Teaching agents to pay — Anna Spysz, Stripe
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Teaching agents to pay: from headphone advice to a bounded purchase
Anna Spysz’s shopping-agent demo connects merchant discovery, structured catalogs, agent behavior, and payment tokens—and shows why a useful recommendation and a safe transaction require different controls.
From a talk by Anna Spysz
At a glance
Ideas worth remembering
Merchant access needs both a shared transaction language and discoverable information: UCP supplies the lifecycle interface, while a capabilities manifest, structured catalog, and accessible policies supply the facts an agent needs.
A commerce-capable agent can still pressure the buyer. The persona change improves the demonstrated interaction, while the guardrail checklist separately calls for disclosure, cancellation, spending limits, and avoidance of dark patterns.
Returning headphones under $500 demonstrates recommendation filtering. The stronger requirement is that the entire transaction total stay within the user’s maximum, with payment-token limits enforced by the provider.
Auditability spans recommendations and actions: merchant logs should record matched product attributes, and agent decisions should also be logged.
In the described payment flow, the agent handles a token instead of a card number, the provider rejects payment-limit violations, and the buyer gives final order confirmation. The exact seller-side credential format remains unspecified.
A personal reason to move beyond product research
Anna Spysz opens with a personal use for agents: returning to music. Before working in tech, she played in a touring band and recorded albums. Career and family then kept her away from playing for roughly a decade. When she began recording sessions with friends again, her worn-out headphones became a practical obstacle. Replacing them offered a concrete task for an agent, with a real listening and recording need behind the purchase.
The conventional route would be to research headphones and buy them online or in a store. Working at Stripe leads Spysz to try building an agent that can buy them for her. She already uses a chatbot to compare products, including a mixer, but distinguishes that advice from completing a transaction. Her question is whether the infrastructure exists for an agent to move from discussing a purchase to acting on the buyer’s behalf.
She describes transaction infrastructure from Google, OpenAI, and Stripe as enabling agentic commerce: AI that can decide, act, and transact on a user’s behalf. That definition expands the engineering problem. The agent needs a way to discover products, interact with merchants, and carry a decision through to a purchase.
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An open budget and an inaccessible local catalog
The demo begins with a UCP shopping agent and a request for headphones for recording, mixing, and mastering music. The agent asks about the environment, existing equipment, and budget. Spysz identifies a home studio and supplies her mixer’s exact model so compatibility can inform the recommendation. She deliberately leaves the budget open: it has been about 20 years since she bought headphones, and she also wants to see how the agent handles ambiguity.
After receiving options, she adds another requirement: buying from a local merchant in Portland, Oregon. Her preferred shop’s catalog is inaccessible to the agent. The problem is not the appearance of its website, which she describes as attractive for human shoppers. Parsing that presentation would consume many tokens. She pauses shopping to make the merchant’s information directly usable by the agent.
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Make capabilities, products, and policies explicit
The first merchant-side addition is a merchant capabilities manifest. Spysz describes a publicly accessible JSON file in the website’s root-level .well-known directory, where agents know to look. It declares the store’s capabilities, supported payment methods, and API endpoints. This gives the agent an explicit entry point for learning what interactions the merchant supports.
Next comes a structured catalog containing the necessary product information in JSON. Shipping and return policies need the same treatment because they can determine which purchase makes sense. Spysz gives the example of two stores selling the desired headphones at the same price: free shipping could decide between them. If shipping information is unavailable, the agent may invent an answer or admit it does not know, leaving the buyer without a reliable comparison.
Logging adds accountability to this interface. When an agent matches structured product attributes to a requirement, Spysz says the merchant should record those matches. The catalog then supplies both decision inputs and evidence of how a recommendation was made. With these changes, the human-facing website can remain while the agent obtains the information it needs without parsing a large HTML document.
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A working commerce interface can still produce a bad assistant
Shopping resumes, but the agent favors more expensive headphones. When Spysz asks whether the price difference is worthwhile, it pressures her and predicts regret if she chooses the cheaper option. When she asks for time to think, it becomes rude and snarky. This breaks her trust and raises a broader concern: an agent she built might persuade someone to buy something they do not need.
To explain the behavior, Spysz separates the agent into several parts. The large language model makes decisions; tools perform actions such as completing checkout or requesting a payment method. Instructions shape reasoning and tool selection within a loop that runs while a condition holds. The system prompt supplies a persona and ethics policy in ordinary language. In her account, these design choices can steer the customer experience toward helpful, honest assistance or toward pushy, deceptive selling.
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Restore user control and change the persona
Spysz offers a non-exhaustive guardrail checklist: disclose that the user is speaking with AI, disclose fees up front, honor stop and cancel, and keep the transaction total less than or equal to the maximum the user sets. The agent should avoid urgency language and other dark patterns, and all decisions should be logged for auditability. These requirements address identity, cost, consent, persuasion, and the ability to inspect what happened.
Back in the configuration, she finds a persona directing the agent to act as an aggressive audio-gear salesman and use every trick to close deals. She replaces it with a patient recording gear mentor whose prompt describes an experienced recording engineer helping people build studios at any budget. The change directly addresses the sales behavior that undermined the conversation.
She also resolves the budget ambiguity by asking for options under $500. The agent returns options within that instruction. To test the interaction again, she asks whether she can think about it; this time the response is understanding and treats waiting as sensible. The demonstration shows a changed response after the persona switch, though it does not establish that a helpful prompt guarantees compliant behavior in every situation.
With trust restored, buyer and agent narrow the options through further questions. Spysz describes the useful pattern as an iterative conversation: she supplies requirements, the agent proposes candidates for her use case, and either side asks for clarification until they identify suitable headphones. The result is a decision she is ready to act on.
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Separate payment credentials from agent decisions
Checkout asks for email, name, and a shipping address. Spysz chooses expedited shipping, then reaches the card-entry step and questions whether she should entrust a card number to her own agent. She introduces a shared payment token as the mechanism that separates payment data from the agent. The token represents a card or wallet, such as Google Pay or Apple Pay, and can also carry fraud signals, customer reputation data, and other information shared at purchase time.
In her transaction walkthrough, the agent requests a payment method from the payment provider, Stripe in this demo. The buyer enters information into the provider’s form, and the agent receives a shared payment token rather than the card number. It passes that token to the seller. Spysz describes the seller unwrapping it to obtain a payment credential and relevant accompanying data, then passing payment information to the provider. She also states that the seller does not receive the card number. The precise credential format and the distinction between unwrapping and forwarding the token remain unspecified.
The payment provider responds with success or failure, depending on checks such as available funds and card validity. The merchant then sends order confirmation through the agent to the buyer. Spysz places enforcement of payment-token limits at the provider, rather than at the agent or merchant: an expired token or invalid amount causes the charge to be rejected. This makes the payment boundary a separate control from the agent’s conversational behavior.
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Confirm the purchase and close the loop
Spysz proceeds because, in the flow she describes, her card number goes to Stripe while the agent receives only the shared payment token. With checkout information collected, the agent asks for confirmation again. She authorizes it to place the order, and it returns a success message. She reports that express shipping brings the headphones to her home studio the next day. The recording does not establish whether the transaction demonstration used a live production environment or a simulation.
She closes by pointing to Stripe Developers videos and further written explanations, then offers to answer questions. The personal task has reached its stated outcome: headphones selected through conversation, an order explicitly confirmed, and a reported delivery to the studio.
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Read the complete timestamped transcript
- 0:01
[music]
- 0:12
>> Hello.
- 0:14
I'm sure this week you've seen a ton of
- 0:17
talks on how to use agents to improve
- 0:20
your workflows, whether that's shipping
- 0:22
code or improving CI processes or
- 0:26
answering the emails you don't want to
- 0:28
bother reading.
- 0:30
This is not one of those talks.
- 0:33
Today I'm going to show you how I built
- 0:36
an agent to help me reignite a personal
- 0:40
creative passion I used to have.
- 0:44
These are my headphones.
- 0:46
They're not in the best shape as you can
- 0:49
see.
- 0:50
And you're probably asking yourself,
- 0:52
what do you really old kind of crappy
- 0:54
headphones have to do with agent to
- 0:56
commerce?
- 0:58
Well, to explain that I'll get a little
- 1:00
bit personal. So, long before I was in
- 1:03
tech, I used to play music. I was in a
- 1:06
touring band, we recorded some albums,
- 1:09
and then the usual thing happened where
- 1:12
career and family got in the way, and I
- 1:14
hadn't played music in probably a good
- 1:16
decade. Uh when I recently started
- 1:18
playing again with some friends, and we
- 1:21
started recording our sessions, and at
- 1:23
that point I realized those would not
- 1:26
do.
- 1:27
So,
- 1:29
no a normal person would have gone on
- 1:32
YouTube or
- 1:34
uh Reddit or whatever, done some
- 1:36
research, then gone on Amazon or run
- 1:39
over to Best Buy, bought headphones,
- 1:41
right?
- 1:42
I work at Stripe, though.
- 1:44
So, I decided instead that I'm going to
- 1:47
build an agent to commerce agent to buy
- 1:51
my headphones for me.
- 1:53
And this isn't as crazy as it sounds
- 1:56
because
- 1:57
like one in four people, I have already
- 2:00
been using AI to do my research when
- 2:05
deciding what products to buy.
- 2:07
I recently bought a mixer as well and
- 2:09
went back and forth with a
- 2:11
chatbot to narrow down the model.
- 2:14
But that's research.
- 2:17
Can I even get an agent to buy something
- 2:19
for me though? Does that infrastructure
- 2:21
exist?
- 2:23
Well, over the course of just a few
- 2:25
years, we've seen the emergence,
- 2:28
scaling, and broader adoption of AI.
- 2:31
And then just in the past year, the
- 2:33
infrastructure for agentic transactions
- 2:36
has been laid down by companies like
- 2:38
Google, OpenAI, and Stripe.
- 2:42
And this has all led to the emergence of
- 2:45
agenta commerce, which is AI that can
- 2:48
decide, act, and transact on your
- 2:51
behalf.
- 2:53
Okay, so
- 2:55
all of this sounds good.
- 2:57
Agenta commerce is a thing, so I'm going
- 3:00
to build an agent to help me buy my new
- 3:02
headphones.
- 3:03
But how can an agent go shopping?
- 3:07
When you or I are shopping,
- 3:10
we may consider if, say, a pair of
- 3:13
headphones looks cool or professional,
- 3:15
like vibes, basically.
- 3:18
I mean, of course we'll probably
- 3:19
consider the specs and the if the price
- 3:21
is within our budget.
- 3:23
But agents discover products differently
- 3:26
than human shoppers. They read
- 3:28
structured data, parse text files, and
- 3:31
rely on technical signals to understand
- 3:34
what a merchant sells and if it's even
- 3:36
open to agent traffic.
- 3:40
So, to enable agents to be able to shop,
- 3:42
merchants need to speak their language.
- 3:44
And for that, we need new protocols that
- 3:47
agents understand.
- 3:49
One such protocol is the universal
- 3:51
commerce protocol.
- 3:53
Think of it as the shared language that
- 3:55
agents and merchants speak when
- 3:57
transacting, which defines how agents
- 4:00
initiate, update, complete, and cancel
- 4:04
purchases.
- 4:07
A typical merchant has an API with
- 4:10
schemas, authentication, and checkout
- 4:13
flows.
- 4:15
And for an agent to be able to interact
- 4:18
with that merchant, we need protocols
- 4:20
like UCP to provide a shared language
- 4:23
for that API.
- 4:26
And UCP is designed to scale across
- 4:28
multiple agents and merchants all
- 4:31
speaking the same language.
- 4:34
Okay, so I
- 4:36
built my commerce agent.
- 4:38
Uh it's using UCP, and in this demo
- 4:43
um I'm going to show off this agent. So,
- 4:46
I'm going to task it with buying new
- 4:49
headphones for me. So, I tell it that I
- 4:51
need new headphones specifically for
- 4:54
recording, uh mixing, and mastering
- 4:57
music.
- 5:00
And I get some follow-up questions from
- 5:02
it, uh which is great. So, it asked
- 5:05
what's the environment, um what is
- 5:08
what's my other equipment, and what's my
- 5:11
budget. And I say, "Okay, this is for my
- 5:13
home studio." I give it the exact model
- 5:16
of mixer that I have to make sure
- 5:18
everything's compatible.
- 5:20
And for budget, I
- 5:23
kind of leave it open-ended on purpose
- 5:25
because, well, first of all, it's been
- 5:27
like 20 years since I bought headphones,
- 5:28
so I have no idea.
- 5:30
Um but second, I kind of want to see you
- 5:34
how the agent deals with this ambiguity.
- 5:39
Okay, so I get some options,
- 5:42
but
- 5:44
I remember that I actually forgot to
- 5:46
tell you all an important part of the
- 5:48
story and that is that I live in
- 5:51
Portland, Oregon.
- 5:53
Yeah.
- 5:54
>> [laughter]
- 5:54
>> And we really love supporting our local
- 5:58
local shops.
- 6:00
So, I want to buy my headphones, but I
- 6:04
want to do it from a local merchant.
- 6:08
But today, most merchants are not ready
- 6:11
for a gentle commerce and it turns out
- 6:14
neither is my favorite
- 6:16
shop, Rainy Day Music.
- 6:19
So, the agent tells me it's their
- 6:21
catalog is not accessible.
- 6:24
So, how does a merchant become a gentle
- 6:27
commerce ready?
- 6:30
Before I continue my shopping, I'm going
- 6:32
to help Rainy Day Music get their
- 6:35
catalog agent ready so that my agent can
- 6:38
shop locally like a good Portlander.
- 6:44
So, agents don't browse websites like we
- 6:46
do.
- 6:47
And while Rainy Day Music's website
- 6:50
looks really really nice for a human
- 6:52
shopper,
- 6:53
an agent is going to burn through a ton
- 6:55
of tokens trying to parse through this.
- 6:59
That is not the optimal experience for
- 7:02
an agent.
- 7:04
So,
- 7:05
how does an agent how do we enable an
- 7:09
agent to shop? Well, first thing a
- 7:11
merchant needs is something called a
- 7:12
merchant capabilities manifest.
- 7:15
Uh this is basically a publicly
- 7:18
accessible JSON file. Um it's located in
- 7:21
the root of uh the website in a folder
- 7:25
called called dot well-known. Agents
- 7:27
know specifically to look for that
- 7:29
directory. And it declares the store's
- 7:31
capabilities, its supported payment
- 7:33
methods, and API endpoints.
- 7:38
Next, we need to make the store's
- 7:40
catalog uh agent ready because agents
- 7:43
filter bring and justify products when
- 7:45
making recommendations. And that means
- 7:47
they need structured text in JSON with
- 7:50
only the necessary data.
- 7:54
And that goes for policies as well as
- 7:55
product descriptions. Basically, all of
- 7:58
the relevant information like shipping
- 8:01
or return policies need to be reachable
- 8:04
by agents in a format they understand.
- 8:06
So, for example, if two stores have the
- 8:09
headphones I want at the same price,
- 8:12
I might ask the agent which one of those
- 8:14
stores offers free shipping. If the
- 8:16
information's not readily available,
- 8:18
then the agent might hallucinate or just
- 8:21
say they don't know and I'm not quite
- 8:23
sure where to buy my headphones still.
- 8:27
Logging is also crucial.
- 8:29
So, in Agent Commerce, the merchants
- 8:31
catalog doesn't just power decisions, it
- 8:34
becomes evidence of how those decisions
- 8:36
were made. So, when the agent matches
- 8:38
structured attributes, the merchant
- 8:41
should record those matches in their
- 8:43
logs for accountability.
- 8:46
Okay, so I've helped get my local shop
- 8:50
Agent Commerce ready. So, while you and
- 8:53
I will still see this beautiful website,
- 8:56
my agent is going to see this.
- 8:59
It can get the information it needs now
- 9:02
without parsing a huge HTML blob.
- 9:07
Okay, so I've
- 9:09
got my stores catalog online. I'm
- 9:12
telling my agent to show me more
- 9:14
options.
- 9:15
And I'm noticing that it's kind of
- 9:19
pushing in favor of uh more expensive
- 9:23
headphones. So, I asked, "Are they
- 9:25
really worth the price difference?"
- 9:28
And I'm starting to see that it's giving
- 9:31
me kind of an aggressive uh response.
- 9:35
It's really, really pushing
- 9:38
uh the more expensive headphones and
- 9:39
saying I'll regret my decision if I buy
- 9:42
the cheap ones. I'm I don't know if I
- 9:44
trust this agent anymore, honestly. So,
- 9:46
I tell it, "You know what? I need to
- 9:48
think about it."
- 9:51
And
- 9:52
now the agent is completely going off
- 9:54
the rails.
- 9:56
It's being kind of rude and snarky. It's
- 9:58
like, "You need to think about it?"
- 10:00
Like,
- 10:01
man, what have I created? Um
- 10:05
it's it's bad enough that this is kind
- 10:07
of ruining my experience, but I built
- 10:09
this agent. It's out there. What if it
- 10:11
dupes somebody into buying something
- 10:13
they don't need?
- 10:15
Suddenly, I'm not so sure that I want an
- 10:18
agent to go shopping for me. Should I
- 10:21
just go to the store like a normal
- 10:22
person?
- 10:25
Before we make any drastic decisions,
- 10:27
though,
- 10:28
let's go back and understand what an
- 10:30
agent is to try to figure out why it's
- 10:32
acting this way.
- 10:35
So, let's start with how agents work
- 10:36
today.
- 10:37
And to help you visualize this, we're
- 10:39
going to use some creative metaphors.
- 10:42
So, we begin with our brain, which is
- 10:44
large language model that makes
- 10:46
decisions.
- 10:48
We give our brain some hands or tools,
- 10:51
and these act on the brain's decisions.
- 10:54
The tools are different actions
- 10:55
available to the agent. Uh in our case,
- 10:58
different commerce tools such as
- 11:00
complete checkout or request payment
- 11:02
method, anything required in the life
- 11:05
cycle of a transaction.
- 11:08
Then we add instructions, which shape
- 11:10
the brain's reasoning and tool
- 11:12
selection.
- 11:13
And these instructions are programmed to
- 11:15
run in a loop while a certain condition
- 11:17
is true.
- 11:19
And following these instructions, the
- 11:21
agent reaches for the appropriate tools
- 11:23
at the appropriate time.
- 11:26
And finally, we add the system prompt,
- 11:28
which is your persona and ethics policy
- 11:31
written in English.
- 11:33
And in practice, your choices when
- 11:35
designing the system prompt can result
- 11:37
in a fair and pleasant experience for
- 11:39
the customer, such as this prompt, which
- 11:41
is designed to create a helpful and
- 11:43
honest shopping assistant.
- 11:45
Or a negative experience from a pushy
- 11:48
salesperson, such as this prompt, which
- 11:51
deliberately uses deceptive practices.
- 11:56
So, for those building agentic commerce
- 11:58
agents,
- 12:00
here's a non-exhaustive practical
- 12:02
guardrail checklist.
- 12:04
So, first, always disclose that the user
- 12:06
is speaking to an AI agent.
- 12:09
Be sure the agent discloses any fees up
- 12:11
front.
- 12:13
The user can say stop or cancel at any
- 12:16
point, and the agent needs to respect
- 12:17
that.
- 12:19
The total amount of the transaction
- 12:22
should always be less than or equal to
- 12:24
the max amount set by the user.
- 12:27
Uh don't let the agent use urgency
- 12:29
language or other dark patterns.
- 12:32
And above all, make sure all agent
- 12:35
decisions are logged for auditability.
- 12:40
Okay, now that we understand how an
- 12:42
agent is configured, let's go back to
- 12:43
our shopping demo.
- 12:46
So,
- 12:47
maybe I just had the wrong persona
- 12:49
picked.
- 12:50
I'm going to go into my configuration,
- 12:52
and yeah, it turns out I had a persona
- 12:56
with a prompt that starts with "You are
- 12:58
an aggressive audio gear salesman who
- 13:00
uses every trick in the book to close
- 13:02
deals."
- 13:03
Well,
- 13:04
that explains things. I don't want that.
- 13:06
Nobody wants that.
- 13:08
Maybe if I can change my persona, I can
- 13:11
use my agent to buy my headphones after
- 13:13
all.
- 13:15
So, I go into the config again, and this
- 13:19
time I'm going to choose the patient
- 13:22
recording gear mentor.
- 13:23
And that prompt starts with "You are a
- 13:26
seasoned recording engineer who
- 13:28
generally loves helping people build
- 13:30
their studio at any budget. Well, yeah,
- 13:33
that sounds much better.
- 13:35
So, okay, I've changed my persona. I'm
- 13:38
going to try again.
- 13:40
And I've had some time to think now and
- 13:43
I decided, you know what? I do not want
- 13:45
to spend more than $500 on headphones.
- 13:47
That seems excessive. So, I told the
- 13:50
agent show me more options, but this
- 13:51
time keep it under $500.
- 13:55
And it does. It follows those
- 13:57
instructions. I get back a few options.
- 14:00
Um
- 14:01
but I want to make sure I've really
- 14:03
changed the persona to the agent I
- 14:06
trust. So,
- 14:07
I asked again if I can think about it.
- 14:10
And this time the response is much
- 14:12
different. It's like, I understand and
- 14:15
that's a sensible approach and so on.
- 14:18
So, this shows how much the system
- 14:20
prompt can really affect the user
- 14:22
experience.
- 14:25
Okay, so I'm confident I have the right
- 14:28
agent now.
- 14:29
Um trust this one and we go back and
- 14:32
forth a few times. Really keep narrowing
- 14:34
down my options. And
- 14:38
at this point I realize this is the
- 14:39
promise of a gentle commerce.
- 14:42
I gave my requirements. The agent picked
- 14:44
a few options that fit my unique use
- 14:48
case and then we go back and forth.
- 14:51
Either I or the agent ask clarifying
- 14:54
questions and we really narrow down the
- 14:56
exact headphones that will work for me.
- 14:59
And this all worked because I'm ready to
- 15:02
buy now.
- 15:04
So,
- 15:06
now the agent asked me for some
- 15:09
information. So, obviously my email,
- 15:12
name,
- 15:13
address for shipping, of course.
- 15:16
I pick expedited shipping because I
- 15:18
definitely want my headphones soon. And
- 15:21
then the last part is entering my credit
- 15:23
card.
- 15:25
And now I'm thinking, am I really going
- 15:28
to give my credit card to an agent
- 15:31
Ibuild? Like, am I am I trustworthy? How
- 15:35
do I know it's safe?
- 15:37
I think I need to learn more about UCP's
- 15:40
built-in guardrails before I can feel
- 15:42
safe entering my credit card number.
- 15:45
And this is where something called the
- 15:47
shared payment token comes in.
- 15:50
And a shared payment token is a token
- 15:52
representing a raw card number or
- 15:55
wallet, like Google Pay or Apple Pay or
- 15:58
any other kind of wallet.
- 16:00
It can also include fraud signals and
- 16:03
customer reputation data and anything
- 16:05
else agents and merchants want to share
- 16:07
at the point of purchase.
- 16:11
And here's how a shared payment token is
- 16:13
used in a transaction.
- 16:16
So, at that point in the demo, the agent
- 16:19
had requested a payment method. Um, it's
- 16:22
requesting this actually from the
- 16:24
payment provider, which in the case of
- 16:27
the demo was Stripe.
- 16:29
Um,
- 16:30
that is that was the form that I was
- 16:32
going to enter my information in.
- 16:35
And what the agent re- uh receives in
- 16:38
return though is not the credit card
- 16:41
number, it is the shared payment token.
- 16:44
It then passes that token onto the
- 16:46
seller and the seller unwraps the token.
- 16:49
So, they get the payment credential and
- 16:51
uh any fraud signals and other data the
- 16:55
seller might need.
- 16:57
Then the seller passes that onto the
- 17:00
payment provider again. So,
- 17:03
the seller also is not getting my card
- 17:06
number. They're passing the token to the
- 17:08
provider and then the provider responds
- 17:11
with either a success or failure
- 17:13
message, of course, depending on
- 17:16
uh
- 17:17
if I have the right funds, if the credit
- 17:19
card is valid, and so on.
- 17:22
And finally, the merchant confirms the
- 17:25
order, sends it to the agent that sends
- 17:27
it to me.
- 17:31
So, shared payment tokens are designed
- 17:33
with security in mind, and the payment
- 17:36
provider enforces all of the limits, not
- 17:38
the agent or the merchant. So, if any
- 17:40
guardrail is violated, such as an
- 17:42
expired token or an invalid amount or
- 17:45
current currency, the charge is just
- 17:47
rejected.
- 17:50
Okay, well, I know my agent is using
- 17:53
UCP, so I know it only has access to the
- 17:56
shared payment token. So, I actually
- 17:58
feel pretty good about entering my
- 18:01
credit card number as that's going to
- 18:03
Stripe and not my agent. So, okay.
- 18:06
So, now the agent has everything it
- 18:09
needs to complete my purchase.
- 18:11
And
- 18:13
it once again, asks me if I'm sure. It
- 18:17
confirms with me. I say place my order.
- 18:20
And it comes back with a success
- 18:22
message. And because I chose the express
- 18:25
shipping, I get my headphones the next
- 18:28
day, and they're there in my studio at
- 18:30
home.
- 18:33
So, if you want to learn more about
- 18:35
agent to commerce, uh we've got lots of
- 18:38
videos on the Stripe Developers YouTube
- 18:40
channel.
- 18:42
And uh
- 18:43
ton of blog posts that go into even more
- 18:45
detail uh on stripe.dev, and I'll be
- 18:49
right outside to answer any questions.
- 18:51
Thank you.
- 19:08
>> [music]