CartAI

The AI agent that handles checkout.

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The buyer-side and merchant-side approaches to this are converging fast, and the tradeoff you picked is the interesting one. Protocol-based integration only works where the merchant has already done the work, which is a thin slice of the web today, so completing on the live surface covers far more ground immediately.

The part I would watch is order state after confirmation. Refunds, partial cancellations and price changes get messy to normalize when you do not own the merchant's system of record.

Do the webhooks cover post-purchase events, or is confirmation the end of the tracked lifecycle?

completing checkout on any merchant surface with zero integration work is the hard part everyone else punts on, so props for actually shipping that instead of just a demo video. curious how disputes/refunds work in practice - if the agent picks the wrong variant or size because the merchant's page was ambiguous, does the normalized order data make it easy for the end user to catch that before it ships, or does it only surface after the fact like a normal order confirmation email would?

I'm curious how merchants feel about this. Do they see agent-driven checkouts as another sales channel, or do some still treat them as bots they'd rather block?

the cooperating with bot detection instead of evading it line is the most interesting part of this. i sell software online and my first thought was whether i'd even know an agent checked out on my site. what does the merchant actually see when cartai comes through?

The agent-rails angle is genuinely interesting, especially how you're framing cooperation with bot detection as a feature rather than a workaround. One thing that would help me trust this more: add a public status page or webhook for failed checkouts, like when a merchant site changes its DOM and Catalog pricing or selection silently breaks. Right now I'd have no visibility into why an order stalled, which makes debugging painful. A simple event stream for "checkout abandoned at step X" would close that loop nicely.

Right, first-party contact means the shopper still gets the merchant's email even when your side logged a fail. What I keep circling is the fully-autonomous case where nobody reads that email: CartAI's own record stays 'failed' while the money actually moved, so the calling agent could retry and pay twice. Do you expose a reconciliation signal, a webhook or a status I can poll later, so the agent can catch a settled-after-failed order before it re-runs checkout?

Interesting because that is a pretty hard problem to solve without being considered spam.

I am curious how it'd work in Turkey as all of the payments are 2FA message guarded.

Congrats on the launch! Checkout is the part most agent demos quietly skip, so tackling it head-on is welcome. The interesting bit for me is the confirmation loop (keeping the human in control of what actually gets charged). Good luck today!

Fail-closed is the right default, and I like that you'd rather under-claim than over-claim a success. The case that still bit us was the inverse: the charge actually settled on the merchant side while our side had already marked it failed, so the user retried and paid twice. Does the per-agent token give you a way to reconcile a 'we called it failed but it settled' mismatch after the fact, or does that fall to a manual dispute?

"Cooperating with bot detection instead of evading it" is the smart call — the evasion arms race is unwinnable long-term. The part I'd worry about: an agent completing checkout on any live surface with no merchant integration is one wrong parse away from buying the wrong thing or the wrong quantity. What's the confirmation/guardrail before it actually pays? That's the line between useful and scary. Congrats on the launch.

The charged-but-no-confirmation case Waqas raised is the one I'd lose sleep over building this. When we ran agents against checkout flows we didn't control, the timeout was never clean: the backend committed but the confirmation render died, and our side couldn't tell 'it failed' from 'it succeeded and we didn't hear back'. Since the Visa and Mastercard tokens are scoped per agent, can you reconcile against the payment network's authorization record instead of the merchant's confirmation page, so a dropped render doesn't strand a real charge?

Congrats on the launch. That’s really strong use case. How do you guys manage a failed checkouts or partial checkouts so that the agent can retry safely without getting duplicate charges or orders?

The no-merchant-integration approach is impressive! How does CartAI handle last-minute price, shipping, or product changes before payment—and when does it ask for explicit user approval?

I like the idea, but checkout is one place where I’d still want a clear approval step. What happens if the price changes, an item is substituted, or the final total is higher than expected? Does CartAI pause and ask before placing the order?

Nice Lanuch! How do you handle the nasty middle state where the card gets charged but the confirmation page times out or never comes back? Curious how you guarantee idempotency and reconciliation there, and how the final state a caller receives distinguishes "order actually placed" from "charged but the merchant has no record of it.

Hi @CartAI Team,

First of all, Many many congratulations for such a great launch, 🎉

Really like the approach of working with bot detection instead of trying to sneak past it. Getting listed in Cloudflare’s verified bot directory is not something most teams can pull off.

One question though. Since you’re checking out on live merchant sites, what happens if a payment goes through but the site glitches before the confirmation shows up? Does the system know not to retry and charge the customer twice? Curious how you handle those messy edge cases, because that feels like the hardest part of making this reliable.

Rooting for you guys, the idea makes a lot of sense. Great job 👏

Checkout is exactly where agent demos need hard state boundaries. The valuable bit is not just completing a flow; it is knowing when to stop, what was authorized, what changed, and how a human can audit it later.