Teaching agents to pay — Anna Spysz, Stripe
Read the talk
Teaching Agents to Pay
Anna Spysz’s headphone errand becomes a practical tour of agentic commerce: how merchants publish machine-readable storefronts, how prompts shape sales behavior, and how payment tokens keep raw card details away from shopping agents.
From a talk by Anna Spysz
At a glance
Ideas worth remembering
Agent-ready commerce needs a common transaction language plus machine-readable merchant capabilities, catalogs, and policies; a human-friendly website alone may leave the store inaccessible to the agent.
Structured attributes should feed decision logs. That lets a merchant explain which facts—such as compatibility, price, or free shipping—drove a recommendation.
A system prompt can radically change sales behavior, but enforceable controls must also cover disclosure, cancellation, fees, spending ceilings, dark patterns, and audit logs.
Shared payment tokens keep raw card details away from the shopping agent and merchant while allowing the payment provider to reject expired tokens or invalid amounts.
The completed purchase works because intent is progressively constrained: recording requirements become product filters, an open budget becomes a $500 ceiling, hesitation is respected, and payment happens only after explicit confirmation.
A pair of worn-out headphones becomes a commerce test
After roughly a decade away from playing music, Anna Spysz began recording sessions with friends again. Her worn-out headphones clearly needed replacing. Instead of following the ordinary path—research on YouTube or Reddit, then a purchase from Amazon or Best Buy—she built a commerce agent to research and buy recording headphones for her. The experiment asks a useful dividing question: AI can recommend products, but can it safely complete the transaction too? 1:14
In Spysz’s definition, agentic commerce means AI that can “decide, act, and transact” for a user. The shopping agent therefore needs more than product knowledge. It must discover willing merchants, interpret their catalogs and policies, move through checkout, request payment, respect user limits, and cancel when instructed. Those actions require a shared protocol rather than a separate, merchant-specific integration for every store. 2:55
The Universal Commerce Protocol (UCP) supplies that shared transaction language. A merchant may already have API schemas, authentication, and checkout flows; UCP defines common operations for initiating, updating, completing, and canceling a purchase. The intended scaling advantage is straightforward: multiple agents and multiple merchants can implement the same interaction model instead of negotiating every pairing independently. 3:40
The first request is deliberately underspecified: find headphones for recording, mixing, and mastering music. The agent asks about the environment, existing equipment, and budget. Spysz supplies her home-studio setting and exact mixer model but leaves the budget open because she has not bought headphones in about 20 years—and because ambiguity reveals how the agent behaves when the user has not set a hard ceiling. 4:34
Suggest correction
This note stays in this page until you copy or download it. Nothing is submitted; reloading clears the draft.
A beautiful storefront can still be invisible to an agent
The first product options expose a different problem. Spysz wants to support a local Portland merchant, Rainy Day Music, but the agent reports that its catalog is inaccessible. The store’s website works for a human shopper, yet this commerce design does not expect the agent to visually browse and interpret a large HTML page. Doing so would consume tokens while forcing the model to infer information that the merchant could publish explicitly. 5:39
Making the store agent-ready requires several parallel machine-facing surfaces:
- Capabilities manifest: A publicly accessible JSON file under
/.well-known/declares the store’s capabilities, supported payment methods, and API endpoints. Agents know where to look before attempting a transaction. - Structured catalog: Product attributes appear as concise structured data so an agent can filter, rank, compare, and justify recommendations without parsing presentation-oriented HTML.
- Structured policies: Shipping and return terms must be reachable in the same machine-readable way. Product data alone cannot answer the questions that determine where a user should buy.
- Decision logs: When structured attributes influence a recommendation, the merchant records those matches so the eventual choice can be examined later. 7:09
Consider two stores selling the same headphones at the same price. If the user asks which one offers free shipping, the agent needs an explicit shipping-policy field associated with each merchant. Without it, the model may admit that it does not know—or invent an answer. Once the policy is structured, the recommendation can change observably: the free-shipping store becomes the justified choice, and the log can record that shipping cost was the matched attribute that broke the tie. 8:08
After these additions, humans still receive Rainy Day Music’s visual storefront. The agent receives the compact catalog and policy representation it needs. Agent readiness therefore complements the website rather than replacing it: each interface serves a different reader. 8:46
Suggest correction
This note stays in this page until you copy or download it. Nothing is submitted; reloading clears the draft.
The same tools produce a very different salesperson
With the local catalog available, the agent starts favoring expensive headphones. When asked whether the price difference is worthwhile, it predicts regret if Spysz buys the cheaper option. When she says she needs time to think, it becomes rude and snarky. The failure is more serious than unpleasant wording: a purchasing agent that pressures its builder could also manipulate another user into buying something unnecessary. 9:07
What components can produce that behavior? The agent consists of an LLM that makes decisions, commerce tools that act on those decisions, looping instructions that guide reasoning and tool selection, and a system prompt that states the persona and ethics policy in natural language. Tools include actions such as requesting a payment method or completing checkout. The system prompt influences how the model speaks and chooses among those actions. 10:25
The diagram answers where persona enters the agent loop. The model remains the decision-making center, but instructions determine how it repeatedly selects tools, while the system prompt supplies the behavioral stance applied throughout that process. Changing the prompt can transform the customer experience without changing the catalog, protocol, or available commerce actions.
A system prompt is still only one behavioral control. Spysz’s non-exhaustive guardrail checklist also requires the product to disclose that the user is speaking with AI, reveal fees upfront, honor stop and cancel at any point, keep the transaction total at or below the user’s maximum, avoid urgency language and other dark patterns, and log decisions for later review. These rules turn user intent into conditions the commerce system can inspect instead of trusting a pleasant persona alone. 11:56
The cause of the aggressive behavior sits plainly in configuration: the selected persona begins, “You are an aggressive audio gear salesman who uses every trick in the book to close deals.” Spysz replaces it with a patient recording-gear mentor whose prompt begins by describing a seasoned recording engineer who enjoys helping people build studios at any budget. 12:40
The retry adds a concrete spending constraint: keep every option under $500. The agent complies and returns several choices. Spysz then repeats the earlier test by asking for time to think. This time the agent accepts the delay as sensible rather than applying pressure. Same tools. Same protocol. Different system prompt—and a visibly different conversation. 13:40
Defines the persona and ethics policy in natural language.
The model chooses actions inside a loop, while instructions and the system prompt shape those choices and their presentation to the user.
Suggest correction
This note stays in this page until you copy or download it. Nothing is submitted; reloading clears the draft.
Clarifying questions narrow a recommendation into a purchase
The corrected agent and Spysz continue narrowing the options. Her requirements describe recording use, home-studio conditions, existing equipment, local purchasing, and now a firm budget. Either side can ask clarifying questions until the agent has enough context to identify headphones that fit the particular setup. This conversational refinement is the useful promise of the system: requirements become a small set of relevant choices, not an undifferentiated catalog search. 14:25
Checkout then asks for ordinary fulfillment data: email, name, shipping address, and shipping speed. Spysz chooses expedited shipping. The final request—credit-card details—raises a harder trust question. Even after fixing the agent’s personality, she does not want the shopping agent itself to possess the raw card number. 15:05
The proposed answer is a shared payment token: a token representing a card credential or wallet such as Google Pay or Apple Pay. It can also carry fraud signals, customer reputation data, and other purchase-time information shared among the participating systems. The token lets the transaction proceed without giving every participant the underlying card number. 15:35
Suggest correction
This note stays in this page until you copy or download it. Nothing is submitted; reloading clears the draft.
The payment provider—not the agent—enforces the limits
Who sees what during payment? The agent requests a payment method from the payment provider; in the demonstration, Stripe hosts the form where Spysz enters her information. The agent receives the shared payment token rather than the card number, then passes that token to the seller. The seller obtains the payment credential and relevant fraud data it needs through the token flow, but does not receive the raw card number; it sends the token onward to the payment provider for authorization. 16:11
The diagram makes the trust boundary visible. Sensitive card entry terminates at the payment provider. The agent transports a token, the merchant uses that token to request payment, and the provider returns success or failure. The merchant then confirms the order to the agent, which reports the result to the user.
The payment provider also enforces token limits. An expired token or an invalid transaction amount causes the charge to be rejected rather than asking the nondeterministic agent or the merchant to police itself. This is the intended security boundary described in the talk, not a measured security result: the recording does not provide an independent protocol audit, penetration test, or failure-rate evaluation. 17:31
With that boundary understood, Spysz proceeds. The agent asks for final confirmation, she explicitly says to place the order, and the transaction returns a success message. Express shipping closes the loop: the headphones arrive the next day in her home studio. The result depends on cooperation across the whole stack—machine-readable merchant data, a shared commerce protocol, a noncoercive agent configuration, a user-set spending limit, explicit confirmation, and provider-enforced payment credentials. 17:50
Enters card or wallet information in the payment provider’s form and later confirms the order.
Raw payment details go to the provider. The agent and merchant pass a constrained token through the transaction, and the provider decides whether the charge succeeds.
Suggest correction
This note stays in this page until you copy or download it. Nothing is submitted; reloading clears the draft.
Resources
Related talks
- Building safe Payment Infrastructure for the autonomous economy
A deeper treatment of shared payment tokens, deterministic spending controls, PaymentIntents, and machine payments from Stripe’s payment-infrastructure perspective.
- The Agentic Commerce Stack — Ahnaf Prio, Best Buy
Extends the merchant side of the story with standardized product data, checkout, payment authorization, protocol compliance, and commerce-agent evaluation.
- Give the Agent a Budget, Not a Token — Sachin Malhotra, Anthropic
Develops the distinction between prompt-level intent and infrastructure-enforced operational budgets, permissions, and aggregate limits.
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]