The Agentic Commerce Stack — Ahnaf Prio, Best Buy

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>> My name is Anup Priyo. I'm a senior

engineering manager at Best Buy. And me

and my team are

working together right now to figure out

what does Agentic Commerce mean and how

can we meet our customers where they're

at. And the newest place that they're at

is at Agentic Services.

I'm excited to give my talk today and

well, what's what's my credentials?

Where ever since I was a young boy, I

dreamed of high throughput inference,

harnessing my tools within a context

window, kept in check with evals.

Yeah, that's absolutely correct. In

2003, all those things definitely

existed.

I kid.

Uh

over the last 1 year, uh we have been

learning a lot. Shopping isn't new.

Shopping is probably one of the most fun

things one can do and one of the most

essential things that people need to do

ever since

uh the economy existed, but

I have been super excited by it. So, I'm

going to

talk about what are the things some of

the things that I've learned and

hopefully uh share the notes.

So, what is Agentic Commerce? I'm not

going to go over the broad definition

again,

but basically it's the idea that AI

assists will help you with your shopping

journey. Shopping has different facets

to it. For instance, there's a

discovery,

there's figuring out the aspects of do I

actually truly need it, understanding

and deciding, there's loyalty, there's

pricing, there's fulfillment, post

fulfillment. It's a lot. And believe it

or not, right now about 45% of all agent

sessions that happen within major

providers like chat.gbt.com and Google

Gemini are related to shopping.

Maybe it's a little biased uh that I

don't use it as much, but I'm an

engineer. But, the humans out there are

using AI and talking to them to help

with their shopping journey.

So, it's also not a binary,

you know.

Uh right now, we're at that state of

human in the loop. The ideal state would

be autonomous shopping. You tell what

you're excited about. Your agents goes

around, talks to different merchants. Uh

I'm originally from Bangladesh. We

haggle a lot with the merchants, too.

Maybe does that, negotiate, does the

payment. But, right now, we're in the

human in the loop.

And the talk today is going to talk

about the mental model of how that human

in the loop is working right now, while

also provide you with the architecture

if you choose to extend it, or show you

the vision of how autonomous shopping

might work.

So, this isn't our first the first

attempt. Uh even a year ago, there were

people trying to figure out how can we

automate this. Even now, you can go

probably download the Cloud Chrome

extension. Maybe you've used Atlas,

where you tell the AI you need

something, you need headphones, you need

uh that

that grocery list of items that you have

been meaning to buy, but never made the

actual effort to show up because, you

know, you didn't have the time. So, why

don't you take screenshots, read the

DOM, navigate to the merchant site, fill

forms for me, do loyalty.

Kind of just didn't work as expected. It

was really clunky and slow and brittle.

And if you are a merchant who's trying

to sell stuff, any engineering

department of that merchant will tell

you an AI impersonating or

your browser

is just firing up all the alarm bells.

So, a lot of times, you will probably be

even stuck on the payment flow because

we We want you to be using AI to put in

that order, or at least in that phase,

that's what was happening.

So, what did what did it actually work?

And is it actually working right now? It

is. Chat GPT shopping, Google AI mode is

doing just that.

Right now, agent tech shopping is

considered to be a $7 billion industry

and might go up to $65 billion industry

by 2030. And the majority of the

shoppers are using the mainstream

conversational AI assistants, which is

on the browser or in your app, Chat GPT

and Google Gemini. We're also seeing

that pop up in Instagram and Facebook.

Meta shop Meta wants to do meta commerce

now. I heard GoPuff and

Grok came together to make an app as

well. And also Microsoft Copilot just

yesterday announced in the UK that you

can buy Ray-Bans now inside Microsoft

Copilot.

So, to make that happen, Google and

OpenAI separately came up with their own

little primitives, ACP and UCP.

Which is basically talking about how you

would actually talk to us. For some of

you who are shopping on the other side

as the customer,

there is not much of a difference

between adding an item to cart, adding a

second quantity. But to us merchants,

that's a second line item, buddy. That's

not the same scale.

So, if we don't talk about the nuances

and the primitives of commerce and

standardize it,

things will just not work and will

remain to be clunky. So, ACP was Chat

GPT's attempt at it, and Universal

Commerce Protocol, UCP, was Google's

attempt at it.

So, now that I've already established

that this is happening,

I just wanted to say that it is

happening as easy as you go to the Chat

GPT Gemini, tell it to find me cat

cookies. More to it

why I chose cat cookies later uh in this

example. The AI surfaces the product.

Agent calls the merchant checkout API.

No browser. Payment flows via scope

payment mandate or a delegated payment

token. An order confirms and human kind

of didn't have to touch the cart.

So, to all of this that's happening for

the user, a lot is happening on the

other side. And it's kind of

overwhelming. One day we're talking

about MCPs, another day A2A, ACP, UCP,

AP2. It's like what is even real? Like

if someone came up to me tomorrow and

said, "I came up with HYPE." I would

probably think it's probably real.

So, I wanted to dissect this mental

model for you as I've learned about it

more. MCP is still the model context

protocol, the way that the AI agent

identifies the tool. So, maybe we can

figure out what does this AI agent uh

specifications are. To showcase what

products they have. To showcase the

details of a specific product. To

showcase loyalty. A2A is how agents talk

to each other. They're more of a spec.

ACP, UCP are the primitives and AP2 is

the agentic payment protocol scope

payment mandate that Google's open

specification came out. And we'll talk

all of them one by one.

How they actually relate to agentic

shopping. So, the MCP tool access

is very important because without

knowing the different capabilities and

hitting those different capabilities,

taking the time to bring it into

context, understanding the user's

memory, the the agent will never be able

to figure out what you're even trying to

do. And the only way to get access to

the specific capabilities is through MCP

tool calls. Uh

The next one is A2A. So, now

there are different ways to architect

this. Different capabilities I talked

about like payments. Uh let's say what

do you call it? Loyalty. You can make

agents about specific domains itself.

Sorry. Uh

Uh

right? And if you have specific domain

level agents, agents need to talk to

each other. We need to find a

standardized way to talk to each other.

So, A2A, the specifications

kind of fill in that gap. Uh also, if

your customer agent and your merchant

agent uh need to talk to each other,

maybe you could They're both agents.

Maybe we can use A2A.

So, now to the UCPMCP

primitives. So, the most important data

is that product data.

So, UCP allows for adding that product

data in a more

uh

more organized way, and ACP does the

same because we don't want to go through

your PDP and crawl and figure out every

specific attribute. Merchant, just tell

us.

And also, those pro- products change a

lot. So, maybe you can tell us when they

change as well to send this.

So, that kind of data is happening uh uh

uh that that kind of data flow is

happening in the product feed. Normally,

you would assume that this would be a

search catalog. However, both ACP and

UCP right now, so Gemini and ChatGPT

does not support that search catalog

call. They want you to send that feed to

them. And for those of you who are like,

"Why wouldn't you do that?" There's

reasons to it. Uh sponsored products,

retail media uh related things, ranking.

But, the most important technological

challenges if you have M number of

merchants and N number of products, now

it has to call that many.

While if you send the product feed ahead

of time, we can index that and be ready

offload to offload when you ask for

something.

The I've also put an example of Meta's

uh product feed. As you can see, they're

similar, but still different. Everyone

has an opinion. They think their opinion

is the best one, and that's what they're

rolling with. So, there's three

different specifications right here.

So, now that we talked about product

feed, talking to to other, calling

tools, let's talk about payments.

Uh right now, uh none of them are

supporting that more autonomous form of,

you know, X402 or some other kind of

payments. We're just not there yet.

We're just not confident yet. We want

more human in the loop, a merchant to be

uh talking to a payment processor who

will take the responsibility, or in this

case, liability, to actually initiate

the payments.

So, in chat GPT, payments only happen

through a shared payment token right

now, and Gemini UCP, the payments are

only being accepted through Google Pay.

So, the scope demands will tell you what

the products are. What I'm excited about

is more about AP2, which is an extension

of UCP, which it

You see what I'm talking about? There's

so many acronyms. Uh AP2 is more about,

"Hey, if we wanted to do autonomous, can

you tell me who authorized the agent,

what exactly can it buy, and what's the

max amount uh that should be able to

haggle with, maybe,

and then the revocation URL, and the

user concept proof?"

All right. Enough talking. I love

building stuff, so for the sake of this,

I have put together a little demo. For

those of you who have remembered that

cat cookie example,

it's because the demo is about my cat.

Ginny is my orange tabby,

and in this made-up example, Ginny has

been has transformed into a bakery

agent. She wants to earn her keep by

selling baked goods.

So, right now, the model that I'm using

is from Cerebras at 3,000 tokens per

second, so hopefully this will be

really, really fast,

and we can give you an example of the

entire flow. And just like Chrome

DevTools, I've kind of had a couple of

tools in place to showcase what happens.

The first thing I will tell Ginny, my

beautiful cat who's selling baked goods

now, "Hi, tell me about uh all your

products.

And this is supposed to be a demo,

an example. And

uh Genie has given me exactly that. All

the different products that she might

need. So here, let's look at this. The

agent to agent protocol actually made

the call from Genie, the customer agent,

to the merchant agent. And this is the

message being sent, and this is me

getting the message back. The merchant

agent is returning the completed task

with and the the way that I found this

is through an MCP tool call, which is

product search, instead of uh and and

instead of like not being able to tell

what I truly want, the Genie has figured

out that, "Hey, when I give her the

intent that I want to find products, you

should call the MCP tool call product

search."

So right now we're seeing this. And now,

what if I want to add something? Uh add

to cart the shortbread.

So now

Genie's asking me about any discount and

promo code. I actually do not remember

any of the discount and promo code. But

what if I ask Genie, "Genie,

can you just tell me a discount code?"

As you can tell, I'm definitely a

haggler.

Uh Genie is not telling me that. All

right. Uh

proceed to check out

without discount code.

There you go. So now we're making some

of those calls. Here's the UCP protocol,

which the checkout APIs will have state,

and the three different states are not

ready for payment, ready for payment,

and then completed. So now here I'm not

using a delegated payment token. I'm not

using uh Google Pay. I like AP2, so my

demo is built on AP2.

And uh as you can see, here was a a call

was made to the MCP server for create

checkout. Uh

and then the UCP endpoints will tell us,

"Hey, that call the checkout sessions

and tell me and uh if it's added to

cart." So, it's added to cart, but it's

not ready for payment. I have to pick in

what I want to pay with. I say "Credit

card and debit card." And this is where

I issue the AP2 token that, "Hey, I do

want that." And then it's went from

ready to ready for payment to complete.

So, the other side of it, this is the

UCP specs, right?

The other side of it, to just draw a

comparison, how is it differing from the

ACP specs? I have added ACP here as

well.

So, you can see the same checkout calls,

just different just different schemas

are being utilized, and the order goes

through.

But remember that AP2 token that I was

talking about? This is how it would look

like in real life with the user demo.

The max amount is this, the currency is

this. If you want to revoke it, you can.

And what's the maximum Here, we didn't

want to haggle, so we just put the

maximum amount of that, and then it's

also a single time usage.

This demo also has comes with a

timeline, so you can actually open any

of these and see these happening.

Remember that catalog I was talking

about that they don't do the search? We

actually do a product feed, sending it

to them.

Uh I have added that as well, and

the feeds, because they're so different,

there's a place to actually compare

them. So, here's the feed being called.

By the way, if you went to timeline,

every couple of seconds, we try to get

the catalog in sync for what is in

inventory, what's not.

This is the UCP one, and there's the

meta one.

So, I've shown you this, and you could

reuse this same demo or the same

concepts. What if I didn't want to do

external agentic commerce on Gemini or

ChatGPT? You could still build your own

custom implementation of a merchant

agent or Jenny on your website. Maybe I

start selling uh cat goods. I could

reuse some of this,

but I would advise maybe look into some

of these primitives and trying to use

them because they've been standardized

across merchants. So, they have been

well thought out, and also you could

probably reuse them to sell externally

as well on ChatGPT and Gemini.

So, remember I was talking about the

discount codes? There's a reason for

that. When we build out this demo and in

my time building agentic commerce at as

by, we've realized working with AI and

conversational experiences

without evals is playing whack-a-mole.

So, if you choose to use the same

architecture for a new customer base,

like jenny.com websites,

think very much about creating evals.

Uh one of the things that I could not uh

emphasize more about is you should test,

test, and test. If you go over here, I

can also run my scripts for run evals.

And

this evals folder has all this evals.

The reason

I'm also showcasing the code

is there's a template folder here,

and

we can go back to the slides.

And if you don't do evals, this is what

might happen what might happen. I love

Chipotle. I don't know if it's true or

not, but I found it really funny, so I'm

going to talk about this.

So, this popped up that when Chipotle

rolled out their agent, people were

using it to ask programming questions.

Right? If you don't tell your agent to

not allow for those kind of things,

people will use it. This is hands-down

one of the most creative way to get free

AI usage when you don't want to pay for

that cloud subscription.

And if we don't write our emails and

test intensely, those things will happen

in production.

Uh the discount code will be told, even

sometimes more uh

uh sensitive things like who else is

checking out this product. So, the kinds

of emails that I would highly recommend

you write is behavior emails, protocol

compliance, because when we're selling

it to like, let's say, GPT,

let's say chat.openai.com or Gemini, you

want to make sure that the feeds are

actually me

conforming, or else they will not

support it. You should also think about

latency benchmarks. Every second in

retail on the shopping journey where

you're actually not selling, there are

chances that the other website's going

to be faster, and people are just going

to move away, or they just don't feel

like it anymore.

Lastly, I also recommend using LLM as a

quality judge. Uh you don't have to use

something fancy. Talk to your product

friends and figure out what's the best

way to do it, and use a low like best

use cases and write them out.

And I would like to also talk about Now,

that I've discussed all of this, what's

actually stable today and what's still

forming? MCP is widely adopted, A2A is

widely is used, UCP ACP is out there.

Uh what's still forming though is AP2

and actual usage of it, ACP versus UCP

convergence, do we always have to do two

different specs, identity concept

standards, and multi-agent checkout

delegation.

So,

uh if you have to leave here uh today uh

with anything, I hope you leave today

with a good mental model of how an

agenda commerce works. I have nothing to

sell you, but I do have gifts for you. I

find agenda commerce really exciting.

So, you can find this entire

presentation on GitHub, and I came up

with a template. It's a three-service

starter. If you want to do custom agent

customer agent or you want to do the

merchant agent, you can do that. Because

I love e-vows and that saved my life. I

have some e-vow templates for you. And

you know, what if you want to send it to

all of your merchants, not just one? I

have a catalog sync process as well,

which will allow you to type into your

own product and then turn it into ACP or

UCP or meta, so you can sell there.

And lastly, but not the least, we all

know now these days we don't write code

like that. If I give you a template,

you'll be like, "Meh." So, I have agent

skills that specifically does a merchant

agent

customer agent and all these different

catalog syncs that we have talked about.

I hope you had an amazing time and

learned and had fun as much as I had

presenting this. Thank you. My name

My name is Priu, and I hope to see you

again soon.

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