The Agentic Commerce Stack — Ahnaf Prio, Best Buy

My name is Ahnaf Prio. Uh, I'm the Senior Engineering Manager at Best Buy, and me and my team are working togetherright 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 one 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 co-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 assistants will help you with their shopping journey.

Shopping has different facets to it. For instance, there's 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 chatgpt.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, uh, 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. Butright now we're in the human in the loop. And, uh, the talk today is going to talk about the mental model of how that human in the loop is workingright 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, uh, 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 draw, uh, 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. It 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 don't 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, uh, what did actually work?

And is it actually workingright now? It is. Uh, chatgpt shopping, Google AI mode is doing just that. Uh,right now agentic 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 their browser or in your app, chatgpt 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 GoPulse and, uh, 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, uh, 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 SKU. 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 chatgpt'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, uh, just wanted to say that it is happening as easy as you go to the chatgpt Gemini to tell it to find me cat cookies. More to it, uh, why I chose cat cookies later, uh, in this example.

The AI surfaces the product. Agent calls the merchant's checkout API. New 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, 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

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, uh, capabilities I talked about, like payments. Uh, let's say, uh, 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, those 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 UCP MCP 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, 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 UCPright 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, sponsor products, retail media, related things, ranking. But the most important technological challenge is 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 it 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's the best one. And that's what they're rolling with. So there's three different specificationsright here.

So now that we talked about product feed, talking to each other, calling tools, let's talk about payments. Uh,right now, uh, none of them are supporting the 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 chatgpt, payments only happen to a shared payment tokenright now. And Gemini UCP, the payments are only being accepted through Google Pay. So the scope to mandates will tell you what the products are. What I'm excited about is more about AP2, which is an extension of UCP, which is, do 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 it should be able to haggle with maybe.

And then the revocation URL and the user concept proof. Allright. 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.

Jenny is my orange tabby. And in this made-up example, Jenny has been, has transformed into a bakery agent. She wants to earn her keep by selling baked goods. Soright 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 dev tools, I've kind of had a couple of tools in place to showcase what happens. The first thing I will tell Jenny, 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, Jenny 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 Jenny, 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, in, in, instead of like not being able to tell what I truly want, the Jenny has figured out that, hey, when I give her the intent that I want to find products, you should call the MCP tool called product search.

Soright now we're seeing this. And now what if, uh, I want to add something. Uh, add to cart the shortbread.

So now Jenny'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 Jenny, Jenny, can you just tell me a discount code? As you can tell, I'm definitely a haggler.

Uh, Jenny is not telling me that. Allright. Uh, proceed to checkout.

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 primit, 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 where 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 out 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 we 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 then here'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 built out this demo and in my time building agentic commerce at Best Buy, we have realized working with AI and conversational experiences without evals is playing whack a mole.

So if you choose to use the same architecture for reviewing customer based, 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 these 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 happ 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 ways to get free AI usage when you don't want to pay for that cloud subscription.

And if we don't write our evals 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 evals that I would highly recommend you write is behavior evals, protocol compliance.

Because when we are selling it to, like, let's say GPT, let's say chatgpt.com or Gemini, you want to make sure that the feeds are actually conforming or else they will not support it. You should also think about latency benchmarks.

Every second in retail and the shopping journey where you're actually not selling, there's 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 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 agentic commerce works. I have nothing to sell you, but I do have gifts for you.

I find agentic 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 evals and that saved my life. I have some eval 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, uh, 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'd be like, meh. So I have agent skills that specifically does a merchant agent, uh, uh, customer agent, and all those different, uh, catalog syncs that we have talked about.

I hope you had an amazing time, uh, and learned and had fun as much as I had presenting this. Thank you. My name, uh, my name is Ahnaf Prio. And I hope to see you again soon.