How Agentic Commerce Is Transforming Retail and Beyond

Picture this: You’re a Hollywood star with an assistant who caters to every wish and whim. Need to prepare for a new movie? Your assistant shortlists, recommends and books personal trainers and specialists for the skills you need to develop. Want to organize a trip across Asia for a couple of months? Your assistant organizes the itinerary, books every flight and hotel, engages local guides for exclusive tours and even reserves tables at the restaurants of your preference. 

With the right requirements and budget, this concierge-of-sorts can research, select and shop on behalf of a shopper, who doesn’t have to waste time with mundane tasks. 

Agentic commerce has emerged to make this reality possible for anyone, albeit in the digital world. AI agents can help automate complex tasks that, if done manually, would require a ton of time. From putting together an office-style outfit to organizing a trip for a destination wedding, AI agents can navigate the web, select the best options according to specific requirements and budget, builds a bundle, applies the best promotions, completes checkout (and even starts a subscription, if needed) and triggers follow-ups — all the while learning from the outcome, so it can perform even better next time.

Transcending individual retailer apps, these cross-platform agents are making waves in retail, as 44% of users who have tried AI-powered search say that it has become their “primary and preferred” source for internet searching. Moreover, 60% of shoppers expect to use AI agents within the next 12 months and 73% are familiar with AI tools. 

However, agentic commerce isn’t limited to being discoverable and shoppable in AI platforms. In fact, retailers can (and should) leverage agentic AI for owned experiences as well. According to Forrester, “guided selling experiences, augmented by generative AI, are the future of digital shopping in owned environments (on a website or app that the brand/retailer controls).” 

For retailers, agentic commerce represents an immense opportunity across a wide range of use cases that can automate tasks, enable faster conversions and maximize loyalty. Let’s take a look at the top eight scenarios for retail — so far.

In the era of agentic commerce, product discovery goes beyond traditional search. 

The agent interprets shopper intent (explicit query, chat, voice, past behavior) and proactively surfaces the single best product or curated bundle instead of a generic search results page. This also means an agentic-enabled shopping flow combines search, personalization, recommendations and even bundling, providing the consumer with a faster and more relevant result than traditional internet search. 

Concrete examples:

• Goal-based discovery: A shopper types “outdoor running kit for cold weather”; the agent proposes a shoe + thermal tights + gloves bundle sized to their history and applies a loyalty discount at checkout.

• Cross-channel discovery: The agent scans web, app and marketplace partners and returns a ranked list of in-stock options with delivery ETA and returns policy, saving the shopper time and keeping them in the retailer’s ecosystem.

For retailers to take advantage of agentic-enabled product discovery and personalized bundling, enhancing product data and making it machine-readable are crucial.

Agentic commerce streamlines the checkout experience by letting AI agents handle the entire process on behalf of the shopper. From adding items to the cart and applying the best discounts or loyalty points to selecting shipping and payment options, agents can complete transactions or even set up recurring orders, making checkout faster, easier and frictionless.

Concrete examples:

• One-click agentic purchase: For repeat consumables (supplements, printer ink), the agent notices low inventory, offers a refill bundle and lets the customer confirm via push or auto-checkout under pre-approved rules.

• Payment orchestration & trusted agent protocols: Industry efforts (e.g., payment networks and platform partners) are developing standards to enable agents to transact safely and reliably on behalf of users. This reduces friction and preserves trust. For example, Visa is preparing AI shopping shoppers with a new “Trusted Agent Protocol” that helps retailers distinguish between AI shopping agents and malicious bots. 

With agentic commerce, merchandising and content strategies become real-time and data-driven. AI agents continuously test and adjust homepage layouts, category pages and email creatives using live signals (conversion rates, inventory levels, seasonality), then deploy the highest-performing variations to maximize engagement and sales.

Concrete examples:

• Autonomous sale sequencing: The agent detects a SKU trending low in stock but high in intent; it adjusts on-site placement, promotes a substitute bundle and nudges customers via app notifications.

• Real-time creative personalization: The homepage hero rotates to show product bundles tailored to the visitor’s recent searches and time-of-day patterns.

Agentic commerce brings intelligence and automation to inventory, pricing and supply management. AI agents forecast demand, trigger replenishment, recommend dynamic price adjustments and allocate stock across channels. This ensures optimal availability, maximizing margins and reducing operational inefficiencies.

Concrete examples:

• Micro-fulfillment allocation: The agent routes inventory from the nearest micro-fulfillment center for same-day orders and adjusts online prices to balance demand.

• Price guardrails: The agent recommends dynamic markdowns to clear seasonal stock while preserving margin, with human override for brand or legal constraints.

• Fraud detection: Agents monitor for anomalous transactions (agent-driven or human) and verify intent/consent. Industry efforts are already underway to authenticate agent-initiated transactions and prevent fraud.

Agentic commerce transforms customer loyalty and lifecycle management by automating personalized engagement. AI agents tailor offers, retention campaigns and loyalty rewards based on customer lifetime stage, delivering win-back sequences, VIP access and point allocations that maximize value while deepening long-term relationships.

Concrete examples:

• Predictive CLTV actions: The agent identifies a high-value customer exhibiting churn signals and deploys a personalized bundle with free shipping through the channel where they engage most frequently.

• Auto-rewards: The agent automatically applies earned loyalty benefits at checkout, so shoppers never have to manually redeem them.

• Re-engagement: The agent can track past purchases, preferences and upcoming events to initiate brand re-engagement. 

• Conversational commerce: Guided selling experiences augmented by GenAI will enable retailers to provide experiences aligned with their brand voice and empathy across the entire customer journey. 

However, there’s more to loyalty in the agentic era than what meets the eye. One thing is certain: While loyalty will remain relevant, it’s shifting. As our CMO, Jen Jones, explained, “Emotional resonance doesn’t disappear, but it won’t be enough to secure placement. If a parent asks for polos under $25, an agent isn’t recalling a brand story. It’s returning a ranked list built on data clarity.” 

So, what wins loyalty in an agentic world?

• Structured data. Sizes, materials, availability — clean, consistent, machine-readable.

• Transparent pricing and stock. No fine print, no delays. Agents filter out fuzzy details.

• Algorithmic trust signals. Return policy, ratings, brand integrity — encoded so algorithms can recognize them.

These are the new rules of loyalty. Smart companies are redesigning loyalty programs so agents can parse and recommend them, tightening product data to survive algorithmic filters and experimenting with branded agents that know their customers better than any generic model.

Agentic commerce enhances revenue by delivering intelligent, context-aware upsell and cross-sell recommendations. AI agents suggest relevant add-ons at the optimal moment — on product pages, in the cart or post-purchase — and can even auto-apply offers to reduce friction and increase average order value.

Concrete examples:

• When a customer selects a digital camera, the agent suggests a lens, protective case and extended warranty bundle, and calculates the incremental monthly payment if financed, thereby increasing the average order value without being pushy.

• Real-time complementary suggestions that respect checkout friction: The agent automatically applies small discounts for add-ons; larger offers trigger a one-tap confirmation.

• Use intent signals (search phrases, dwell time, past purchases) to prioritize which upsells are displayed, then measure incremental revenue per suggestion using uplift tests.

Agentic commerce extends the power of AI agents beyond online channels into physical stores and omnichannel experiences. Agents can support smart assistants, suggest personalized in-store bundles, streamline checkout and intelligently coordinate returns and exchanges across all channels. 

Concrete example:

• A sales associate's tablet displays a best-fit bundle and real-time availability across nearby stores; the agent holds the item and offers a scheduled pickup or delivery option.

Agentic commerce improves customer satisfaction and retention by automating post-purchase care, providing proactive service and resolving issues before they escalate. 

AI agents monitor orders, anticipate customer needs,and guide consumers through returns, support and warranty processes — all within a secure, frictionless environment.

Concrete examples:

• Proactive issue resolution: After a customer receives a new laptop, the AI agent detects a common setup question from similar orders and automatically sends step-by-step setup instructions or troubleshooting guides, reducing support tickets and improving first-touch resolution.

• Automated returns and exchanges: If a customer initiates a return, the agent guides them through the process, generates shipping labels, schedules pickups and updates inventory and refunds in real-time, thereby reducing the manual support workload.

• Warranty and subscription management: The agent monitors product warranty periods and subscription renewals, sending timely reminders or automatically activating coverage to ensure compliance and increase renewal rates.

• Contextual follow-ups and engagement: Using purchase history and intent signals, the AI agent suggests complementary services (like installation, extended support or maintenance packages) and can auto-apply offers with minimal friction to boost CLV.

• Feedback collection and sentiment monitoring: Following delivery, the agent solicits reviews and analyzes sentiment to identify dissatisfied customers for immediate human follow-up, enhancing loyalty and protecting the brand's reputation.

Agentic commerce is set to expand well beyond retail, finding traction wherever transactions are repetitive, data-rich and benefit from intelligent automation. Sectors with frequent reorders, clear product attributes or complex bundling logic are especially primed for adoption.

Retail & consumer-focused industries:

• CPG & consumables: Ideal for replenishment agents and subscription-based models.

• Fashion & apparel: AI agents can drive virtual styling, clienteling, size personalization and smart bundling to reduce returns.

• Electronics & appliances: Perfect for dynamic bundles, financing options and warranty cross-sells that boost average order value.

• Groceries: Automated replenishment and subscription combinations enhance customer retention.

Emerging non-retail opportunities:

• B2B: Agents can generate quotes, recommend bulk bundles and automate contract renewals. It’s expected that 20% of B2B sellers will be forced to engage in agent-led quote negotiation in 2026. Additionally, agent-to-agent B2B commerce will come into focus for digital B2B companies.

• Healthcare & pharmacies: Intelligent reordering systems ensure compliant, timely replenishment for clinics and pharmacies. According to Kearney, 70% of consumers are willing to trust agents to find lower-cost alternatives in the health and pharmacy sectors.

• Travel & hospitality: Personalized package building (flights, hotels, experiences) and proactive loyalty management streamline the booking experience.

There’s no denying that agentic commerce presents challenges to retailers, including the risks associated with disintermediation (when they’re bypassed in favor of AI platforms) and zero-click shopping, the disruption of traditional loyalty schemes and the potential for dependency on AI platforms for visibility, conversion and, naturally, revenue. 

For retailers to win in the agentic era, they need to:

• Creating a strategy: Define how AI agents will add value across the customer journey, from discovery to post-purchase care. Align technology, data and governance to ensure secure, seamless integration while delivering differentiated, brand-led experiences and measurable business outcomes.

• Make data agent-ready: Enhance visibility and shopping functionality within AI-driven platforms through strategic partnerships and seamless integrations (e.g., ChatGPT, Perplexity).

• Map your “ownable moments.” Define distinctive, owned experiences across the journey (save a sale, protect a relationship, secure the next order) where brand-led agents can deliver value through strategies like conversational commerce.

• Prioritize trust. Offer clear privacy protections, transparent receipts, event logs and even one-tap reversals with every agent action. 

• Build a strong agentic foundation. Opt for a modular, API-first strategy that keeps your brand agile, scalable and firmly in control.

Together, these strategies position retailers not just to survive the shift to agentic commerce but to lead it across any use case, transforming AI-driven disruption into a durable competitive advantage.

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