What happens when an AI agent goes shopping for your customer? It might be a ChatGPT plugin comparing three retailers for a winter jacket or a Perplexity agent finding the fastest delivery for a last-minute birthday gift. Welcome to agentic commerce — the shift where AI agents, not people, are making purchase decisions on behalf of real shoppers.
This is not a far-off scenario. Google and Shopify have already launched the Universal Commerce Protocol (UCP), an open standard that lets AI agents connect with any merchant to browse products, manage carts, apply discounts, and complete checkout. OpenAI released its own Agentic Commerce Protocol (ACP). The infrastructure for AI-powered shopping is being built right now, and it is accelerating fast.
Here’s what most coverage of agentic commerce misses entirely though. These AI agents do not evaluate retailers the way humans do. They do not respond to brand storytelling, visual merchandising, or emotional campaigns. They evaluate structured, machine-readable operational signals. Delivery speed, pricing transparency, return simplicity. In other words, your delivery layer is now your discoverability layer.
Every existing article in the search results for ‘agentic commerce’ defines the concept. None of them translate it into what it means for delivery operations. We’re here to fill that gap for you. Let’s explore what happens when AI agents become the primary shopping interface, and why delivery and returns are emerging as the new ranking factors that determine which retailers get recommended and which get skipped.
Why should e-commerce leaders care
Agentic commerce is the emerging model where autonomous AI agents discover, evaluate, negotiate, and transact on behalf of consumers. Rather than a person scrolling through search results and clicking ‘add to cart’, an AI agent handles the entire process — from finding the product to completing the purchase — based on the shopper’s preferences, constraints, and history.
The concept is moving rapidly from theory to infrastructure. OpenAI’s Commerce API allows AI agents to interact with merchant systems directly. Google and Shopify co-developed UCP to create a universal language for agents to negotiate with retailers across payment methods, discount codes, loyalty programs, and critically, delivery options. As Google’s VP of Merchant Shopping put it, agentic commerce requires a shared language across the ecosystem, and UCP provides that framework.
From human browsing to machine evaluation
Humans shop with emotion, brand loyalty, and visual appeal. They linger on product pages, read reviews, and can be swayed by photography and copy. AI agents shop differently. They operate on structured data, APIs, and performance signals. They compare options across multiple retailers simultaneously, and they optimize for the criteria their user has set — fastest delivery, lowest total cost, best return policy, or highest reliability.
This means that the signals AI agents evaluate are fundamentally different from what traditional e-commerce optimization targets. Beautifully designed UX is invisible to an agent parsing an API response. What matters instead is the clarity, accuracy, and machine-readability of the data behind it.
What AI agents actually look for
When an AI agent evaluates a retailer, it examines a set of operational signals that many retailers still overlook as competitive differentiators even in traditional e-commerce shopping. These include delivery promise accuracy and specificity — not vague ranges like ‘3-5 business days’ but precise dates like ‘arrives Thursday’ — and delivery reliability based on historical performance data.
They also include pricing transparency, where the total cost including delivery is available upfront with no surprises at checkout. Add to that return policy clarity, where structured, API-accessible terms beat policies buried in FAQ pages or PDFs. It’s exactly the same agentic shopping principle as with the product data, in which ‘100% organic cotton, 200 GSM’ wins over ‘delightfully soft to the touch’.
Delivery as your new discovery layer
If agentic commerce changes what gets evaluated, delivery is where the consequences are most concrete. When AI agents compare retailers in real time, your delivery capabilities become a ranking factor before the sale.
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Delivery promise as a machine-readable trust signal
Consider how an AI agent handles a simple shopping request: “Find me a pair of running shoes under €120, delivered by Friday.” The agent queries multiple retailers simultaneously. Retailer A responds with “arrives Thursday by 6pm” — specific, calculated from real carrier performance data, dynamically adjusted for this particular order. Retailer B responds with “3-5 business days” — a static estimate set on a quarterly basis that may or may not hold. The agent recommends Retailer A.
That’s the decisive gap between adaptive and static delivery infrastructure. Retailers using AI-predicted delivery windows — calculated from real-time and historical fulfillment data, accounting for carrier behavior, destination and seasonality — produce precise, reliable promises that AI agents can trust and recommend. Retailers still relying on manually configured delivery rules that have to be widened during peak season ‘just to be safe,’ produce the vagueness that agents penalize.
Delivery pricing as a conversion signal
AI agents also evaluate the total cost of the transaction, and they are particularly sensitive to pricing opacity. A retailer that shows ‘free shipping’ but adds a surcharge at checkout would frustrate a human user, but it also sends a negative signal to an AI agent that evaluates pricing consistency and transparency across every step.
That’s where delivery pricing aligned with real delivery economics outperforms flat-rate models. When pricing reflects the actual cost of each delivery — staying competitive where carrier costs are low and adjusting appropriately where they are higher — it creates the consistency and transparency that agents interpret as reliable. Flat pricing, by contrast, creates unpredictable variation that erodes agent trust over repeated interactions.
Carrier performance as a competitive moat
AI agents have memory. Unlike human shoppers who may not remember that their last order from a particular retailer arrived two days late, an AI agent tracking performance across interactions builds a cumulative reliability profile. Retailer A whose orders consistently arrive on time accumulates a compounding trust advantage, while retailer B with inconsistent fulfillment sees its agent recommendations decline over time.
This is where connected delivery infrastructure — where checkout data, tracking outcomes, and return performance feed into a single intelligence layer — creates a data flywheel that standalone tools simply cannot replicate. The retailers who connect these systems build an operational advantage that grows with every order.
Returns are the silent ranking factor
Most discussions of agentic commerce focus on the purchase. Almost none address what happens when something goes wrong. That’s a significant blind spot, because an AI agent optimizing for customer satisfaction must factor in a critical question — what happens if this does not work out?
Why AI agents evaluate return policies
Think about it from the agent’s perspective. Its job is to make the best possible recommendation for its user. That means considering not just price, speed, and availability, but also the risk of the purchase. How easy is it to return? How long is the return window? How fast is the refund processed? Can the item be exchanged instead? For an AI agent evaluating risk-adjusted value, return policy clarity is a first-order signal.
Return policy clarity, return window policy, refund speed, and exchange flexibility all become machine-evaluable data points. The retailer with structured, API-accessible return terms gives the agent exactly what it needs to make a confident recommendation. The retailer whose return policy lives in a PDF or a buried FAQ page is effectively invisible in this dimension.
Operational gap most retailers haven’t closed
Online return policies should be agent-readable. At this point, they exist as human-language text on a webpage, full of conditions, exceptions, and qualifications that require interpretation. An AI agent cannot reliably parse a paragraph of legal text to determine whether a specific product is returnable, what the window is, or who pays for return shipping. Again, AI agents compare based on data, not design, so structured data is the new foundation.
The solution is not just rewriting the policy in simpler language. It is making return terms available as structured data that agents can query directly. This is no different from how product data moved from catalogue descriptions to machine-readable feeds over the past decade. The retailers who made that transition early gained a discovery advantage. The same dynamic is about to play out with delivery and return data.
Connect return performance back to checkout intelligence
The most forward-thinking approach goes further: it connects return outcomes back to checkout decisions. When return data feeds into the same intelligence layer that shapes delivery promises and pricing, retailers can dynamically adjust return terms based on product category, customer behavior, and historical return patterns. This creates a closed loop where operational data from every completed transaction — whether it ends in a kept purchase or a return — strengthens the intelligence that powers the next one.
This connected approach is rare in the current market. Many delivery platforms treat checkout and returns as separate products, often priced independently and technically disconnected. Checkout-only tools see nothing after the purchase button. The retailers who unify these systems are building the operational excellence that AI agents will increasingly use to differentiate recommendations.
Checklist: is your delivery stack AI-agent ready?
Understanding the shift is one thing. Preparing for it is another. Here is a practical framework for evaluating whether your delivery infrastructure is ready for a world where AI agents are the gatekeepers of customer attention.
Infrastructure shift to delivery intelligence
The pattern across delivery promises, pricing, and returns points to a fundamental infrastructure question — can your delivery stack adapt in real time, or is it built on static rules that break when conditions change?
Most delivery platforms were designed around quarterly configuration, where you set a delivery range with a buffer, pick a flat shipping rate, order methods manually, and revisit next season. That was workable when only humans evaluated the result. An AI agent comparing three retailers in real time notices the gap immediately, and a delivery promise that was accurate last quarter but broke this week damages agent trust in a way that is difficult to recover from.
The alternative is infrastructure that learns. Delivery times predicted from real fulfillment outcomes. Methods ranked by actual carrier cost and performance. Pricing aligned with shipment economics rather than averaged estimates. Every completed order — delivery or return — feeding back into the intelligence that shapes the next checkout. The technology exists today. The question is whether retailers’ delivery stacks are built to use it.
Retailers who win in agentic shopping
The delivery experience has always mattered to customers, and now agentic commerce rewards the best operational reality. The retailers who will get AI agents to recommend them to customers are the ones investing in delivery intelligence today: AI-predicted delivery promises that adapt in real time, pricing aligned with actual delivery economics, return policies that are structured and agent-readable, and connected data that builds trust with every fulfilled order.
