What is Google's Agentic AI Strategy? (Explained by Google Cloud's CTO) | CXOTalk #897 - CXOTalk

Episode Transcript
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(00:00):
Google is betting billions on AIagents and multimodal models.
But what's really behind the strategy?
Today on CXO talk number 897, Will Grannis, chief technology
officer of Google Cloud, takes us inside what you need to know

(00:21):
now, I'm your host, Michael Krigsman.
Let's get into it. My team and I work with our top
roughly 150 customers around theworld world to make sure that as
this technology factory that we're spinning up all the time
and releasing new products, thatthey know how best to leverage
that technology for their own specific business use cases and
needs. And then on the other side, we

(00:43):
also work on kind of new and emergent problems in technology
to try to solve them. An example of this could be
right now as you build 10s, hundreds, even thousands of
agents, agent alignment against tasks is a really difficult
problem. So that's one of the things that
more of an R&D mode we work to make easier for our customers.

(01:04):
What's happening in the agent world?
And then we can talk about what you're doing specifically with
agents. Well, agents have quickly become
kind of the surface through which many consumers and
businesses are starting to experience the power of AI.
And if you, if you think about in historical context, we've
always been looking to automate tasks that human beings, it

(01:27):
isn't necessarily the highest expression of our value.
So for example, you know, we in spreadsheet software, we have
been running formulas for many years to try to automate the
calculation of certain numbers and to automate a task of
calculation. That's something computers do
really well and humans don't need to necessarily spend their
time on. If you've ever worked on a

(01:47):
laptop and you've ever tried to put a macro or like an
automation on your laptop, that was, you know, an early form of
task automation. Because if you're continuously
clicking or doing the same thingover and over again, you want to
automate that so it gets done a little bit more quickly and more
efficiently. So agents are kind of like
almost like this third wave of automation and intelligence and

(02:09):
that they can take intent, they can take it in a variety of
ways. It can be typed, it could be
spoken and it can execute tasks on your behalf.
And this obviously has profound potential impact in today real
world impact across number of consumer use cases as well as
business to business use cases. And it's not unusual in my, you

(02:29):
know, I mentioned earlier, I work with, you know, 150 ish
customers around the world, top customers and across all
industries. It's not unusual for me to run
into an organization these days like Highmark Health, which has
an agent that they've deployed, an agent platform they've
deployed where their employees who have a routine questions
about benefits, processes, procedures within the within the

(02:54):
organization. Before they'd have to go to like
websites and scroll through it. They'd have to ask somebody if
they know you know how to how tobook travel or how to expense
something or how to provide or how to set up a conference room.
All of those kind of like basic everyday tasks the organization
has to do. All that knowledge now has been

(03:14):
indexed and is now available through a very simple interface
that today I think roughly 60,000 users at Highmark Health
are interacting with agents to get the knowledge that they need
to execute the work that they need to execute almost
immediately. Now let's take a moment to learn
about Emeritus, which is making CXO talk possible.

(03:35):
If you're a business leader navigating change or driving
growth, explore executive education programs with
Emeritus, a global leader. They offer programs developed in
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(03:59):
strategy and leadership. Find your program at
www.emeritus dot org. What are the the complexities of
creating these agents and what are you doing?
What Google doing to make it easier?
We are consolidating our own approach to agent development

(04:23):
and AI development. And just yesterday we launched
something called Gemini Enterprise, which brings
together all the different pieces of AI and agent creation
and deployment and management into one singular stack and
package. And what this has done is this
created what we like to refer toas the new front door for AI in

(04:45):
the workplace. And it consists of 6 core
components. So first, the chat interface or
the AI interface. This is something that people
are now used to using. Four years ago was a brand new
concept. Now it's, you know, it's very
commonplace. So this is the first piece of
this Gemini Enterprises, the chat interface.
Now underneath that are all the models that feed the

(05:06):
intelligence. So this could be Gemini 2.5 Pro,
it can be Gemini Flash, it couldeven be multimodal models such
as VO and Imagine. And those are the models that
help people execute their tasks.And that's the brains of the
execution engine behind agents. Underneath that is the agent
platform. So individuals within an
organization can build this kindof task automation level of

(05:27):
agents, but also multiple organizations within one company
or firm can come together and build multi agent workflows
using this third tier, this agent platform.
Underneath that you have out-of-the-box agents.
So we have data science agents, we have deep research agents, we
even have coding agents that areavailable to get value from the

(05:51):
platform out-of-the-box and thencreate scaffolding and enrich
them over time. There's anybody who works on
agents knows you need access to data and context.
So there's also a set of connectors to 3rd party data
sources like your ServiceNow implementations, workflows,
Oracle, Salesforce, as well as even JIRA, Confluence and, and

(06:15):
databases like Google Bigquery. So those connectors are a key
part of this Gemini enterprise stack.
And then finally, and maybe mostimportantly, as agents
proliferate governance and security, so making sure that
the agents are created by the right people with the right
policies, have the ability to execute their tasks safely and
securely within an enterprise context.

(06:36):
And so that's job number one forus in making agents more useful
and more available was bringing together all of the ways that we
help organizations with AI agents.
There are a couple of, there arethere are also a couple of
really important things I think translate beyond even Google's
approach to this for any organization in general when

(06:56):
they're building and deploying agents.
So number one, I mentioned this earlier is context.
Think of agents as a very explicit task execution machines
and within an organization, first step is to make sure that
you actually have written down or available to, you know this

(07:18):
task automation, your business process or your business
workflow. One interesting work right now,
Michael, is that in regulated industries such as financial
services, healthcare, public sector, those organizations for
many years have been highly documented in terms of their
business process and workload, their standard operating
procedures. And so they actually have a jump

(07:39):
start into the agent world, where as we tend to think of
technology as you're coming to regulated industries second or a
little bit later in the world ofagents, they're seeing time to
value a little faster because they already have all of those
key business processes and data documented that could then feed
the agent execution and the agent rules through a workflow.

(08:00):
So the context and the data is critical.
I just want to remind everybody that right now you can ask your
questions. If you're on Twitter X, use the
hashtag CXO talk. If you're watching on LinkedIn,
just pop your questions into theLinkedIn chat.

(08:21):
And this is from Anthony Scrifignano, who is a very
prominent data scientist. And he says one of the biggest
challenges with RPA robotic process automation was
automating previous tasks that were not designed for
automation. And so how do you advise clients

(08:42):
to be agentic ready before they rush to deploy agents in the
enterprise? Every customer that I've worked
with that has been successful inthese early agent
implementations, they've all picked a problem or a business
process for which they had data and context.
They had standard operating procedures.

(09:03):
It was a, it was a known problemin a workflow.
And so to your point around trying to automate the wrong
thing, they take the time and the diligence and they, and it's
kind of in the old world, we would call this application
rationalization. I guess in the new world we
might call this, you know, agentic workflow
rationalization. And that's taking inventory of
all the things that you might choose to do and being very

(09:24):
methodical about picking one that has the data, the context,
the documentation. That's absolutely critical.
Then once you, you know, you've identified that very specific
workflow that you're trying to automate and execute, it's also
extremely important to have a sense of how you're going to
evaluate completion and success And evals actually, and

(09:48):
evaluations tend to be one of the stickiest parts of the kind
of agentic development workflow today because think about it
like a manager at a desk. And in the past, the work that
that would come to that manager would be gated by how fast
humans could bring decisions or bring execution tasks to that
manager. And so, you know, it's a steady

(10:09):
stream of them. Today, agents and automation can
bring many, many tasks for approval, and it overwhelms a
human's ability to make decisions fast enough.
So the only way out of that trapis to design evaluations and AI
as a judge or AI as a critic, orAI as the evaluator, so that the
AI itself and the agents themselves can evaluate task

(10:31):
completion, whether it's good enough or not, and either send
agents back to do a better job or move on to the next step in
the workflow. And that is a unique problem
that kind of transcends our PA, which is RPA being kind of like
single task, single automation. These are multi task, multi step
automation concerns now. And you know, looking ahead a

(10:52):
little bit, one of the things that we see is that most of the
value from agents will be derived from multi step, multi
task, multi agent work flows. And I, you know, an example of
this could be there's a really interesting problem in telco and
in customer service in telco. And that is that when your Wi-Fi

(11:15):
goes out, you know, that's a very, it's, it's in our hyper
connected world, Wi-Fi going outis a very emotional moment for
human beings, myself included. And so when the Wi-Fi goes out,
the first thing you want to do is you want to get help.
And the typical workflow for getting help is you go to the,
the website and you try to scroll through, you know,
whoever your provider is or the mobile app, you try to find how

(11:36):
to debug the problem. It's a very, it's a very chat
kind of 2010 interaction model. Well, in the world of agents.
And there's a telco in the UK that's doing this right now that
we're working with. What they do is they have an app
experience. And if someone is experiencing

(11:56):
an outage of the Wi-Fi and they still have access to cellular,
they pop up a live multimodal experience where the customer
support agent will come up. They will have the individual
point their camera at the equipment that they have in the
house. And they're able to figure out
how to do troubleshooting based on the equipment that's in the
location, you know, of the apartment or the house, that

(12:18):
person said through a completelynative multimodal experience.
And so that is an example of, you know, you have an
orchestrator agent, you have subagents underneath the hood that
are characterizing, you know, a video and, and orchestrating the
chat and orchestrating the text.So all these different agents
are coming together to create this very seamless support

(12:38):
experience. And that is a very that's a very
modern multi, multi step, multi work, multi agent workflow.
Now let's quickly hear from Emeritus, which is making CXO
talk possible. If you're a business leader
navigating change or driving growth, explore executive

(12:58):
education programs with Emeritus, a global leader.
They offer programs developed incollaboration with top
universities, designed for decision makers like you.
There's a program tailored to your goals, whether that's AI
and digital transformation or strategy and leadership.
Find your program at www.emeritus.org.

(13:24):
You made a very interesting point several times, which is
this idea of intelligence or judgement.
I don't think you use those terms as being a key
distinguishing factor. It seems to me that's the point
that actually changes all of this.

(13:46):
It's absolutely true, and we call it evals or AI as judge or
AI as a critic, but it is the step at which companies
themselves and organizations themselves also have to, in some
cases for the first time, actually document what their
decision rules are. You know, I come from the
enterprise. I've been working in enterprise

(14:07):
technology for a few decades now, longer than I would like to
specify very specifically. But one of the things that
happens in a lot of companies and most companies that I work
with and I've worked in is there's the process, there's the
documentation, and then there's the human judgement, unspoken
norms and things that sit underneath the surface.

(14:30):
And so in order to give, you know, the software is very
explicit, if you say, you know, I want, we're working with a
company right now and part of what we're working on is people
want to buy HomeGoods, but they want to be able to see it in
their, in their kind of living space before they choose.

(14:51):
And so if you're going to createan AI as kind of a designer to
automate these processes and to create this kind of like before
you buy it, like let's put it inyour space, that's a very
complex problem because you're dealing with physics, you're
dealing with inventory, real time inventory.
There's a lot of very complicated pieces to making
this design work really well. And So what we've had to do is

(15:13):
we've had to architect evaluation at multiple steps in
the process. The first step is, OK, so
someone, the AI has come back and said, you know, I want to
create this type of set up in this room.
Well, First off, does it defy the laws of physics?
A funny little quirk is one thing that we found when we were
first starting to do this is that, you know, manipulating

(15:33):
objects in three-dimensional space.
Sometimes we'd have like a couchsitting on its side.
And so we had to actually instruct, you know, give
explicit instructions to the evaluating agent to say, you
know, it has to sit with, you know, the bottom oriented
towards the bottom of the space.We actually had to Orient the
physics. So that's step one.
Step 2 was, is this item actually, you know, an item

(15:54):
that's in our inventory, you know, a very specific item.
And so that was a second evaluation and critique 1/3
evaluation and critique was there are certain clusters.
If you, if you ever go to like ahome design showroom, you'll see
that they purposefully put like an end table and a chair and a
lamp and a, and a, and a client.They put them all together in
clusters. One of the things we learned is

(16:14):
that these clusters should meet certain brand criteria or brand
sensibilities. And so depending on which
company are working with, they may have different clusters.
So the clusters had to match thedesign sensibilities and the
brand sensibilities of that. So we're already in like 4
different evaluation steps and that it's that methodical
definition and methodical evaluation and instrumenting all

(16:38):
of that, that to your point, that really unlocks the scale.
Once you get that right, then you can send lots of different
requests and lots of different users through that same
pipeline. And it meets the brand
guidelines that matches the lawsof physics.
And it actually matches inventory can create some pretty
magical experiences. It's really interesting the way
you describe these eval points, putting them through the entire

(17:03):
process frequently enough. Would it be correct to say that
you are making many, many, many,many small course corrections?
Is that an accurate way to say it?
Yeah, that's absolutely true. And, and one of the most
interesting things to me about agents and I've been around AI
both, you know, kind of pre generative AI and, and now you

(17:24):
know, generative AI and agents for about 20 years.
And one of the things that's really interesting to me is once
you get into agents and agentic workflows, you're actually
creating a trail of behaviors, documents, log files, telemetry
that is at a scale that is that far surpasses kind of the
business intelligence. Like the operations logs of the

(17:48):
Business Today are all gated on manual work flows.
There's a human in every in every loop.
Well, the more that you get to agents and automation and these
automated work flows, you're actually creating more data and
exhaust to analyze the behaviorsof the agents themselves.
So it's kind of a, you know, it's kind of a, a recursion,
which is like the more agents you have, the more interactions

(18:09):
you have, the more telemetry youhave, which then you can analyse
your behaviors and you can actually refine them.
So to your point, Michael, it's an iterative cycle.
Nothing works the first time. Nothing works the first time
when we you know, from the most simple single flow agent to
handling, you know, like an inbound issue in customer
service to the most complex workflows of like trying to do
financial reconciliation at the end of the quarter.

(18:32):
You know, that has to pull off treasury and has to pull off of
systems of record and has to be,you know, match SEC rules for
disclosures. You know, that is you know, this
volume, this this analysis and being very methodical about
capturing what the agents are doing is both one of the
greatest challenges and one of the greatest opportunities.

(18:53):
Like debugging multi agent work flows right now is very very
complicated. We have a very interesting
question on LinkedIn from Stephanie Satsots who is an
account executive with Work Day and she and she says our agents
role or skills based, which is to say one agent equals one job

(19:16):
to be done, which I think is so interesting.
We'll give in the lengthy discussion you were just
describing around multi agent technologies working together.
What we see today are kind of 2 ends of the spectrum.
On one end are kind of single purpose, single task agents that
individuals themselves might build within their company to

(19:41):
let's say you've got meetings coming up this week and you're
like, you know what, I want to prep for these meetings.
Well, today it's, you know, it'skind of a singular agent to
multi data source and back through a singular agent where
you say, let's get me prepped for these meetings.
Could you tell me who I'm meeting with, what I ought to
know about these folks? And so that single agent
workflow is pretty easy to compose.
As long as you have the connectors to, you know, or the

(20:02):
data access and context access for like your calendar for, you
know, a form of CRM for even e-mail, then you can gather the
context and agents are and they are pretty good at returning.
Hey, here are the people you're meeting with.
Here's when you're meeting with them.
Based on what interactions you had in the past, what we know
about them, here's some things you might want to prep.
So on one end spectrum, they're these kind of task specific,

(20:25):
they're not really role specific.
It's more task specific execution, you know, very
quickly against known data sources.
On the other end of the spectrumare these more what you're
describing, which are these rolebased agents, which have many,
many functions that occur withina role.
It's just like if you're a financial analyst at a bank, you

(20:46):
have multiple tasks and multiplejobs that you execute every
single day. And once you start getting into
that end of the spectrum, you'reactually composing multiple
agents to execute multiple tasksin concurrency or sometimes in
the serial form. So one example of this is that
for example, in at Harvey AI, it's a legal AI company right

(21:10):
now. They have a is there's kind of
they, they have like a role based AI.
You can think about this as you're kind of like a paralegal
where this paralegal AI goes outand it'll do, it'll analyze
contracts, it'll do due diligence.
It'll, you know, it'll do all ofthis work that before, like a, a
paralegal, you know, might have to go and search through and

(21:32):
index all this information and bring it back.
The AI now can go and index thatinformation, synthesize it,
bring it back. And now a paralegal, you know,
the human paralegal is more likesteering and the AI paralegal is
more like consolidating and analyzing.
That's much more of a role basedagent.
Arsalan Khan on Twitter X, who'sa regular listener, asks this.

(21:55):
He says when it comes to AI agents, how many low hanging
fruits should folks go after until they address, quote what
he says the entire forest? And also who decides what is
holistic AI agent deployment? It depends what your objectives

(22:16):
are. The best advice I can give you
is if you personally and or yourorganization aren't already in
the building and construction ofearly agentic work flows, you
should get started right away. There is.
So that's capacity building. I've been working in advanced
technology for a really long time and I can tell you it

(22:37):
always takes longer than you think to get used to using a new
technology. And so just to even start the
learning curve just to get comfortable with the tools.
You know, it's different in the past, maybe you work with AP is
well, MCP servers and the flow of agents are a little bit
different. You know, we even now have
computer use which can bypass some of the, you know, need to

(22:59):
construct APIs in front of data.And now you can just interact
with, you know, the screen in front of you without having to,
you know, drop a bunch of semantic ties between, you know,
data sources and intent. So there's all these, there's
all these emerging technologies that if, if you haven't like
rolled up your literally rolled up your sleeves like I have, and
if you're not, you know, experimenting, then you're kind

(23:20):
of falling behind a little bit. So on that case, I would say,
you know, I talked about Gemini Enterprise earlier.
One of the great things about Gemini Enterprise Oregon, you
know, these platforms is that you can go right to the user
interface, the main page and youcan start building an agent
right away. You can and and like a Gemini
Enterprise, I know because I wasjust building 1 this morning.

(23:40):
You know, you can go in, you canspecify what type of agent it
is. You can specify the data you
want it to connect to that you have access to based on your
organizational policies and you can get up and started right
away with building a So do that right away.
Now the other question which is go tackle the, you know, the big
nasty multi agent, multi workflow kind of those role

(24:02):
based agents that Stephanie had referred to earlier.
That's something that requires multiple stakeholders usually
within an organization and usually comes from a top down
business imperative or urgent business imperative.
This could be, you know, we needefficiency in our software
engineering or we need lift in revenue.

(24:25):
And so we need more engagement, you know, at our on kind of like
our storefront from a digital consumer journey in retail, for
example. And so I would say depending on
what your role is and the natureof your company and where you're
at in the life cycle, you know, it might be a good idea to just
start building and or you might be in the place where it's time

(24:46):
to bring the finance department together, central IT together,
the line of business and actually start to construct and
build these multi agent workflows.
But it's all going to be kind oflike where you sit, where your
organization sits in a period oftime.
Let's talk for a moment about the organizational challenges
associated with multi agent orchestration.

(25:11):
In a way it's it's kind of easier to talk about the
technology. The technology is hard, but but
people are harder. So tell us your thoughts about
that. The most complex problems that
we deal with when it comes to implementation of technology at
scale are human and organizational issues.
So number one, I mentioned this in this in the previous

(25:33):
question, willingness to use. If you think about every great
technology, wave, mobile, you know, big data, AI, cloud AI,
they were all started both from like an organization's
understanding that they need to do something differently.
But usually it's by people within their own organization

(25:55):
who are experimenting with new technology.
You know, we, we call the shadowIT or, you know, kind of
consumerization of IT. People discover these
technologies, they see how it could be leveraged for their
business and they're bringing itin from the bottom up.
And so one of the things that, you know, I have been really
appreciative of at least in likeGoogle's approach to technology,
is that we really encourage folks to experiment with new

(26:17):
technology and we're always watching for where the hotspots
show up. So for example, we figured out
that AI and, you know, this kindof new generative AI wave, you
could access it through the command line interface if
there's a really, you know, a smart integration done.
So Gemini CLI, yeah, it was thisthing that we just released.
It was a small team that worked on it and, you know, it exploded

(26:39):
immediately. But it was our organization's
willingness to allow a small number of engineers to try
something and to, you know, allow it the space and time.
Not a lot of resources, by the way, that's a misnomer.
It's actually very scrappy, verybootstrappy.
But just allowing and, and fostering and being willing to
encourage experimentation is super, super important.

(27:00):
Second organizational thing thatI highly recommend is when
you're getting into agents, again, they're going to take
instructions and they need rulesof engagement and they need
explicit guardrails. And if you don't have standard
operating procedures or decisionframeworks within your
organization for how you want to, you know, how you want to
guide your own operations, you can't guide agents because they

(27:22):
can't interpret intent very well.
They're very explicit right now.And probably the last one is,
you know, we'd mentioned this atthe beginning in, in a kind of a
different lens, but there are inevery organization, there are
hidden rules, agendas, norms. You know, it's important that to

(27:43):
understand that if you ask software to do something, it
will do it. If you don't have software to do
something, it won't do it. And as you're constructing, you
know, agents within a large organization, a lot of the, a
lot of the, a lot of the outcomes that people are
disappointed with, you can traceback to.
They just assumed that would be able to make a logic leap that

(28:06):
wasn't explicitly given to it. And that's a different way.
You know, today, a lot of what the way that like managers and
leaders and organizations work is they trust the humans that
they hire and they put in these positions to bridge the gap
between what is stated and what is unstated and what's needed
for the business. And so being really aware of
that and, and getting in that culture of experimentation will

(28:27):
offer will often surface hidden rules and norms that the
organization didn't even know that they had, because the
software will break. You're the CTO of Google Cloud,
and we want the AI to intuitively know and understand
our implicit rules of engagementand our implicit culture, so we

(28:53):
can just let the software do itsthing and help us.
Think about it this way. If you receive Gemini, for
example, Gemini has been trainedon, you know, an enormous amount
of information that is accessible to everyone.
So if you're looking for outcomes that are very specific

(29:13):
to your industry and or your company, then it also requires
pairing this AI with grounding or other data sources that will
allow it to understand the unique context of your business,
your industry, or your use case.There are no shortcuts.
So you know, these frontier models need, and I think the
biggest opportunity for organizations is to bring their

(29:35):
data, their contacts, and their understanding partner with these
frontier models. And it's amazing AI that's being
provided and create something that is specific to their
organization and their industry.And that's where differentiation
and that's where competitive advantage lies.
It doesn't lie in accessing the same model everybody has access
to. It lies in combining the power
of the incredible power of these, you know, AI models that

(29:59):
have a general understanding of intent based on the data they've
been trained on. But you might have very domain
specific language within your industry.
So for example, I spent a a bunch of time in manufacturing
and industrials. You want to talk about a place
that has its own language. You know, there are terms, very
specific terms that mean very specific things.
And AI isn't necessarily going to be trained on what those

(30:21):
terms mean. And so it won't have the ability
to bridge, you know, like that semantic layer.
You're going from general interpretation of how of what a
term might mean to a very domainspecific term.
This also shows up in code. A lot of organizations have
built their proprietary softwarelanguages.
You see this a lot in financial services and healthcare

(30:43):
companies. And the AI wasn't trained on
that data out-of-the-box. So as an organization, it's that
combination of bringing your domain specific data and
language and understanding intellectual property and
combining that with this AI in aprivacy Safeway that creates the
real, the really big outcome. So, Michael, I hate to

(31:04):
disappoint you, but you know, you it, it, it requires these
organizations to do both to leverage that AI, but also, you
know, using a platform like Gemini Enterprise, bring in and
ground it in the realities and the specific knowledge of their
industry. This is a question again from
Anthony Scrifignano. And then we're going to move to
some new folks. But he's asking about the role

(31:28):
that active inference can play in all of this, to make agentic
AI better, more useful, and so forth.
The more inference and the more this kind of like this loop that
needs to be created is going to be an opportunity for
organizations to continuously improve the quality of their

(31:49):
agents. And you know, hill climb on
performance. And you know, one thing having
worked in AI for a while too, isit's, it's a never ending
journey. Like training AI isn't the end,
it's the beginning. And having the sensibility about
continuous improvement and continuous loops of of training

(32:11):
and feedback are really important to making agents
improve their outcomes over time.
This is from Justin Kavanaugh and he's asking what are the
most practical ways that small businesses can start integrating
AI at the infrastructure or datalevel, not just to save time,

(32:33):
but to future proof how they attract customers and compete
with enterprise organizations sothat they are not left behind.
It's a really important question, actually.
The most practical method is to just leverage the native
capabilities of the cloud. So for example today in Big
Query in Google Cloud, we have native AI integrations and

(32:54):
native AI inference in the data system itself.
And I think trying to bolt it onis a is a costly and and time
intensive proposition. And so for the smaller to medium
sized organizations, I would sayleverage the native capabilities
of the vendors and the cloud partners that you have.

(33:15):
And in Google Cloud's case, you know, if you need intelligence
baked in to the data systems, you know that already comes
standard and what we provide. I just have to amplify Will a
comment that that you made earlier, which is just get
started. If you're a small business, the

(33:38):
more you can gain familiarity with the kind of services, for
example, that Will was just describing, the better off
you're going to be. And you'll then you'll learn and
you'll know how to how to take those tools and apply them to
your specific business. And so much now like in our
platform, for example, AI is everywhere and embedded into the

(34:00):
services themselves at every core component.
So it doesn't matter whether youknow you're talking about a
storage system, a database tier.If you're talking about even
like compute and how we optimizecompute for specific jobs, you
should really just leverage the cloud providers integrations
natively of AI into these into every tier that might be

(34:21):
supporting business applications, your website and
what have you. We have a question on LinkedIn
from Kenroy Benedict, who says, do you see an increase in public
services using AI outside of simple chat bots?
And I'm not sure whether he means public cloud services or
in the public. Sector so as yeah, the founder

(34:44):
of Google public sector and a board member still, which I'm
very proud of the explosion of AI in in public services is
starting to happen. And I'll give you you know, one
example that is I think really, really cool and that is one of
the one of the most important functions that public sector
organizations fulfill is that they provide help when people

(35:07):
need it. And so, for example,
unemployment benefits at the state level can often be or at
the end or at the federal level,but unemployment benefits are a
really big deal because this is a person's most vulnerable time.
And one of the things that AI has enabled states to do with
the state of Wisconsin and others is instead of having

(35:30):
someone submit an application for benefits and going back and
forth with a bunch of paper and that taking, you know,
potentially months, meanwhile, this person is suffering.
They don't have access to the resources they need.
It's a very, it can be a very, very significantly, you know,
painful time for an individual and their families.
AI has enabled interactions and the processing of claims, for

(35:54):
example, to go from, you know, weeks to hours or days through,
you know, the initial submissionof information to AI, triaging
the severity inherently of the cases, pushing, you know, top
cases to the top, providing initial recommendations.
And, you know, all of those kindof automation speed up steps are

(36:17):
the difference between, you know, thriving and, you know, at
least getting back on your feet or not.
And we're going to, you're goingto see a lot more in public
sector services at the federal, state and even internationally.
Greg Walters on LinkedIn says hesees a world where all
applications and functions are contained within the LLM and the

(36:41):
AI. What say you?
And I'm going to ask you to answer that pretty quickly.
Wow, that's my answer. I, I love the vision.
I do think that one of the more exciting capabilities of these
models is their ability to provide tailored user interfaces
like we call them like ephemeralapps or ephemeral UI.

(37:04):
And that means that the AI becomes the UI and that the
application UI is less important.
And so in that way, I do agree with the vision that so for
example, if you have access, if the LLM has access to the data,
has access to the user intent, we've seen behaviors where AI
now can spin up the user interface ad hoc and create what
is essentially an ephemeral AI app immediately.

(37:26):
We have crazy question, a good question from Arsalan Khan on
Twitter X he says, and very quickly, please.
He says these AI agents are likesoldiers who will follow orders.
Who will be the generals, colonels or even military police
to provide leadership and guidance to these soldiers?

(37:48):
Should that be high level AI agents too?
Well, it will depend on the stakes of the workflow that
you're dealing with. So for example, if you're
dealing with Transportation Safety, those are always going
to be human in the loop, human monitored workflows.
But if you're dealing with, I'm trying to create 3000 short form
videos for certain brand outcomes to go create an

(38:09):
advertising campaign, you don't necessarily need to have a human
in the loop because of the stakes of getting something
wrong. So think about it in terms of
stakes and what happens if you get something wrong.
And you can probably back into which work flows are going to be
more human in the loop and whichare going to be more agent
driven. Let's talk about deployment of
agents in the in the enterprise.Can you describe the common

(38:34):
patterns of successful agent deployments and AI in general,
and again relatively quickly please?
#1 success criteria is picking aproblem that is actually likely
to be somewhat solved by agents and higher forms of automation
and faster task task execution. So a good example of this would

(38:56):
be in financial services. You know, we're working with the
bank right now that has seen thethat has speed up their research
function. So now they can go cascade
across all the documents and information that they have and
bring back to analysts and customer relationship managers
synthesize pieces of research ina matter of hours or minutes

(39:19):
where it used to take multiple days or a week that research to
come back. And so that was a problem.
They knew that they had the data, it was very meaningful to
their customers, which is number2.
And so specific problem had the data meaningful to their
customers and it was something that would get better over time
iteratively. So those are kind of key

(39:39):
components that we see over and over again.
What are the most significant challenges that you see as
organizations are trying to deploy agents?
Where do they run into trouble? Lack of data, lack of context to
the agents in the models is the number one trapdoor.
A second one is realistic expectations in the early going.

(40:05):
These are iterative loops and many organizations like to like
to set up projects so that they're, you know, if they don't
get it, they're used to being really like experts in their
field. You know, you can think of like
aerospace, very precision oriented manufacturing.
It's important to have a culturewhere you understand that an

(40:26):
agentic software development, the first few iterations are
probably not going to have a quality or an efficiency that
you really love, but it's in theiteration and the fast iteration
that you get the results that you want.
And so that's probably the second trap door is the way that
we construct projects for success out-of-the-box in many
cases is exactly the opposite ofthe way that these projects work

(40:48):
and will make agent development projects successful.
I will also say the third reallyimportant factor Michael, and
this goes across whether it's agents, AI or G or even
technology projects in general is leadership modelling the
importance and get getting involved in this.
The leaders that and I'm talkingabout CEOC suite senior leaders

(41:10):
rolling their sleeves up. It was a, it was a very
important moment to all of us atGoogle and I think, you know,
signal to the market when, you know, our CEO Sundar was asked,
you know, hey, you know, what are you doing with AI?
He's like, I'm vibe coding and he went into a long exposition
of what he's actually doing withAI.
What that does is it says, I'm doing this, we're all in this

(41:30):
together. I, I'm committed to this.
We're committed to this. And I can't, I can't emphasize
enough how important the leader modeling the support of this
exploration, this kind of new wave of exploration, how
important that is in every in every way to agent development.
But you're really forcing organization, business leaders
therefore to become technologists and the CEO or

(41:56):
CFO. Obviously Google's a special
case because you're developing these products, but the average,
you know, manufacturing CEOCFO, is it really realistic to expect
them to get into the guts of? Absolutely, yeah, absolutely.
And they all want to and see, that's the great part is my job
is to make it so the technology is mostly invisible.

(42:19):
So instead what they see is a platform, you know, like again,
Gemini Enterprise where they cango and they can just in plain
language scope an agent, give ITsystem instructions, give it
initial prompts, click button ondata sources and build an agent.
Now, it doesn't mean that CE OS necessarily all CE OS should be
out there building multi agent complex work flows for, you

(42:43):
know, highly specific tasks. But it's my firm belief that
they're all capable of participating materially in this
technology wave. And that's one of the things
that makes it so powerful and soubiquitous, Michael, is that AI
has gone from a department or a very like weird and mystical
technology to part of everyday life.

(43:03):
If you type on a keyboard, a virtual keyboard on your phone,
you've got AI under the hood that's anticipating the words
that you're trying to type and trying to serve you up a word.
Any search that you run every single day, it's all around you.
And for the first time in my career, the platforms to
actually deploy advanced AI are accessible to everyone.

(43:24):
Can you talk about nano banana? Explain what nano banana is?
And I have to say, I just think it's incredible.
So please just just briefly tellus about Nano Banana.
So Nano Banana is our image generation model, latest image
generation model. And this is a big theme,
Michael, and that is that multimodal AI is the future for

(43:48):
a couple reasons. Number one, if one picture is
worth 1000 words, a video is worth a million pictures.
And as humans, how we experiencethe world is through our senses,
sight, speech, sound and AI. Now these multimodal AI models
like Nano Banana can take imagesas inputs along with, you know,

(44:12):
prompts and it can create new things.
You know, if you if you take Nano Banana and the ability to,
you know, create images and you extend it, we're able to create
videos as well. VO is a video creation model
that we have under the hood of multimodal model extended.
Even more, we have a Gemini Live, the API, and I mentioned

(44:33):
that that use case earlier of being able to take your phone,
flip it around, show it something, show the AI
something, and it knows inherently that that's a cable
modem. This is the model and you can
start your debugging right away.That's nano Banana is a glimpse
into the multimodal future of AIand that's how humans want to

(44:54):
interact with AI. They don't want to have to type
things all the time. Sometimes they just want to show
you something. So you know, hey, here's what
I'm looking at, you know, give me some background on, you know
this and it can explain mathematical equations, it can
explain geolocations. That is, that's the future of a
is is multimodal immersive experience and nano bananas one

(45:15):
form. Elizabeth Shaw says how can you
tell what's real from the hype with respect to AI?
Then how can you turn that into something that's practical?
Measure, measure, measure and bevery transparent.
I even have to coach my own teamon this.
When you're building something, the natural human tendency is to

(45:38):
show all the best parts of it and to talk as though you know
everything is great in our team.And what I would encourage for
all of you that are working withAI and especially building
agents right now is it's possible, but it's not always
easy. And it's important to show the
initial is, is to gain the credibility and be very

(45:58):
realistic about how it's going and measure incrementally.
We break, we break long agentic development workflows into very
incremental steps. And we measure and we talk about
what we need to do to hill climbon performance and or efficiency
at every single step rather thansaying, oh, it's going to be
able to do all this great stuff.We start with very atomic, very

(46:19):
specific and very measurable outcomes we're looking for, and
we make a decision at each one of those gates about whether to
proceed, whether to change or whether to stop.
And that type of transparency inevery step of development and
that very sober look at what it can and can't do will help bust
the hype cycle. Also, for those of you that are
leading projects, highly encourage you not to make big

(46:39):
statements about what this aboutwhat AI is going to do and
instead think very soberly aboutone of the biggest problems that
your organization is facing. And how might automation speed
multi modality? How might those things combine
to chip away at solving? You know what are always going
to be some really complex problems that aren't you know

(47:00):
you're not going to solve through magic AI wand.
Very, very good advice. You're, you're saying
essentially approach it from a very practical standpoint about
what problems you need to solve rather than AI is great and
we'll change our lives and solveeverything in the world.
Yeah. And I will also say that in the

(47:20):
history of technology, we alwaysunderestimate how profound the
changes are going to be and we overestimate how fast they're
going to happen. And I think that also creates
some of this hype cycle that you're referring to, which is
now we can see the potential, but that potential will take
years to realize. To the point earlier, Michael,

(47:40):
if you don't get started now, you don't climb the learning
curve and you don't create the incremental successes that
eventually lead to the breakthroughs.
And you're always watching somebody else do it.
Where is this all going in the next 6 to 18 months?
What can we expect from your vantage point, from what you
know right now? First off, the models will
continue to improve in capability, in quality and

(48:03):
you'll see more multi native multimodal capabilities.
Just like today, you know where you can go to Gemini and you can
interact through voice, you can interact through video, you can
interact through images. That multi modality is going to
get more and more seamless and it's and it's going to feel more
natural to interact with AI. Second, a continuous improvement

(48:27):
around the out-of-the-box that are available to help people get
started and Gemini Enterprise more and more connectors to data
sources. So you know, for those of you
that have, you know, SharePoint or JIRA or Confluent or you
know, all these different, all of those connectors and many,
many more are coming. More improvement around
governance and security and especially in where value is

(48:51):
transacted. So today, many of the agent
workflows that I see, they're not commerce transactions, but
we just released the agent payment protocol AP2 as part of
this kind of standardization stack.
So MCP A2A, which is the agent to agent protocol and now the
agent payment protocol, because we're going to want agents to

(49:12):
fulfill tasks that include commerce.
And that is going to be a significant area of focus is
making sure that that's done well, it's done safely, it's
done securely. And AP2 Agent payment protocol
is the first step in that journey.
And with that, I'm afraid we're out of time.
Will Grannis, Chief Technology Officer of Google Cloud.

(49:34):
Thank you so much for being withus, and I hope you'll come back
another time. Yeah, you got it, Michael.
It was a real pleasure. Thanks for the questions
everybody. And everybody, thank you for
those great questions. Before you go, I want you to
write this second. Go to cxotalk.com, subscribe to
our newsletter. We have truly great shows that
are coming up and we want you tojoin us.

(49:57):
Everybody, I hope you have a great day.
Thank you guys. Are you guys ask the most
amazing questions. You guys are awesome.
Thank you so much. Take care, everyone.
We'll see you soon.

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What is Google's Agentic AI Strategy? (Explained by Google Cloud's CTO) | CXOTalk #897

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.css-14f5ked{margin:0;word-break:break-word;display:-webkit-box;-webkit-box-orient:vertical;box-orient:vertical;-webkit-line-clamp:2;overflow:hidden;}What is Google's Agentic AI Strategy? (Explained by Google Cloud's CTO) | CXOTalk #897