Most retailers' first instinct for onsite AI is an LLM powered chat widget. It's understandable, but it centers on the technology, and not the shopper. Shoppers are unlikely to abandon twenty years of search-and-browse behavior just because a chat bubble appeared in the corner. The harder, more valuable work is starting with shoppers and determining how and when AI can benefit their shopping experience. That's a design problem specific to each retailer's categories, customers, and friction points, and it is solved through iterative exploration, not just adding a chat widget and calling it a day. Offsite, the infrastructure for agent-driven transactions is forming slower than last September's announcements implied. According to The Information, Away, one of the brands Shopify showcased when OpenAI launched its in-app checkout, still isn't purchasable inside ChatGPT. A DTC brand with roughly $300 million in annual revenue applied for the commerce beta and was told the rollout was proceeding slowly. The bottleneck isn't payments, which work; it's that a merchant's catalog requires hands-on data optimizing before agents can recommend inventory reliably. These struggles share a root, and the patterns we see in client conversations cluster around four tendencies: 01 BOTH / Treating catalog as a checkbox 03 ONSITE / Chasing agent traffic instead of strategy. instead of earning direct habits. Sparse or stale catalog data doesn't just lower conversion onsite; it gives agents less to work with. This no longer just lowers conversion; in the agentic world, it risks exclusion from consideration entirely.

ONSITE / Chasing agent traffic instead of strategy. instead of earning direct habits. Sparse or stale catalog data doesn't just lower conversion onsite; it gives agents less to work with. This no longer just lowers conversion; in the agentic world, it risks exclusion from consideration entirely. 02 ONSITE / Leaving first-party data unused Direct sessions protect margin, generate proprietary data, and don't depend on a platform's ranking algorithm. The goal is building onsite experiences good enough that shoppers skip agents entirely; when they don't, it's ceding the transaction to the LLM provider. 04 BOTH / Waiting for maturity instead of Returns, support tickets, post-purchase failure learning while the market forms. patterns are where category advantage lives. LLMs will keep improving at general reasoning; By the time agent traffic reaches 10-15% of they're unlikely to independently learn which of your sessions, retailers who started early will have run SKUs actually work for which use cases. dozens of learning cycles on what predicts conversion, what earns trust, and what to protect. Those defaults will be expensive to change. INTRODUCTION 01

The best shopping assistance has always been human: the ex-plumber in the plumbing aisle who asked about your project and level of expertise before recommending parts, the audio specialist who learned about your room before suggesting speakers. That expertise was expensive to staff, so it got cut. The technology to restore it at scale without the headcount now exists. This guide provides a framework for winning in agentic commerce across both frontiers. Part 1 defines the onsite/offsite split and why they're connected. Part 2 covers building onsite advantage, using ambient intelligence and outcome data to supplement what external agents can offer. Part 3 addresses how to get discovered by agentic platforms (GEO) and what to protect once you are. Part 4 reframes retail media economics as agent-driven shopping compresses the funnel. Part 5 diagnoses where the work typically stalls and how to sequence it. Appendix B has a 15-minute diagnostic. It'll tell you whether to start now or fix foundations first. 1 https://www.theinformation.com/articles/openais-shopping-ambitions-hit-messy-data-reality INTRODUCTION 01

A shopper asks ChatGPT for standing desk Some retailers will benefit (at least initially), others recommendations, gets clear on weight capacity, will get routed around. This splits Agentic commerce cable management, and height range for their setup, across two battlegrounds: then lands on a PDP with a spec sheet and 47 reviews to parse. That gap between the conversation 01 Onsite (your properties): they just had, and the experience offered is where they’re lost. This is the onsite problem: shoppers are forming expectations about what intelligent shopping feels like, and most retail sites are not meeting them. Not because the technology is missing, but because it's implemented wrong…a chat widget that waits to be discovered instead of surfacing when behavior signals confusion. Where you control the experience and hold outcome data no one else has. Where you can build habits that make external agents less necessary. This is the offense: earning direct relationships through intelligence that frontier agents can't replicate. 02 Offsite (agent ecosystems):

ignals confusion. Where you control the experience and hold outcome data no one else has. Where you can build habits that make external agents less necessary. This is the offense: earning direct relationships through intelligence that frontier agents can't replicate. 02 Offsite (agent ecosystems): The offsite problem is different but related. A growing share of shopping journeys will start in Where consideration sets form and where you agent surfaces rather than search queries. need to be discoverable without giving away your ChatGPT, Gemini, Perplexity, and whatever comes differentiation. Open protocols are arriving fast: next will shape consideration sets before shoppers Google's UCP is already live, OpenAI's commerce reach any retailer's site, and increasingly close the integration ACP is onboarding merchants now. transaction without them reaching it at all. These will standardize transactions but not whether Models can reason over unstructured content, but agents recommend you in the first place. What you they won't infer claims for you that could break trust can control: catalog depth, outcome signals, and in them. If your catalog requires guessing on what you share versus protect is covered in Parts 2 allergens, compatibility, or fit, agents will and 3. The goal is staying in the game without recommend someone else. becoming a commodity. These problems share a root: Retailers who treat onsite and offsite as competing frontier models are getting good enough at product priorities will fail at both. Retailers who see them as reasoning that shoppers will use them. two expressions of the same capability will compound advantages in both directions.

ms share a root: Retailers who treat onsite and offsite as competing frontier models are getting good enough at product priorities will fail at both. Retailers who see them as reasoning that shoppers will use them. two expressions of the same capability will compound advantages in both directions. AGENT ECOSYSTEM ONSITE AGENTIC EXPERIENCES PART 1: THE EXPECTATION GAP AND DUAL FRONTIER 03