Agentic Sites: Building Hyper Personalized Websites — Carlos Sanchez, Adobe

[music]

>> Hello.

Thank you for coming. Um I'm going to

talk to you about Agility Sites, how we

call it as building hyper-personalized

websites. I'm not going to just talk

about it. I'm going to show you what

we're building.

Um I've been working on on this project

for for a bit now, and

we'll try to show you what is possible

today with with AI.

Uh I work at Adobe at a

I'm a principal scientist at a product

that not many people know, Adobe

Experience Manager, content management.

We run a lot of

uh

website properties for big brands, and

my background is in in open source, uh

contributing to to a lot of foundations

and projects.

What are Agility Sites, and how are we

building this thing?

So, we're looking for sites

that are

uh

looking at the what intent the user

browsing

uh has. What is the user doing? What is

the user trying to achieve? And the end

goal is to personalize these pages for

the for the current user browsing, so

that eventually this

uh drives uh higher engagement or uh

conversions, whatever the marketing

teams want to want to achieve.

And these pages are personalized in real

time based on the on the user that is

uh accessing the site, and what is the

what is the user doing.

The stack we're using is AMH delivery.

So, this is the part of the product we

we have,

uh where all the content is on the edge

and then we have back end service that

powers this experience with

different LLM providers LLM services

we use Cerebras for fast inference or we

can use also we tried bedrock and and a

bunch of others. I'll be showing

Cerebras today

and you will see the reason why.

The

the engine that is personalizing this

this bits is

using the rich content and blocks.

So different blocks on the site are

customized depending on on what the user

persona is.

We don't want the the whole site to be

generated. I mean if you talk to

marketing people they they have a very

strict brand guidelines. You don't want

to just

come up with our have some

hallucinations there. So the what is

personalized is different sections of

the site and we use the whole site as

a corpus. We built a rack from the whole

site. So what is generated is grounded

on on the existing site.

We tried to solve the problem where one

size fits all. We want

hyper-personalized experiences. Also we

want to help

our customers to do more automatic

authoring. So not having to create

thousands of different variations of the

site but use AI for this

and then do these multiple layers of of

personalization.

Some examples of what we're doing or

I'll show in the demo. It's

instant persona adaptation, query

generation when the user search for

something on the site, the page with the

results is customized for them and also

uh, something like recommendations where

after you browse the site for a period

of time, we

we can create a page that recommends

something based on on on what you are

what we think you are looking for.

For marketers, uh, they can define this

strategy on natural language, and they

can use analytics to

to drive the loop of personalization,

and what is the end goal, and how this

goes back again to change to adapt the

personalization to improve that uh,

whole cycle. Everybody's talking about

loops in this conference, so that's

that's one of the loops there.

How the architecture look like? So, it's

a dynamic front end with some blocks,

what I mentioned before, and with uh,

edge delivery services is basically you

compose these blocks, and uh, they are

updated on on real time through with the

AI.

The back end,

uh, we

we do the um,

evaluation of the models and the

providers,

and one thing we realized is is that

this is very dependent on the site. So,

we have a bunch of prompts, and we look

uh, we run it across a huge variety of

uh, models and providers, and then we

look at the accuracy, we look at the

speed, but this is going to depend

highly on what type of site, like how

big is the site, how I don't know, what

different

what different um,

area is the site targeting, what what

type of commerce it is, and so on. So,

we we run this this

um, evaluation continuously. We use uh,

Promptfoo. Uh, anybody heard about

Promptfoo?

Okay, some people.

So, Promptfoo allows you to evaluate

models um, prompts against my multiple

models, providers, and you can do local

models and any of the

a bunch of open AI compatible

uh providers and and uh

a lot of them, basically.

We look for two things. Why? Accuracy.

That's that's typically what people look

for, but also we want the speed because

we don't want the site generation to

take

more than 1 or 2 seconds,

right? Because people uh

this is already

uh proven that people want the the

faster the site, the more conversions it

it generates or the the better the

experience it is for the user.

Um yeah, what I mentioned is different

sites may have different requirements.

Uh so, you may have to run this uh

evaluation of models depending on the

site.

This is a an ex

uh we we secured this

some of these queries, so we have a 15

prompts for this example site.

Um we have uh

at the top you can see with Cerebras on

the Gemma 4 model that was announced

last last week,

we can get an average latency of 1.1

seconds generating a page.

You you can compare that to the second

one, which is 4.6 seconds, right? So,

the difference is huge.

And that's why uh we use Cerebras for

for this use case.

And uh

you can see that different providers,

different models have different

um

different speeds. And here is uh let me

I can show you the whole

thing here. Not this one, this one,

right? So, at the at the bottom we have

other other tasks.

Sometimes uh maybe some of them may be

good.

They don't need to be perfect, but

they're good enough if they're fast

enough. So, that's going to be the the

kind of

decisions that you need to make on

whether the model is good enough for

your use case or not.

Yeah, we're looking Yeah, average 1.1

seconds. And then the the next ones are

going from 4 seconds higher.

And you don't need a huge LLM to do this

sort of work because you are generating

text, you are deciding where to put

blocks and how to organize the website,

you don't need a lots of information for

that.

So, this browsing and the queries

uh is are being recorded. So, these are

the metrics or the

the the data we gather from the user,

and this is fed into the LLM to

personalize the site. And then in this

example, we personalize the hero card,

the products, the blog feeds, and and

the navigation based based on the

persona.

Also, what are some of the buttons like

our call to action navigation, you can

also we can also personalize those.

We

we create and I'll show you the a for

you page, which is a recommendation.

And this is a interesting one because

this you could

pre-generate, right? As the user browses

your site, you gather these signals, and

you could keep generating in this. So,

in this case, you wouldn't need so such

a big speed.

But

but that's interesting because it it

would be if a user wanted to

buy something, you could just say,

"Okay, for you, I will recommend these

three products or or something like

that."

Um

Yeah, and then they can see this

recommendation, and if they go there,

that that could be pre-fetched for them.

And obviously, you have to keep updating

it as the user navigates around the site

and and so on. So that that's also

something to consider on the cost cost

implications of doing multiple

generations, multiple LLM calls.

When

when the user runs a query, dynamic

personalized page is shown to them.

When the

these queries are also grouped into

personas or intent types. So what is

this guy what is this guy trying to do

in the site? Is trying to buy something?

Is trying to just get information? So

you can get marketers to decide what

type of groups, how many groups you want

to have, how you want to deal with with

customers. And the AI will choose the

the blocks and the suggestions for for

those groups of people.

Um

And we can adopt yes, the the different

blocks, the the the sequence of the

blocks and

media. You could also do media. One of

the things we consider is

there was some a model announced

today or yesterday the

the nano banana light. So you could even

generate images

very fast on the fly.

Obviously not as fast as text, but

that's also something that would be

I don't I don't know if it's that

something like marketing people would

want to have generated images. That

depends on on the quality a lot if it's

on brand.

And

the site in this example we have a a

product site

and then we have guides, experiences,

blocks and the whole response of the LLM

is grounded there. And

there's comparisons. We can do

comparisons between products that are

tailor and the product pages can be

tailored for the for the user.

Okay, this is this is a bit of the

stack.

Um not going to spend too much time

here, but the browser you have some

layers. You have the browser where the

signals get

get

uh

from I got I got I got so

I got from the from the user and then we

have the back end. Uh

we can have the back end. We run this

some of these in in Google. Some of

these are in our Cloudflare. So, the

back end is basically just calling the

LLM and doing some reasoning using the

rack that is built on on the site to do

the generation.

And you have obviously you have to have

the vector database, the inference uh

machinery and uh

that obvious business manager is doing

the serving the

the

at the edge is serving the the pages and

the static content.

So, let me show you because I think this

is uh so, we call this uh audience of

one

because the idea of in marketing

they they always dream on being able to

personalize things for each individual.

So, we call it yeah audience of one. So,

I have this this site. Uh this is a site

that is absolutely generated

uh example site. It's a coffee

uh machinery. So, I can go and and read

some stories

and I can go and look at some products.

Let's go and look at this product.

I can spend some time here.

Uh

let's go and click

here.

Okay, so I'm I'm browsing around the

site and I have this debugging tool

thing uh which

Uh

let me go here, I think.

Let's see.

So, down there is the signals that the

that the browsing

is giving us. So, I don't know if you

can see it much because I cannot see it

much. The So,

the user is bucketed into the exploring

category. We have the pages that have

have visited, and then we have how much

time is spending on each page. All of

this data is now available for the LLM.

So,

if I go here, I already have a for you

page that was generated for me

and

based on my browser.

And you will not notice that it's

slightly different than everything else,

but if I go here and I run a query

like I want I'm looking for a coffee

machine to

uh prepare

coffee

while camping.

The site is this was just generated for

me.

And then you're going to see some things

like the text is customized. Camping

shouldn't mean compromising on your

uh whatever routine.

Uh the coffee tips for camping um

machinery that are being recommended are

coffee agile and um or the nano, which

are

good for

for the

for a camping trip, right?

So, you saw how fast this was.

I'm going to run it here something

similar that I had here and I can run it

on the debug mode here.

And you will see, let's make this

bigger.

Total time 164 seconds to generate the

page. So, this includes a round trip to

the LLM. This is using Cerebras Gemma 4.

So, the the Gemma model from Google

running on Cerebras on their

very fast chips.

Uh we get

2,300

tokens per second.

Which is not bad.

I would say.

>> [snorts]

>> And if I run it again, uh probably

something like that.

Uh the LLM time is 1 second. And again,

2,200 tokens per second.

This is something that we only dreamed

about before.

On the on this site example site, we

have some other options.

Uh

so, because we we've been showing this

to customers, so we have the

the ability to change the different

models, temper temperature, tokens, and

so on. And we can uh

we can

show uh and try the different models and

see how they behave. Besides the

automatic test with Prompt Full, then we

can uh manually come and and click

things and see and see how that how that

works.

And uh

we also have

uh OfOneLabs.

So, we have we build this tool that

generates an agentic site for any site

we want. So, if somebody wants to have a

demo for a customer, come here

and enter the URL. In less than an hour,

you have an agentic site. I did this

last week with the AI engineering site.

And

I got this site that is just a search

box and a few things.

And

let me open it here, the full page. Not

this one. Yeah, okay.

So, I could say

Europe AI conferences.

So, these suggestions are also AI

generated. And I get a page that is

more focused on

It should be more focused on on the on

this European conferences.

If I go back, did I go

I can search for anything the same way I

did with with the Arco. So, I as a

specific There was someone that was

generating a good comparison side to

side.

Let me see if this one. Okay, here. This

one.

I went and this generated a page with

a pretty good comparison. If I'm looking

at two conferences and I need to decide,

if I figure out that the user wants to

do that, this is great because that

gives them a side-by-side comparison on

the fly.

Now,

this this is I think this is cool

already, but then we have I have this

idea that

probably the I'm

a bunch of people are we are talking

about is the web that is is the web the

future still and so on. Nobody knows.

But we can also do something with this

with this audience of one, this

generative sites. So, imagine you have

you have your personal assistant and you

ask a query through in this case through

Google and you say I want to buy I don't

remember what the query said. It was

something like I want to buy a machine

and I get this on my Google TV.

Right? So, this is absolutely

personalized to my query.

Okay? No, go back.

This is absolutely personalized to my

query. So, I'm there in my living room.

I don't need a phone, I don't need a

computer, I don't need anything, just my

voice and something that will

kind of show me

something that is absolutely

personalized to to me.

Okay, so that one.

So,

what I was trying to show and hopefully

you remember from this session is that

this is now possible.

It's only going to get better from here

on. It's only going to get cheaper, it's

only going to get faster.

And you will uh be able to have uh

huge personalization options for sites

and for other things.

And you can do this with intent driven.

So, what is the what is my user trying

to do? What does my user want to buy?

These sort of questions. And you can uh

assemble a page just for them.

And you can also do this with uh

multiple models and and eventually

it's just going to be

faster and faster, right?

So,

that's it. Um

thank you for coming and I hope you you

got the idea. Thanks.

>> [applause]