Hi everyone, my name is Lee and I'm a

lead solution engineer here at OpenAI

working very closely with financial

services customers across AMIA. Today

we've got some really exciting things to

show you, some of which we only launched

last week. Every day when I work with

financial services institutions, they

always have one question. Where can AI

actually change how my business runs?

Today there are two paths for AI

adoption. first giving chatb and codecs

so that employees can use AI in their

daily work. Second, there's systems.

This is where companies are building

entirely new products. They're uh

enhancing their customer service.

They're improving client advisory and

they're working on operational support.

So, it looks like this. We've got chat

GPC and codeex working from the bottom

up using employees as they get more AI

literate. And then we've got AI systems

from top down. And these are those major

transformational uh initiatives.

But there's a missing layer in the

middle, the automation at the team and

the department level. And this is a gap

that the new chatbt workspace agents is

designed to close. And that's what I'll

be demoing for you today.

When we say agents, we mean AI systems

that we can delegate meaningful tasks

to, not just ask questions of. And we

can do that by using tools that we

already rely on like email, calendar,

and those productivity apps that we're

using every day.

And we want these agents to complete

work in the same way that people do. And

in the last few months, there's been a

huge jump in capabilities. And we had

another leap last week with GPT 5.5.

Today, agents take on complex work that

used to take hours or days, and they can

handle them from start to finish. What

happens when you need to build something

custom and you need to delegate

something that your team or your

departments are currently working on?

Many of you have already built custom

GPTs. With Workspace agents, we evolved

that into something much more powerful.

We've got a new agent builder which

brings shared applications, skills, and

deployment all to one platform. And this

allows these agents to work in the same

place that work already happens. So now

let's jump into the demo.

This is the is a standard attribute

interface that I'm sure you're all

familiar with. But down the left hand

side, you can now see we've got an

agents option that we can start with.

And now for many teams, the challenge

isn't a lack of work to automate. It's

that the work is spread across meetings,

documents, emails, and other systems.

And the decisions all depend on specific

context.

So today, I'm going to show you how I

can quickly spin up an Aentic co-orker.

In this case, I want to build my own

chief of staff agent. I want it to help

me coordinate work, track priorities,

prepare meetings, and help keep the team

moving. Every function can delegate

meaningful work to agents and these can

understand the role, use the right tools

and they can operate with how the team

already works. Here we're going to use

one of the uh templates we've got

already for the chief of staff uh agent

here. And you can see this already has a

set of instructions. It already has a

set of tools that it's able to work with

and it already has capabilities that I

can start using.

Next, you can see here I want to uh

start connecting some of those tools

that I mentioned to make sure it's

correct for my workflow. In this case,

I'm going to use the Microsoft set of uh

tools here. So, we've got things like

Outlook calendar, Teams, and then my

Outlook email.

Now, we can see that the instructions

are going to be automatically written by

another agent. So, you don't need prompt

engineering skills. You don't need any

technical skills at all. But what it

means is that as a business user, you

can now use an agent to build another

agent for you just with natural

language.

And that's it. That's the initial

version. I haven't written any code and

I've got the first version of the agent

ready to go. But now I want to customize

this to my own requirements. I want it

at 9:00 a.m. I want it to run every day.

I wanted to look at all of my meetings,

look at all of the applications, look at

my emails that came in overnight, and

generate a daily brief so that I could

arrive and be prepared for all of the

meetings for the day.

So once again here I just give the

instructions again in natural language

telling it I want it to run at 9:00 a.m.

No technical skills needed here at all.

And within a matter of seconds we can

see that it's able to customize my

agents to my team's requirements and

work exactly how I like to work every

day.

So once this is done, we can go and test

our agent.

And we can now see there's two starter

prompts underneath to get me started. If

I want to, I can give it its own set of

instructions to perform for me. But you

can see that there's two already there

to go. And you can think of these as

capabilities that have already been

built into this agent. So let's ask it

to prepare the today's brief here. You

can see I'm asking it to do a concise

brief uh using all of the available

information that I mentioned earlier.

Highlight priorities, decisions,

blockers, and follow-ups and then post

these in the CFO team channel in the

daily prep channel.

And now we can see the agent spinning

up. It's going to start grabbing all of

those details, connecting into my email,

connecting to my calendar, and all those

other sources that I mentioned. It's

going to check all of those meetings

that I've got for the day. It's going to

cross reference that with information

that might be in my emails. And it's

going to pull all the context needed

from all of these sources. And the first

time it runs, it's going to ask me to

give permission to post to the Teams

channel. So, it's just going to set that

up now. And we'll go and give it the the

approval and see how that's worked.

And that's it. That's now posted to

Teams. So, let's now go and have a look

at what it was able to generate for me.

So, now over in Teams, we can see in the

daily prep channel, we can see that

there's an update. So let's go and have

a look what it posted and we can see our

chief of staff agent from chat GPT has

gone and collected all that information

and then it's posted that daily brief

for me inside teams exactly where I want

the information to be for my daily work.

So within a couple of minutes we've

built an agent from scratch. We've

connected it to tools that I use every

day. We've given it some customized

guidance and we now have a running chief

of staff agent for my whole team.

But let's go back to the agent and take

it a step further. This t this week my

team have been burning themselves out

running from meeting to meeting and they

haven't had any time to prep in between.

So now let's add a new capability. I

want the agent to proactively research

before every meeting like having an

expert chief of staff who's the telling

me who's there, what's the latest and

then what's the goal of that meeting. So

for this we need some additional context

for some other tools. So now let's go

and add some more that are available.

I'm going to start off with SharePoint.

This is where I'm storing all of the

company information and all the

information I've taken as notes that's

shared across the organization. So,

we'll go and add that. And next, I want

to add Salesforce for all of that CRM

and all that kind of rich information

about all the context from that

customer. So, we'll go and add

Salesforce as well.

And now that's done. We've got those two

apps connected. You can also add other

apps that you use every day here as well

or even custom applications that you

just you have inside your business. Next

is skills. Skills are a way of capturing

snippets of information instructions to

perform critical tasks. Think of these

as an amazing way to capture all of that

those tribal knowledge and conventions

that are currently trapped in people's

heads. And we can turn those into

repeatable workflows.

You can see that there's two skills

already in use here. We've got the chief

of staff skill and we've got a final

brief formatting skill.

But now let's go and add another one

that I've already been using across my

team. So I've already got a skill here

for meeting prep. This tells uh chatbt

the way that I want this information to

be structured, the key information

that's needed, the source of this

information, and where I want that

information to be posted. So, let's go

and add that to my agent as well.

Finally, let's save our changes.

And now I want to give the agent some

more instructions about what to do with

these new applications that I've gone

and connected. So again, we'll use the

agent on the side to have a natural

language conversation and give it this

additional context to go and update the

agent.

So here there's all the information.

I've just added Salesforce and

SharePoint. Add a new capability. I want

it to be able to generate these quick

meeting briefs in in chat GBT. And all

the information I want to give it is

just give me the information for the

next meeting. So it needs to go through

here and make the updates. Um and then

it's it will also um add a new starter

prompt there for me to use in a second.

So now again, let's go and update this.

And now we can go and deploy this. and

is now available for the whole team. So,

let's go and use it. And here is my

completed agent deployed and ready. And

now you can see we've now got a third

starter prompt underneath as well to

prepare me for my next meeting.

Now, it's going to run pull all of those

contexts from those different sources

including Salesforce um and SharePoint

that I went and added. It's going to go

and um check all of the information, put

that together into a concise brief in

the way that the skill gave the

instructions to go and represent that.

And personally, I have one of these

running every day. Um I have my own

agent that checks all of my emails that

come in overnight, the important updates

from across the business, the things

that I said I would do on Slack

yesterday or on calls and all the

contexts on the transcripts. And now it

means that I get that first hour of my

day back because I come into the uh into

work in the morning and all of my emails

have a draft ready to go with all of the

contacts from across the business. And

it means that I can just go through all

of those emails and click send and just

approve those and get those out to my

customers. And it's completely

transformed the way that I work.

So now we can see here we're all ready

to go for that next meeting. Before the

team was understaffed and they couldn't

prep for those meetings, but now we've

enabled everyone to turn up as if

they've been prepped by their own chief

of staff agent.

But that was just one example there. But

this is a pattern that you can apply

across the business. We've seen examples

of agents like KYC on boarding, AML

investigations, relationship management,

and more. The opportunity here isn't

about one single uh automation project.

It's actually about a brand new

operating model. Every team can spin up

a ro specific agent to take manual work

off their plate and help the business

move faster. But the next question is is

what if we've got thousands of these

agents? How do we manage them? And

that's where Frontier comes in. Frontier

is our platform for deploying and

managing agents at scale. It connects to

systems usually in silos, things like

data warehouses, CRM, and internal

applications. It gives AI co-workers the

same shared context that the teams

currently rely on. And from there,

agents can reason over data. They can

run code. They can use tools and they

can take actions all in a governed

environment.

And the key thing here as well is as

they work, the system improves. They

will learn from interactions. They will

evaluate their performance over time.

And it means that the more they do, the

better they get, just like human workers

in the business right now.

So today it's possible to build in chat

GPT codeex and the API and we want to

make it easier to deploy out of the box

agents, plugins and skills all specific

for financial services workflows. This

matters because it moves the system

towards much more automation. We can use

purpose-built agents that plug directly

into work and they can handle all the

repeatable processes with even less lift

and customization.

With all those foundations in place,

agents become incredibly powerful,

allowing you to delegate more workflows

to AI over time. And next, Stephanie is

going to show you how teams are using

them to create transformative impacts

across the workforce. Thank you.