We've built our first AI agentic agent

using Microsoft agent framework.

Good job. But wait,

we want to do something a bit more

complicated.

Right.

Right.

Enter workflows.

Are you excited?

Hello everyone and welcome back to my

channel. My name is Kirk and in today's

video we're going to be looking at

workflows in Microsoft Agent Framework.

Now, as you probably know or maybe you

don't know, but this video is a part of

a series of videos where we keep on

diving into topics that help you build

AI agents using Microsoft Agent

Framework. So, with that said, you

should probably subscribe if you're not

already subscribed. Now, let's just jump

right into it and start looking at

workflows.

We are back to our AI agent. An agent is

given a task. After given this task, the

agent will then do react, reason plus

act. This is what makes the agent

agentic.

The agent perceives a task, reasons

about what to do, and acts by using

tools to produce outcomes.

But wait, agents often act like black

boxes. You just give them a prompt, and

they just figure it out.

Enter workflows. Workflows and agentic

frameworks give structure into how

agents think, act, and interact with its

environment. Workflows define explicit

steps in the reasoning and acting

process.

In the example shown here, we can see

the workflow steps, search knowledge

base, retrieve additional info, and

summarize info. Now, let's look at how

we can define workflows using Microsoft

Agent Framework.

And we are back and we're finally gonna

have a bit more fun with agents by

looking at workflows in Microsoft Agent

Framework. Are you excited? I am. Okay.

So, if you remember our first tutorial

way back when we first looked at

Microsoft agent framework, we created an

agent. We created an agent with tool

which had this get weather tool that we

passed into the agent and we also had

this rag tool. What I'm going to do is

I'm going to go to this agent with tool

and I'm going to modify it to create

structured output in its response.

And how we're going to do that. So I've

actually just copied this file into

agent with tools structured output

and we're going to modify this and we're

going to use a pedantic model

to create our response. And this model I

will show you right now.

It's going to be city info. And of

course

this we're going to have to import base

model here.

This is our pedantic model schema

and we're going to modify the agent

here. But let's give it a different

prompt here. When we create the agent

instead of just saying you are a helpful

assistant, we're going to say

you are a helpful assistant that figures

out the city from the information

provided and also returns the weather.

So, what we're going to do now in our

main, instead of saying, "Tell me the

weather in Los Angeles."

Now, I'm based in Canada.

So, what I'm going to do is change this

to say,

let's say

I'm at the CN Tower.

Hopefully, you know the CN Tower. If you

don't, it's uh

the second largest structure in the

world based in Toronto. I'm giving you a

hint right there. And what we're going

to do was we're going to run our agent

and we're also going to say

to let's just format this a bit nicer

and give it a response format of city

info.

And now

when this agent runs, we should have a

value and now we can output the city and

the weather in nice JSON format. That's

what we're going to get back by using

this pedantic model. So let's save this.

Let's run this.

And now the response is city Toronto

weather cloudy with a high of 15. And of

course that's what our tool does. Says

the weather in Toronto is cloudy with a

high 15. And now we are just responding

back. So think about this. The agent

figured out

just by giving it a request of I'm at

the CN Tower. It knows we're in Toronto.

can figure that out and use the tool to

return the weather.

That's pretty awesome, right? I mean,

that's worth uh the admission here just

watching this tutorial. I can just leave

now, right? I mean, we accomplished

something. I'm kidding. We haven't hit

workflows yet. So, stay tuned. A lot

more excitement, but this is step one

because we're going to use this in our

workflow next. Okay. Now what I've done

now is I've created this file called

agents and workflows

and this I've taken a lot of stuff from

our agent with tool structured output

have all the import statements we have

our padant model we have our get weather

tool and now we're going to start

filling out the contents of our main

function and what we're going to do is

we're going to create our first

workflow. Okay. So, first thing I'm

going to do in this main is I'm going to

create our chat client.

So, this looks very similar to what

we've done with agent with tool and

agent with tool structured output. So,

you look at here, we have our agent that

we're just creating and that's it. We

have our chat client.

Now we're going to create our first

agent that we're going to put into our

workflow. So I'm going to copy and paste

some code here.

Let's put this into a try.

Now look at this. We have our city info

agent and I'm giving it a name and I'm

giving it some instructions here and I'm

giving it the tools and the response

format city info. So if I just save

this, compare this to what we just did

before.

We have the exact same instructions

here. Our system prompt tools. We've

just given it a name. And our output

format, our response format, instead of

putting it here, we're putting it into

when we create the agent. So look at

that again. Pretty much the same thing

that we just did before. This is going

to take

an input.

For instance, wherever you're at, you

can put in Empire State Building and it

should give you a response of New York

and use the tool here.

And

actually, what is it? It's November 9th.

It's on a Sunday afternoon. I'm

recording this. I'm based in Toronto

and it is snowing. And yes,

probably a lot of you are not from

Canada and you think, well, it always

snows in Canada. This is pretty early

for snow

on November. Early November, December,

Christmas. Yes. So, what I'm doing is I

just changed this tool to say snowy with

with a high of 0 degrees Celsius. Gang,

I apologize. I'm a Canadian. I'm

thinking in Celsius, not Fahrenheit. But

I just want to show you that. So, we

have our agent

and we've created it. That's it. Next

step, let's create another agent. Okay,

I'm going to create another agent here.

I'm going to copy and paste some code

here.

And let's save this. Pretty much

creating the agent in the same way.

Wrapping it wrapping it with this agent

executive. Actually, I have to import

that as well. And so from

from agent framework

import

agent executive.

Save that for now.

Let's look at this agent I just created.

I'm going to call this tours

recommendations agent.

And what I'm going to do is say you are

actually not not an email assistant. You

are an assistant who provides tourist

recommendations based on

let's actually change the wording based

on a city.

Your input might be a JSON object that

includes city

and it might also include weather.

Base your response on city and weather.

If the weather is sunny and warm,

recommend an outdoor place. else

recommend an indoor place.

Do do not recommend the place you are

already at and return JSON with a single

response, a single field response. So

think about this. This agent is going to

is going to expect

as its request a JSON object with city

and weather.

This agent we already created is going

to output with city info which will have

city and it's going to have name and

weather.

So actually I should change this uh it's

going to have yeah city the name city

city info. So hopefully it will just

know that yeah um base response on city

and weather. Now it's gonna give a

response with hopefully just a

recommendation based on your input. Got

that? Okay. Now, we're going to create

our workflow. We're going to create a se

a sequential workflow pipeline.

And to do that, I'm going to copy some

code here.

We have our workflow here. We're using

workflow builder. We're going to say

start

at the city info agent. So that's the

first one we do.

Then we're create an edge.

So if you worked with other agentic

frameworks, very similar. We have nodes

which are agent executives. So something

that's being processed. In this case,

it's an agent. And we're going to attach

them.

We're going to attach these edges

together. city info agent and tourist

recommendation recommendations agent and

we're going to build it. And of course,

I have to import all this stuff.

And there we go. We have defined a very

simple sequential workflow pipeline.

Let's save that.

And I'm also gonna have a little bit of

a helper here to create diagrams for us.

So, let's do that next.

Okay. Our workflow here is pretty

simple. Just two nodes, one edge, very

it's just going to be sequential. But

when when things start getting more

complicated, we want a way to visualize

this. And Microsoft Agent Framework has

a really good helper to create diagrams

for us. Workflow viz. I've already

imported it here and I've already

installed it. So I've already done UV

add graph viz. So this is something you

need to add. Uh I'm using UV as my

package manager. So you can do pip

install as well. And you I'm also on a

MacBook. So I have brew install here.

Graph viz. I've I've already installed

this. If you are on a on Windows, I

think you have to um download it. And if

you are on Unix, you can just use appget

to install it. But just look that up. So

install graph viz and let's take a look

here. Okay. So I'm going to after we

create our workflow, we initiate our

workflow viz class here and let me give

this just give the name workflow

workflow architecture and just create a

SVG file. So that's that. Uh we run

this. Hopefully it will save this to our

docs.

I think that matches our try statement

right now. Save that. And let's give

this a run now.

Okay, I just had to match this up here.

Have this filing statement here. And now

we should be able to give this run. So

let's give this a run now.

And here we go. So this created our

workflow architecture diagram. Look at

that. So we have city info, our start

going to tourist recommendations gang

just a helper so you can visualize your

workflows. Ours is pretty simple right

now but in the future you probably want

this. So next let's actually try to run

this workflow.

Okay, now we are actually ready to run

this.

I going to run in here. We don't need to

create our diagram again. I'll just

comment that out. But if you want to

create your

diagram and again all this code will be

in my GitHub repo which you'll see in

the the comments below this. Hopefully

you're just following along. I'm trying

to do something a bit more interesting

than just a a simple workflow that

doesn't really do any AI magic. this.

I'm hoping you'll see it and it'll

inspire you

and it'll really get you excited about

using a frameworks specifically

Microsoft agent framework. Okay. So,

what I'm doing is we're going to run our

stream our workflow and say you are at

the CN Tower. Same thing that we did in

the last demo at the very start of this

tutorial. And what do you think it's

going to do? It's going to go through

that sequence, figure out what city

you're in, and then hopefully the next

agent, it will give you some more

recommendations of other places to go

based on the weather. Isn't that kind of

cool? Okay, so we have we're running the

stream

and I'm just going to copy some code

here.

Okay, so what I have here is we have a

collection of events from our stream.

And now once we are running this we're

and it's agent run update event we're

going to start outputting what's coming

from from it and I'm also outputting the

workflow status event. So let's actually

run this and see what happens and I'll

try to explain it to you. I'm hoping I

have everything here

in my import.

Yes, we do.

Look at that. So our first status event

is workflow run status in progress.

That's down here.

And

the next one we're going to have this

here. This eid is going to be our

executive name which we defined up here

as being city info.

and we're going to get back

remember Toronto and the weather in

Toronto is snowy with a high of 0

degrees which I can assure you right now

on November 9th it is and I'm not happy

about it. Okay,

now the response. Look at this. We're

coming back. We're going into our

tourist recommendations here and we've

inside the the workflow. We're feeding

in Toronto and the weather. And the

response here is since you are at the CN

Tower and the weather is snowy, a great

indoor option to explore could be the

Royal Ontario Museum. That makes sense,

right?

And then at the end, we our agent is

done. But look at that. That's pretty

cool. And that's a bit more interesting

than just um doing some very trivial

example of I don't know creating a bunch

of text and then summarizing it, which a

lot of workflow examples do. I'm hoping

this gives you a little bit of

inspiration. But let's actually change

this. Pretend again this is our weather

tool. You'd probably want to do a

restful call to actually get the real

weather. But let's say if it's

let's change this to be sunny and 20

degrees, which I assure you it's not

right now,

but sometimes it is. And I run this

again.

And right now, since the weather is

sunny and warm, okay, again, we're in

Toronto. Consider visiting High Park for

scenic outdoor experiences. So there it

knows there it knows the weather that's

being fed in from one agent in our

workflow and giving you re

recommendation since it's warm and sunny

outside telling you to go to a park.

That's it. Pretty simple, but we

actually accomplished a lot. The code

will be in my GitHub repo. Go take a

look. If you like this, please give it a

like. subscribe because I'm going to do

a lot more. I'm going to go in a lot

more depth with these agents. This is

just the start, but this is pretty

exciting. I'm excited. Hopefully, you

are too. And let's continue this

journey. Okay, that's it for today's

lesson on workflows in Microsoft Agent

Framework. And as I said, this is a part

of a series of videos on Microsoft Agent

Framework and building agents. So, if

you like this video, please like it. And

if you're not already subscribed, please

subscribe. We have a lot more to talk

about, even with workflows, a lot more

to talk about, and of course with

agents. So, until next time, I'll see

you then.