Benchmarks
Agentic commerce benchmark: stores, categories, platforms
An agentic commerce benchmark is the aggregate result of running AI shopping agents against many stores, all together or one retail category at a time, and recording how far they get: whether the store can be discovered, whether its data answers a shopper’s question, and whether an agent can reach the payment step. Ottom publishes one benchmark across every store it has scanned, one per category, and a comparison by storefront platform, all built only from stores it has actually scanned, beside a count of how many stores an AI agent can read at all.
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All stores
Across 317 stores Ottom measured at every stage in the 90 days to 20 October 2026, in every category and including stores whose category could not be determined, the median agentic readiness score was 97 out of 100. 87% of them let an AI shopping agent reach the payment step.
97median score
Of the 317 stores Ottom measured at every stage, 84% scored 80 or better at the discover stage, against 90% at the compare stage.
The all stores benchmark in full: quartiles, each stage, the stalls that stop agents most often, and the data to cite.
Can AI agents read these stores?
Before an AI agent can compare or buy anything, it has to be let in and find something it can read. These figures come from the first step of every scan, across the 343 online stores Ottom scanned in the 90 days to 20 October 2026, in every category.
- Under 2%
Block AI agents in robots.txtEvery store scanned: 343
Fewer than 2% of the 343 online stores Ottom scanned in the 90 days to 20 October 2026 block AI agents in robots.txt.
- Under 2%
Stop agents with bot protectionStores robots.txt lets in: 343
Of the 343 stores Ottom scanned in the 90 days to 20 October 2026 whose robots.txt lets AI agents in, fewer than 2% stop an AI agent with bot protection before the storefront loads.
- Under 2%
No machine-readable product feedStores an agent could open: 341
Of the 341 stores Ottom scanned in the 90 days to 20 October 2026 whose storefront an AI agent could open, fewer than 2% publish no machine-readable product feed.
- 4%
No llms.txtStores an agent could open: 341
Of the 341 stores Ottom scanned in the 90 days to 20 October 2026 whose storefront an AI agent could open, 4% publish no llms.txt.
- 8%
No schema.org Product markupStores whose product pages an agent read: 334
Of the 334 stores Ottom scanned in the 90 days to 20 October 2026 whose product pages an AI agent could read, 8% carry no schema.org Product markup on those pages.
- 8%
No shipping costs before checkoutStores whose product pages an agent read: 334
Of the 334 stores Ottom scanned in the 90 days to 20 October 2026 whose product pages an AI agent could read, 8% give no shipping costs an agent can read before checkout.
Each share is counted over the stores that reached the point where it is checked, which the sentence names. When fewer than 5 stores show something, the exact share is not given: the panel gives a bound it is under, so no store can be singled out, or withholds the figure when the stores counted are too few for that bound to be small. A store enters Ottom’s survey only if its catalogue can be read, so these are mostly stores an agent can reach, and blocking may well be more common across online retail as a whole. The methodology sets out each denominator. The same figures as JSON.
A category is published once at least 20 stores in it have been measured at every stage inside the 90-day window. Below that line there is no page number to read: not a provisional one, not a directional one. The methodology explains why, and what every figure on a published page is counted from.
How a benchmark is built
- 1ScanAn AI agent works through a store: discover, evaluate, compare, buy. It stops before payment.
- 2One scan per storeEach store counts once, from its latest scan in the last 90 days.
- 3At least 20 storesMeasured at every stage, in one category. Below that line nothing is published.
- 4PublishMedians and quartiles, never one store’s score. Recomputed every night.
By storefront platform
The same stores grouped by the platform their storefront is built on, such as Shopify or WooCommerce. A platform is shown once at least 20 of its stores have been measured at every stage, and the comparison is published once two platforms have. Read it with its caveat: the survey reaches some platforms more easily than others, so a difference between platforms is not a verdict on either.
By store segment
The same stores grouped by what kind of store they are: the currency they price in, their price tier, how many products they list, who runs their checkout, whether it offers an express wallet and whether a bot manager answers in front of them. The headline figures are public; the full breakdown is free with an Ottom account.
Published categories
- Apparel
Across 60 apparel stores Ottom measured at every stage in the 90 days to 20 October 2026, the median agentic readiness score was 97 out of 100. 93% of them let an AI shopping agent reach the payment step.
97median score
Discover78%Evaluate85%Compare95%Buy93% - Beauty and personal care
Across 29 beauty and personal care stores Ottom measured at every stage in the 90 days to 20 October 2026, the median agentic readiness score was 97 out of 100. 90% of them let an AI shopping agent reach the payment step.
97median score
Discover79%Evaluate90%Compare97%Buy90% - Electronics
Across 22 electronics stores Ottom measured at every stage in the 90 days to 20 October 2026, the median agentic readiness score was 97 out of 100. 91% of them let an AI shopping agent reach the payment step.
97median score
Discover95%Evaluate95%Compare77%Buy91% - Food and drink
Across 31 food and drink stores Ottom measured at every stage in the 90 days to 20 October 2026, the median agentic readiness score was 97 out of 100. 81% of them let an AI shopping agent reach the payment step.
97median score
Discover97%Evaluate84%Compare94%Buy81% - Health and wellness
Across 26 health and wellness stores Ottom measured at every stage in the 90 days to 20 October 2026, the median agentic readiness score was 97 out of 100. 81% of them let an AI shopping agent reach the payment step.
97median score
Discover85%Evaluate85%Compare88%Buy81% - Home and furniture
Across 30 home and furniture stores Ottom measured at every stage in the 90 days to 20 October 2026, the median agentic readiness score was 97 out of 100. 93% of them let an AI shopping agent reach the payment step.
97median score
Discover90%Evaluate93%Compare90%Buy93% - Jewellery and accessories
Across 34 jewellery and accessories stores Ottom measured at every stage in the 90 days to 20 October 2026, the median agentic readiness score was 97 out of 100. 94% of them let an AI shopping agent reach the payment step.
97median score
Discover74%Evaluate91%Compare94%Buy94% - Outdoor and sports
Across 30 outdoor and sports stores Ottom measured at every stage in the 90 days to 20 October 2026, the median agentic readiness score was 97 out of 100. 87% of them let an AI shopping agent reach the payment step.
97median score
Discover80%Evaluate90%Compare83%Buy87%
Categories side by side
Each published category’s readiness scores on one scale, so they can be read against each other and against the score a stage needs to pass. Every category is counted the same way, from its own stores measured at every stage.
Stores passing each stage, by category
The share of each category’s stores that scored 80 or better at each stage of the journey. The deeper the green, the more stores passed.
| Category | Discover | Evaluate | Compare | Buy |
|---|---|---|---|---|
| Apparel | 78% | 85% | 95% | 93% |
| Beauty and personal care | 79% | 90% | 97% | 90% |
| Electronics | 95% | 95% | 77% | 91% |
| Food and drink | 97% | 84% | 94% | 81% |
| Health and wellness | 85% | 85% | 88% | 81% |
| Home and furniture | 90% | 93% | 90% | 93% |
| Jewellery and accessories | 74% | 91% | 94% | 94% |
| Outdoor and sports | 80% | 90% | 83% | 87% |
Categories collecting
These are not published yet. Their pages say so and are kept out of our sitemap and llms.txt until there is data behind them.
Read next
- How these benchmarks are measured
What counts as a store, what counts as a completed checkout, and the rules that decide whether a category is published at all.
- What is agentic commerce?
The definition behind every number on these pages.
- Agentic commerce statistics
Market size forecasts, adoption figures and agent success rates from other sources, each with its primary source, beside these figures.
- AI commerce ROI calculator
Put a revenue figure on the stage where agents stall on your own store.
- What stops an agent, by category: Apparel, Beauty and personal care, Electronics, Food and drink, Health and wellness, Home and furniture, Jewellery and accessories, Outdoor and sports
The mechanism behind each category, which needs no benchmark to be true.
- The agentic readiness checklist
What to fix first, in the order an agent hits it.
Get the monthly benchmark report
One email a month: the median agentic readiness score by category, the stages where AI agents stall, and how each has moved over the last month, quarter, half year and year.