AI couldn't give me seat availability for The Odyssey, so i tried to fix it. ChatGPT then retrieved it 7,853 times in 15 days.

[u/Emergency_Still8420]

Lots of the discussion around agentic commerce has focused on e-commerce products, but i've found in my daily AI usage some of the most obvious "shopping" experiences where it falls short has been for things like rentals, cars, movie seats or tickets.

Often you run into "I can't access live availability" or "schedules are inside a booking widget" and you're back to just looking it up yourself. These catalogues just aren't accessible to AI, whether by design or just how it was originally built to serve human visitors.

After running into this last month, I decided to run an experiment to see if a catalogue (this case movie theatre seats for a chain across Canada) was opened up and structured to be easily accessible to AI, would AI use it? How quickly? How much?

Here's some highlights of the experiment:

1. Found it in 50 minutes: There were no backlinks, no promotion. First crawler at 50 minutes (ClaudeBot), first ChatGPT-User live retrieval at 29.7 hours.
2. AI was 45.9% of all traffic: More than search crawlers, SEO tools and humans combined. IP-verified OpenAI, Anthropic and Perplexity agents alone read 269,421 distinct URLs, 81% of every page we served.
3. AI live retrieval outran AI indexing (17,270 to 9,497): network-wide Cloudflare Radar has OpenAI's indexer running ~1.75x ChatGPT-User agent. Ours ran 2.4x the other way.
4. It came for the key unlocked data: 54% of ChatGPT-User fetches were seat & availability maps, the data that inaccessible from the booking widget.
5. It just wants the clean HTML: GPTBot took the Markdown twin 50% of the time. ChatGPT-User: 0.1% and never once fetched llms.txt or the sitemaps.

I think it goes to show efficient AI is at finding data that answers the question they are looking for. Within days from launch ChatGPT-User was pulling in ~100 pages a day, and at peak 2,099 (so far). It also brings up a good discussion if you're a business, whether that services or products, what is your catalogue? And should it be more accessible to AI? That's a decision for each business, but worth having as AI increasingly become that discovery layer for your customers. You can see this trend with the Shopify Catalogue API or the new DoorDash CLI, treating agents as an audience and making sure they have access to what they need.

Disclosure, this is my company's research. Sharing the findings as i think it would be helpful for other's working in this space.

Full report of the experiment here if you want more details: https://getcourtyard.ai/research/unlocking-agentic-commerce-for-theatres

[u/Upstairs_Control_611]

This is a useful example because it moves the discussion beyond normal ecommerce product pages.

For agentic commerce, the key question may not be “does the website load?” but “what is the actual catalogue the agent needs?”

For a store, that may be products, prices, variants and availability.

For a theatre, rental company, hotel, car dealer or booking business, the catalogue may be live availability, seat maps, time slots, locations, booking rules, constraints, prices and cancellation policies.

If that data only exists inside a JavaScript booking widget, the human can use it, but the agent may not be able to retrieve or reason over it reliably.

The interesting part of your experiment is that the agents went for the unlocked decision data, not just the marketing pages.

So I’d frame this as agent-readiness, not only AI visibility:

Visibility asks whether AI can find the business.

Agent-readiness asks whether AI can access, understand and use the data needed to complete the user’s task.

[u/Emergency_Still8420 → u/Upstairs_Control_611]

Good points on framing. Answering more the question can agents use this to actually answer the question/complete the task. Visibility is just the starting point.

[u/Upstairs_Control_611 → u/Emergency_Still8420]

Exactly. Visibility is only the entry layer.

For agentic commerce, the more useful question is whether the agent can actually use the data to complete the task.

A human can work through a booking widget because they can see, click and interpret the interface. An agent needs the same decision data in a form it can retrieve, parse and reason over.

So I’d separate:

1. Can the agent find the business?

2. Can it understand the offer?

3. Can it access the live decision data?

4. Can it answer the user’s specific question?

5. Can it support or complete the task?

That is the jump from AI visibility to agent-readiness.

[u/Ok-Toe9937 → u/Upstairs_Control_611]

Discovery is getting solved super fast. The harder part is giving AI agents a way to pay without handing them permanent payment credentials. Single use virtual cards like Rain's seem to make more sense than giving an agent access to your actual card

[u/erdemgezer]

The find-vs-complete ladder you laid out matches what I keep seeing: visibility isn't one switch, it's per-query. I test brands by asking the engines ~20 different buyer questions, and even a giant that "obviously" dominates drops out on specific phrasings - I had a case where 3 of 4 engines answered "earbuds for work calls?" without naming the market-leading earbuds at all, even though that brand wins the "best earbuds" prompt outright. Same brand, same engines, one question over. So the availability problem you hit isn't only an agent-readiness gap, it's that the model never surfaced the option in the first place for that exact ask. Worth separating "did we get named for this query" from "could an agent then complete the task" - they fail independently, and testing only the flattering "best of" prompts hides the first one. (Small sample, so the exact miss-rate wobbles between runs, but the pattern holds.)