricing data.
Building proprietary agents enables retailers to control the customer experience and capture first-party data. Retailers can explore building their own AI tools, so that they own the experience and can direct users accordingly rather than solely fitting into the playbook of the largest LLMs. Proprietary agents can embody brand values, prioritize margin-favorable products, and create differentiated experiences that third-party agents cannot replicate.
Hybrid strategies will likely dominate. Smart retailers will ensure their products surface effectively in third-party agent recommendations while simultaneously deploying owned agents for high-value customer segments and complex shopping journeys.
Rethinking Business Models and Monetization
For many companies, this will require not only new technology but new business models. Marketplaces, loyalty platforms, and intermediaries that thrive on human-driven engagement may face existential questions: Should they build their own agents? Should they welcome or block agent-driven traffic? And how will they monetize when algorithms-not people-make the purchasing decisions?
Revenue models built on display advertising, search placement, and human browsing behavior must evolve. Agent-mediated commerce may shift monetization toward:
- Commission-based models where agents take a share of transactions they facilitate
- Subscription services offering priority placement in agent recommendations
- Data licensing fees for access to inventory and pricing information
- Premium services where brands pay for enhanced visibility to specific customer segments
First-movers have the unique opportunity to set and define these pricing models, capitalizing on the current monetization gaps where agents are often free and providers have yet to develop a clear revenue strategy.
Infrastructure and Technical Readiness
Agentic commerce demands modern technical infrastructure. Retailers must invest in:
- Real-time APIs that agents can query efficiently for inventory, pricing, product details, and transaction capabilities. Legacy systems with batch updates and slow response times will be filtered out by performance-conscious agents.
- Integration standards and protocols enable seamless agent interactions. Adopting emerging standards ensures compatibility with the agent ecosystem.
- Trust and security infrastructure becomes critical as autonomous systems make purchasing decisions. As AI systems act with greater autonomy, issues of trust, transparency, and control grow more urgent. Businesses will need new mechanisms to verify that agents act legitimately on behalf of authorized users, and to ensure accountability when autonomous systems make mistakes.
The Urgency of Action
McKinsey argues that early movers will have a decisive advantage. Staying competitive will require more than incremental change. Without intervention, retailers risk being reduced to background utilities in agent-controlled marketplaces.
This moment calls for decisive action-before AI agents start making the decisions without you. Retailers who wait for agentic commerce to fully mature before responding will find themselves competing from a position of structural disadvantage, struggling to gain visibility in agent algorithms optimized around early adopters.
Conclusion: The Agentic Commerce Imperative
Agentic commerce represents more than a technological evolution-it’s a fundamental restructuring of how value flows through the retail ecosystem. “We are entering a commerce 3.0 experience. The fact that now a shopper can directly interact with an agent that can be an AI platform, or can be an owned agent on your channels … this is a major breakthrough”.
For retailers, the dual nature of agentic commerce creates both opportunities and obligations. On one side, internal AI agents promise to multiply team effectiveness, surface hidden opportunities, and optimize operations with superhuman consistency. On the other, consumer-facing agents are reshaping the discovery and purchase journey, potentially disintermediating brands that fail to optimize for agent-mediated transactions.
The winners in this transformation will be retailers who recognize that agentic commerce is not another channel to manage, but a fundamental shift toward high-context, conversational discovery. Success requires simultaneous investments in operational agents that enhance internal capabilities and strategic positioning that ensures visibility to external shopping agents.
The timeline for this transformation is compressed. Because these AI systems operate along the same digital pathways as human users, they can “ride the rails” of existing commerce infrastructure rather than waiting for new ones to be built. This means adoption can accelerate far faster than previous digital transformations required.
The strategic imperative is clear: retailers must become agent-ready across every dimension of their business-product data, technical infrastructure, operational excellence, and customer experience. Those who move decisively will shape the standards and economics of agent-mediated commerce. Those who hesitate will find themselves competing for scraps of attention in a marketplace where AI agents control access to consumers.
In the early days of e-commerce, many who lagged found themselves left behind or even out of business. Now, as then, companies need to figure out how to adapt to this emerging new reality-even if it means rethinking their existing business models-or risk a similar fate.
The age of agentic commerce has arrived. The question is no longer whether to prepare, but whether you’ll lead the transformation or be left behind by it.
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