Okay cool.

>> Yeah,

>> then we can uh get started.

>> All right, do we know like are people

on?

>> We are live.

>> We are live.

>> Thanks everyone. Thanks everyone that is

live uh for taking some time today to

hear a little bit more about you know

data stacks IBM's go to market and and

really techd um as a quick introduction

uh my name is Brian Searing. I'm with

technology dynamics. We're an IBM

partner that focuses on reselling and

implementing IBM's data data solutions.

Um data stacks is a big part of our go

to market just where it fits into the

overall portfolio and how it aligns with

a lot of where our customers are trying

to go. So today we are going to kind of

focus on that double click on the

technology how does it fit um answer any

questions and you know kind of kind of

go from there. Um with us we have uh Gil

from the IBM team who I think was

formerly part of data stacks and we'll

really kind of go through again like the

go to market why should you care where

does it align um and then we're here to

answer questions so feel free to reach

out via chat raise your hand and we can

um try and get those all answered or if

you'd prefer to have a conversation

after the fact we're certainly happy to

get something scheduled. So um as I know

we're quickly scrolling through some of

those although we really do focus from a

techd standpoint across IBM's portfolio

um our goal is to align with you know

kind of organizations key objectives as

it relates to data um how are they

trying to utilize how are they ensuring

the accuracy how are they categorizing

it um and as we go to market with that

it again it's completely aligned with

kind of IBM strategy and so um as they

bring on new acquisitions like data

stacks we try and kind get caught up to

speed and make sure we're we're up to

speed on the the latest and greatest.

So, um, as we go through here, again,

feel free to ask questions, let us know

how we can help, and and after the fact,

happy to jump on a call and and kind of

further the conversation.

Uh, with that, Gil, I don't know if you

wanted to quickly introduce yourself

and, you know, take it from there.

>> Yes. And thank you to TechD,

Brian, and company for sponsoring us.

Uh, it's pretty exciting to be here. Uh

you could see I'm wearing my I don't

know can they see my video in this? I I

don't know. I'm wearing my legacy uh

data stacks gear hacker gear.

>> We can see your video but you might have

to the camera has to be a little bit

There it is. We got you.

>> I don't my camera has some weird AI

tracking. I don't want to go into it

because I'm sharing the screen where the

thingy is but uh but yeah you know this

is an exciting part as you know data

stacks uh you know leading uh player on

the in the database field on you know

especially when it comes to Cassandra

and lately around Gen AI we were small

and scrappy and now we're getting uh to

work with you know more and more players

like techd and of course across the IBM

landscape. So I am again legacy data

stack solution architect have worked on

just about every technology out there uh

in my time at data stacks. Uh it's been

just incredibly impressive what we've

been able to do again as a small and

scrappy uh player. Customers love our

solution when they get on it. they just

generally seem to only grow because of

the power of the

data stack solution, the core database,

the many different ways that you can use

data stacks, which I'll talk about, and

then in the last year and a half or so,

the splash we've made in Genai space

with Langflow. And so, uh, you know,

this is a bit of a tech talk, but I am

going to start out with kind of like the

context of like why are we here? How

does this all fit in to the kind of

challenges that we're seeing in the

market today? So, you know, I'll talk

about what the challenges are with

building Genai apps. Uh again the

difference that data saxs is bringing to

the table here is Astra database which

is our platform as a service Cassandra

plus laying flow which is easy drag and

drop um product and open-source

framework for building genai apps a

little bit about customer successes and

then we'll go right into a demo and

you'll see all of this in action.

So this is really the landscape that

we're seeing and you could see you know

we're quoting a few thought leaders here

with this slide. Everybody is

experimenting. There is a lot of

experimentation going on and that's

really great. I mean it's really opening

up people's eyes. It's kind of blowing

people's minds as far as what can be

done and there are pilot projects that

are being started and really kind of

getting everybody's expectations there.

The challenge though is how many of

these can really scale to production. I

mean some of these are really fun kind

of toy projects. Not to minimize the

work that's being done, but to really

get them to enterprise strength for them

to really work at an enterprise scale to

be compliant with the regulatory uh

limitations

and um and uh um really be secure is

something that is really a gating factor

for projects in order to get to

production. And you know the sort of

like TLDDR on this we certainly are

seeing with our customers for them to

get to production. These have been

differentiators. Having a cloudcale

Cassandra compliant database where you

can very easily put your data and you

know it's secure and you know you can

scale is a differentiator for people and

having something like Langflow which

just makes it easy to build Genai apps.

We have been able to help many customers

take their pilot projects out of that

early percentage and get them into

production. So like if you took nothing

away today, go to these links. You can

get started for free with all of this.

We have very generous free tiers here.

Plus Langflow is actually an open-source

free framework. But let's just kind of

remind everybody if you've heard of

Cassandra, if it's something you've been

around the database space and you're

wondering, you know, what is this thing?

This is just kind of a reminder to

everybody. You may or may not know this,

that everything you do in today's

internet scale world probably comes back

to a Cassandra database in some way. And

this what I'm showing here this is not

data saxs customers this is large

internet scale businesses who have taken

an open-source framework and run it at a

massive global scale. So all of these

businesses at their core they are using

Cassandra

and you know there's a few numbers here

about just how widely used it is in the

market but it is an extremely powerful

database and again it powers our world.

>> Hey Gil and would you would you still

define data stacks as an open-source

company?

>> Absolutely. Great question. Thank you.

So we started when Cassandra started. So

little bit of internet history. Facebook

actually created Cassandra to support

their business model and we very quickly

saw that as uh something that people

were responding to but they needed

enterprise support. So we built our

initial business which is a business

based on taking Cassandra packaging it

and running it in your data center or in

your cloud and we were actually a bunch

of you know initial committers in the

Apache Cassandra

uh project and we commercialized it into

certified bundles and we provided much

more quickly uh provided critical

vulnerability fixes and you know the

business kind of grew from there. So

absolutely and in fact you may have seen

recently we just relaunched the New York

Apache Cassandra uh user group and

you're going to see more of these around

the c country and on the west coast I

think it's either Netflix or Uber

sponsors the west coast one but as part

of kind of a new thing in IBM we we uh

piloted the Apache Cassandra one in New

York and also Langflow is open source uh

And I'll just also say something that's

been interesting to me since the

acquisition.

IBM really is an open- source company.

Like if you look at Red Hat and if you

look at Hashi Corp, all of these are

very strong open-source initiatives. And

I I will actually end this up with a

little promo for something we're doing

on Monday in New York, which is a

hands-on open-source Gen AI event. So,

thank you. Great question. And this also

talks to that because our

platform as a service database platform

as a service which can be deployed same

exact product in either AWS, Google

compute or Azure cloud. We run a cloud

on the scale of some of those other

providers. So we actually are dependent

on open source for our platform as a

service. And you can see again here some

numbers and this is actually end of last

year. I know these numbers are even

bigger since then.

So

we're very much all about open source

and scaling open source. And the other

thing about Cassandra, one of the things

that got us a lot of attention is we

helped innovate to create the vector

capability in in Cassandra. But we

differentiate in that when you buy data

stacks in the box you get a database

that not only does vector but it does

core Cassandra SQL which is the query

language for uh Cassandra and the API.

It's also a document database and it's

also a graph database. So uh Forester

has noted us for that. We are the

leaders as far as a hybrid NoSQL vector

database. And you need to think about

this whole thing of what makes your

Genai project successful.

Very often people are really focused on

well I just need a jet vector database

but the very next thing they need is

some other kind of database and we

provide all of these capabilities. So

for the same dollar you're spending to

get your vector database, you will also

get all of these other capabilities.

And then also just to kind of drive home

the myriad ways you can use data stacks

uh te database technology and again our

onremise version is the data stacks

enterprise. The cloud version is the

data stacks Astra database.

The uh newest onremise version is

something called the hypercon converge

database HCD which runs in a Kubernetes

cluster runs in a Redshift uh open shift

cluster um and it supports all of these

APIs. And so really wherever you need to

run this NoSQL database and whatever

type of API you need to support your

database workload, we will support it on

premise or across the cloud.

Now we have been just been bought by

IBM. So that happened the middle of this

year. That deal finalized. If you have

dealt with IBM, uh, this is a business

that sells a lot of products. And so I

wanted to make sure if people are

circling back to talk to techd or other

IBM folks, they know kind of where data

stacks fits because there are various

brands within IBM.

So we sit within the Watson X data

brand. So I'm kind of really

highlighting here for everybody. We are

now one of the engines, one of the query

engines within Watsonx data and that's

how you would buy our product now as

part of IBM.

So let me just talk about a couple of uh

data stacks Astra customers you're

seeing here digital river. So, Digital

River migrated their on premises

Cassandra to Astra database and what

you're seeing here and this is typically

what we see when people move off of on

premises deployments into Astra is their

savings are dramatic. And there's a few

things that go into this. Some of it is

operational, but a lot of it has to do

with just a much more optimal

infrastructure model and moving to the

cloud-based model, not having to

pre-provision things, really only

running kind of what you need at any

given time, being able to scale up

either on demand or in a managed way to

meet your increased um uh business

needs. And so here's an example of a

customer that realized 60%

reduction in their uh total cost of

ownership.

This is a great example that just talks

about the scale and the level to which

that Astra can really integrate in with

diverse environments. And you could see

a fairly involved flow diagram here. You

know this is if we wanted to have a

whole architecture discussion but the

point here being Astra really fitting in

with a very diverse set of enterprise

architecture in order to support this uh

educational app which connects academics

with their uh students. The uh traffic

as they roll this uh app out increased

50 times. two million students, six

million daily active users, and they had

zero downtime.

So again, speaking to the scale and the

ability to really support a very large

diverse um customer and uh enterprise

infrastructure base.

So let's start talking a little bit

about lane flow. So,

and you know, actually, let I just I

just did a big uh no pun intended data

dump on everybody. Let me just take like

a quick pause. Are there other

questions Brian?

>> Not currently. I've been trying to

monitor it.

>> Okay.

>> Relatively closely.

>> Okay. Uh

great.

>> If anyone has questions, feel free to

use the chat just as another reminder.

Yeah, please feel free uh any questions

that come up. Uh okay, great. Uh

different data. Oh, look, I can see the

chat now. On the cloud, security is a

huge concern.

Uh yes. Okay, so as the data stax

enterprise does, Astra offers a

transparent data encryption.

uh also uh the data stax enterprise

product came out of uh operating in very

highly regulated environments. So if you

go to our trust center, you would see

that we support uh a lot uh certainly

most of the major uh security and

compliance frameworks we are compliant

with and we run across the globe and um

we again we provide our own encryption.

So, you can use our encryption keys or

you could bring your own key to encrypt

your Astra database if you wanted to do

it that way. Um, autoscaling. So, yeah,

I mean the database, it's a cloud-based

database. It does autoscale.

If you uh, you know, you get a spike and

the database starts to scale up, it will

scale.

that kind of ondemand scaling is a

slightly higher cost than the kind of

pre-provisioned scaling. We typically do

not see that many customers who suddenly

have a spike in their database. I mean

sure it happens but it is unusual

typically like we work with retail

customers like right around this type of

time of year to pre-provision their

capacity for a specific period of time

and we work with them to really uh

another one is um the like the

accounting businesses there are some

businesses like that where we work with

them to scale up around tax time and so

we provision in advance of that period

they kind of ramp up to what they've

scaled to and then when that period is

over they ramp down. And you know again

you can get pretty granular with exactly

the capacity you need and when you need

it and so we have kind of both of those

uh models. Um

so um yeah real world scenarios well

again a lot of it has to do with

scalability and global reach needing uh

systems that don't go down that will be

available even if you lose like a data

center or something like that. In fact

um there's some anecdotal evidence that

during Amazon's last big outage and I'm

not picking on Amazon every cloud has

its outage. Um but some of these large

players who I showed early uh because

they were running Cassandra in a global

way and it supports a multi-data center

deployment.

It's focused on being up. So if you lose

um uh one part of your deployment, it

will still run and it'll serve uh

customer requests. Um and again it's

it's really all about something that

scales needing something that scales and

has maximum uptime. So yes, good good

questions. Thank you. Uh so yeah, let's

talk about Langflow a little bit. So

Langflow is an open-source

uh graphical drag and drop um interface.

It there's a whole community around

Langflow. It's based on a code framework

called Lang Chain

and it allows you to

rapidly iterate to just work in a drag

and drop mode. Every one of the widgets

in Langflow is actually a code widget.

So you can drop into code if you want

to. You could create a custom widget

that you then drag on with lang chain

and Python code. Um, and it really

provides kind of pre-built ways to

integrate with all of the different

things that you need to coordinate when

you're building a Genai application. And

well, here's just a little taste. I'll

show a little bit of this in the tool as

well, but you know, when you're building

a Genai app, you have a lot of things

you need to orchestrate.

And so Langflow helps you do that. Uh

just quickly talk about a customer who

is built their business on Langflow.

This is super interesting. They have a

product called Athena. It's kind of like

a assistant that knows everything within

your enterprise. So it's it's

intelligent about all of the knowledge

that you have within your

internal systems

and you basically invite it to meetings.

You can give it tasks and it will go out

into your you know internet essentially

and get you the information and say

gentic based workflows. And these guys

actually came out of the defense

industry and so they know how to work

and they some of their initial customers

were you know required extreme

government compliance and you can see

this quote here from uh Brendan Guiles

and again I mentioned our event November

10th he will be at this event

uh that it completely transformed the

way they worked. So they would take lang

flow to their customers and they would

start to build these flows and they

could give lang flow and the customer

could build some initial flows and they

went from PC to production in two weeks.

So really quite a dramatic result and

it's enabled their business to move

extremely fast.

Uh and just a little bit at the bottom

that you know we do have subject matter

experts and building Genai apps and you

know when we have customers who are

really building at this scale we can

partner with you to help advise you on

the best way to go about this. So I'm

just going to talk a little bit about

what it really takes to build a Genai

app and then we'll go to the

demonstration. I think we had a poll we

wanted to show.

Are we able to show the poll?

>> Yeah, absolutely. That all set up.

>> So, while I'm talking, we're just kind

of curious where folks are in their

journey to build Genai apps.

Uh this is a much simplified view of

what you typically need to do if you

want to build a corporate genai app

based on your data. So typically you

have and I'm just going to move my mouse

around here folks can see that you have

kind of your own this would be your

vector store

where you want to take your data and

turn it into something that can be used

in a genai

um setting and in order to do that so

these are somewhat equivalent terms

they're used interchangeably you create

an embedding in something that's called

a vector. And like at its core, Gen AI

is all like statistics and math. And so

the representation in a database is

something called a vector. And it

literally, if you remember any of your

like high school math matrices,

uh arrays, it's a list of coordinates.

It's literally like a mathematical

object. Uh typically in a genai setting,

it has like 1500 coordinates or

something like that. Um, and so you

first need to take your data, you need

to run it through an open AI model. And

um, I I'm just noticing, it's funny. I

This is very OpenAI Azure focused, by

the way, folks. I I'm just realizing

I've had this slide for a while. This is

like the Azure A. So no, again this

could be any cloud, but that's kind of

what you're seeing with this this one

visualization. So uh again, no slant on

anybody who's using a different cloud

here, but you create the embeddings

uh which is running it through the model

and storing it in a vector database. And

then when uh are we getting answers here

for our uh poll,

Brian?

>> No, it popped up. So, we should be

getting some of those. Just looking for

for results.

>> Okay. All right. Um and then you're able

to query it in a genai way. You're able

to say, "Tell me something about my

data." you get some results back and

then you send it on like a typical like

would be like a chat GPT type request

through a genai model. You get a

response back. You send it to the app.

So just flowwise and I'm showing you

this because you're going to see very

similar in lang flow. You create the

embeddings

then your users input creates a search

against that database. You get some

results. You send those results to the

LLM and then you package it up as like

an API that can be embedded in any

application. So, Lenflow really helps

you do all this. Let's go take a look at

it. I think

>> and Gil um we had a couple responds. The

>> the overall thought was a few in a

within the next six months.

>> Okay. some within you know the other

quarter within the next year and then

>> um not sure yet for about 50% of the

folks here. So

>> definitely top of mind but you know how

some of these projects go just taking a

little bit more planning and timing.

>> Absolutely. And I mean, if you do not

have a toolkit to help you manage all of

this, and I uh I know this is kind of a

lot to talk through in a sort of like

somewhat beginning presentation, but

this is the struggle that people are

having is really orchestrating all of

this. So, thank you. Thanks for the uh

insight there. Hopefully, this is

something people will crack open to help

them move more quickly. Um I have a

slide about agentic. I mean, agents,

there's a lot of talk about agents. It's

really any kind of, you know, piece of

code or something that works with the

LLM that can be plugged in to do

something for you. This is a great link.

I would suggest if people are really

trying to wrap their head around agents,

they they read this guide. It's a really

great guide that IBM uh put together.

Um, but I really kind of want to get to

our demo. This is our like legacy data

stacks uh picture. We uh last year, if

you saw us at any shows, we had this

purple flying rhino. So, I thought that

was fun to kind of include here. So, I'm

going to start actually with a database.

I'm just going to quickly show you what

it looks like when you're working with

the Astra database. So, this is, you

know, like any cloud portal. I actually

have a database running here. We'll look

at that in a second. But let me just go

and create a database and just show you

how easy it is. So typically if we were

creating a database on premise, you

know, I'd have to provision servers. I

have to wait for the servers to start. I

have to size them. I have to attach

storage on and on. If I just want to

create a serverless vector database and

Astra is serverless, that's uh key

feature here. Uh let's get just give it

a name the webinar

DB

and I'll just say I'm deploying it into

Amazon US East and create the database.

This is going to provision everything I

need. I that's it. I did not have to

think about how big is this thing. I can

later on assign additional compute

capacity if I need to to this thing. but

it's just going to run and when it's

finished I'll have an a endpoint here

that I can access it with and in

addition the portal gives me all kinds

of code to access it. Um this could take

you know five sometimes you know a

little bit more than that minutes but in

the interest of time let's take a look

at this one that I've got here. So again

the portal is telling me my endpoint. I

can generate tokens here for access

and

uh I have a uh monitoring portal here.

This is just a super high level view of

requests. There are a lot of metrics

here in case you wanted to integrate

this with your existing kind of metrics

and monitoring solutions. It can be

exported. And then I've created a key

space here. You can see this keyspace

actually has data in it and um

I could navigate it look at a different

view of the data. These are actually

vectors of uh representation of data

that I've brought in which I'm going to

be talking about and I also because this

is a vector database I have defined it

with a specific AI model. This one has

768 dimensions. Again, that's those like

string of um number pairs and it's using

something called dotproduct similarity.

When we do a search against a a vector

database, it uses a mathematical

algorithm as opposed to like your

typical SQL select, find me a match

based on a key kind of thing. So, this

is the embedding model that it uses to

find something. Um and so that's defined

with the vector database. There's you

know a huge amount of functionality in

here. I just wanted to give people a

sense that you know this is really a

platform as a service and you just go

here create one you get the keys from

this uh you can there's a integrated

security model in case you want to

integrate this with your enterprise

security define entitlements of who can

do what etc.

And this is really what we are going to

base our Langflow app genai application

off of. So let's go to Langflow. I am

running Langflow desktop. So I can go to

langflow.org

and I can just say install the desktop

for Mac OS or Windows and it creates

this um uh graphical um view on uh

program app basically on my desktop and

it's these are some projects I have but

I can just come in here and create a new

flow. Let's just start with a blank flow

so I can get you right into the canvas

here. And so I have basic tools for uh

building a flow here. So like chat input

like your basic hello world chat output

and then I just connect them and if I

run it and as we would expect if I say

hello webinar it's going to come back

and say hello webinar. So very simple uh

this is really kind of deceiving how

simple it is because I could actually

deploy this. It has an API endpoint.

This is the graphical component of lang

flow, but behind the scenes is the

actual server. When you deploy this into

production, you deploy the server

without the graphical piece and it's

literally serving these flows. And I

have an API immediately with this thing,

right? Uh, and even tells me how to

access it, Python, JavaScript, curl,

etc. This is just like ready to go. So

if you remember to that complicated

diagram I showed the last piece is make

it available as an API. You get that

immediately here. Uh let's show you a

little bit more about what's in here. So

I can do agentic work. I have models

that I can bet in here. Uh all kinds of

different ways I can get data control

processing. I also have again if you

recall that slide with all those

different genai

solutions on that slide that people have

to orchestrate. Well, everybody who

participates in the Langflow community

which is you know almost anybody who

wants to provide a genai solution they

are in here. And in fact, if we go over

here to um IBM,

we actually have the IBM LLM is in here.

Let's get rid of this line and

um wire this up. Okay. So, now we want

to talk to the um I Whoa, what happened

there? Sorry.

We want to talk to the IBM LLM. So, our

chat input will go in and the output

will come from the LLM. I'm actually

running a Watson service right now. I

have a um Watson project set up and I've

set up environment variables which have

my keys in there. And so if I do this

and we'll just select this model

and run the playground, I can now say

some, you know, fun uh uh what is your

favorite webinar?

Let's see what it says.

And so we now have are just using the

Watson LLM

to

build our own little chat GPT here which

we could embed in um the uh whoops in

any of our applications. So you see it's

saying something fun technology TED

talks HubSpot inbound etc etc. Now

our challenge here today is we want to

build these types of apps with our data.

So for example,

what does Watson know about me? Gil

Isaacs, right? Tell me about Gil Isaacs.

Okay, so uh I haven't given it any data.

This is just going to And by the way,

this gives me a different answer every

time I do it because it it finds some

interesting things online. Uh, so

there's uh somebody who's a founder and

CEO of Tonic Health. Uh, I can tell you

that's different than what I did the

last time, right? Which this just has to

do with the way it goes out on the

internet and what it finds first. And we

haven't really done any kind of like

refinement

um of uh what we really consider to be

accurate using some AI techniques and

ranking and things like that. Uh oh, I

have a question. How easily does

Langflow integrate with other software?

Thank you. Uh so again the point I was

making before this is available like if

I like this flow I would just deploy the

server piece of this and it would be

running with a known API URL. Uh all of

these widgets let's close this down.

uh these are codebased. So here is the

lang chain code that this widget is

based on. So you can really integrate

with anything and again the community is

quite vibrant and there's kind of new

integrated pieces here as well. And then

the last piece when we start talking

about agentic

you can deploy these flows as um an MP

MCP server. So then now you're using MCP

which is the way everybody is doing

agentic and agent tools. Um you can

access flows running in lang flow. So uh

thank you thank you for that question.

Uh so let's go back to this question of

what do we know about Gil Isaac? So

let's think about our data. What do we

know about our data? Clearly with just a

standard LLM call we wouldn't know about

our data. So the uh way we do that and

actually neglect that whole big flow I

showed is something called rag retrieval

augmented generation. So I actually have

if I go and create a new flow. We have a

pre-built one for rag. I've got one

that's already running. Just in the

interest of time I'll go into that one.

And so if you recall there were two

pieces load data and then retrieve data.

Right? So the first piece is down here,

this pre-built rag flow. And in this

case, I've made it so that it uses the

IBM Watson embedding model

uh to embed my specific data. I actually

have, let me just quickly show you, and

not enough folks know this, you can

actually use LinkedIn to create like a

resume version of your profile. So I

have this and I have used this flow

to create a database version embed with

embeddings of my data. So, uh, this has

ran and it has inserted this data. And

if we go back to, um, the astro view,

um, which was here,

that's exactly what's here. So, there's

kind of like the content and then

there's like the metadata and the

vector. This, which is binary, so it's

just it's not really going to show it,

but this is the vectorzed version of my

data. So, that's that first piece. Think

of this as your proprietary data has now

been vectorized. The next piece is we

now want to engage with the user. Okay,

so that's this piece up here. And you're

also just seeing a little bit of sort of

the look and feel of langlow here. Um,

I'm going to have some input from the

user. I need to read against the

database. Now again, this Astra database

that I've been accessing, remember I

showed you I can get the keys right from

there. It's real easy. I have the keys

to then just put them into this flow and

I do some prompting. Take this content

which came back and now send it to chat

GPT type uh search. In this case, we're

using the Watson um AI model, not the

chat GPT one. And you can see I've ran

this before. Tell me what you know about

Gil Isaac's pre-sales software solution

cloud architect uh pre-sales technical

consulting worked in various roles it

has my actual uh companies here um again

just trying to move this along for folks

uh in the interest of time but um

think of this again as your data what do

we know the business you're in let's say

I'm in some financial advising business

that I have proprietary financial advice

I will create my own firewall database

in Astra

vectorize that data and now I can give

that to users who want to know what the

financial analysts have to say.

So let's take this to the last step

which is the agentic step. Um let's see

how do we start integrating lang flow

into existing AI workflows. Well, uh it

depends what you've used for your uh

workflows.

Uh typically now the way a lot of uh

these AI flows are integrating is

they're using uh capabilities like MCP

servers. So often whatever you're using

it is using MCP. Um again if it is not

using MCP if it's not available as a

tool you should be able to deploy some

existing flow as on a server where it

has an endpoint and then Langflow can

incorporate that in with the endpoint.

So let's I'm just I'm going to show you

a quick agent flow and then I'll show

you kind of like the final agent flow

just so you sort of see how it works.

This is a simple one. And um basically

I'm going to uh change this. I'm going

to use a different model. I So I I'll go

over here. Sorry. Let's go here. And

again, we'll drag in our Watson model.

We can search bring in Watson here as if

my drag will work. Come on. Bring it in.

And

I will that's those same parameters. You

can just see how easy it is to reuse all

of this once you have it set up. My

project ID,

my um API key. In this case, I just want

to use the language model. I'm not going

to use because the agent is going to

call it for its language model. And I'm

going to say connect another model. And

again, what you're seeing here is how

Langflow makes it really easy to like

switch back and forth between

frameworks, etc. And let's just run this

thing. And now, oh, whoops. I forgot to

let me let me show you something. So,

uh, this has a URL tool automatically

connected to it. I have, uh, a weather

site and, uh, yesterday

it was really windy in New Jersey. So,

here's the URL for the wind. And I will

go here to my chat and I'll say, uh,

tell me what I can can expect from the

wind based on this URL.

And what you're going to see here is

that the agentic flow Oh, what happened?

It does not like my deployment. This is

what happens when you do a live demo.

Uh,

Oh, I did not select a model. Helps if I

tell it what model to use. Okay, we'll

just save this guy.

This is a proof that we're really live,

folks. All right, playground. Run this

thing. And now, here's what you're

seeing. This is an agentic flow. I have

two I'm not going to use the other

tools. It's a calculator. It's much less

interesting. This is our kind of like

default reference tool. uh agentic flow.

But you could see this tool is calling

that it is and this is kind of like a

rag scenario. It's reading the data from

that. It's vectorizing it and then it's

sending it to the LLM and it's telling

us, hey, here's what's going on at this

URL.

So, this could be kind of interesting

for some of your internal sites. If you

had uh wanted to scrape some URL

information from existing sites, you

could build an agent to do that. Now,

the flow that I built here built on the

sort of Gil Isaac scenario we were

talking about. So, let's go to the flow

um that I had pre-built here. Now, what

this one does is I am interested in uh

kind of comparing my background to some

jobs that I'm looking at. So, I actually

have a uh old job posting. Uh whoops.

This is the old job posting.

And it's a data stack strategic account

executive. And I'm going to ask this

flow. Uh how Oh, we already did. Yeah, I

think we're running on time. So, let me

just show kind of what happened there.

How well do I match to this role? And so

you can see basically the invocation of

the tools here. It's showing you and

then it's getting my experience. It's

matching it. It's saying, well, I have

this job experience. Here's some

relevant skills and here's a conclusion.

So this is really an agentic flow using

my data.

And so this is this is kind of like the

it was in the oven and I sort of showed

it to you. Uh and you're seeing kind of

where you really can get to with your

own data and a combination of Astra

database with Langflow in this scenario.

Uh I just want to end as I mentioned on

this event that we have on Monday. If

you are in the New York area or

convenient to New York, you can register

here

for this webinar. In fact, I will I

mean, sorry, sorry, this is an in-person

event. We have an afternoon

of free hands-on with we're providing

food and everything. So IBM has a whole

bunch of open- source AI frameworks and

then we have an evening meetup with

pizza and beer and some as I mentioned

the CEO of um that startup Athena

Intelligence will be there. There's a

separate sign up for the evening that's

linked to here. So love to see anybody

from this webinar join us at this event

that we have scheduled. So, I think I I

dumped a heck of a lot of information on

folks. Let me really just kind of take a

breath here and see where folks are at

and um

what kind of questions people have.

>> Yo, great job. Thanks for walking

through all of that. It was certainly

helpful. Um, great. You can tell see how

excited you are about where the, you

know, where data stacks is going, where

it fits into the IBM portfolio and

really the overall like aentic

experience that it's it's continues to

provide and enhance. So, thanks. I think

um definitely learned a lot and looking

forward to continuing to, you know, have

these types of conversations. Are there

any other questions from from the group

um as we got someone like Gil on the

call here to answer those?

If there aren't any other questions, you

know, Gil pointed us all to this awesome

event that's happening in I think you

said New York coming up here quickly.

Take a look if if it meets, you know,

where you live or what you're looking to

to go for. Um, please sign up, register.

It' be a great event. Um, if you have

any additional questions, feel free to

reach out to myself or um, if you're

already associated with maybe an IBM rep

or individual, obviously funnel those

through them. But, um, we're here to

help. We're we're looking forward to

data stacks continuously being

integrated into the IBM fold and and

bringing it to more and more

organizations. So, Gil, thanks for all

the time today and and walking us

through the tech.

>> Thank you, Brian. Thank you, TechD. And

thanks to everybody who took some time

out to uh listen to me blab on.

>> Perfect. Well, with that, we'll we'll

wrap it up here. Um there should be a

recording that follows and so we'll get

that out to everybody. Gil, thanks

again.

>> Fantastic. Have a good one, everybody.

>> Bye. you.