What are AI agents and will they really change the world as some
folks claim? Today on episode 868, we're
taking an advanced look at agentic AI with a prominent VC
investor. Praveen Akiraju is managing
director at the venture capital firm Insight Partners, where
he's focused on AI agents. This is his second appearance on
CX, so talk to discuss Agentic AI Praveen.
Welcome back. Thank you, Michael.
Excited to be here. What are AI agents?
You can think of AI agents as application software that is
able to understand a user's intent and be able to reason on
that user's intent, come up with a plan and execute the plan.
At a broad level, that's really what an AI agent is.
So today, if you kind of look at how that task is done, you
typically have an application, you have a human who comes up
with the plan and executes the plan, right?
So the, the transition that we are in the process of making
with AI is that we're now offloading a lot of that
planning process and the execution process to these
agents and allowing them to use applications as humans would,
right? So that's really the paradigm
shift that we are in the process of making.
That's why AI agents are so exciting.
What is the role of LL miss large language models on this
whole agentic AI world? It's important to understand
that AI agents are not just all about large language models,
right? You have to think about AI
agents as essentially another software application which now
incorporates large language models in in areas where they
add the most value. So let's take a step back and
think about sort of how applications were previously
defined. You used to have a database, a
system of record. You used to have a software
workflow that was built on top of this.
And then you had a user interface.
That's typically what your classical application looks like
today in the SAS context. So the power of large language
models is that they are able to insert into the stack at various
different points and be able to kind of dramatically improve the
productivity and the capability of these application software
models. So you can think of large
language models as playing different roles in in in the
sort of new paradigm we call AI agents.
The first layer would be the user interface itself.
You know, we are all now used to the ChatGPT interface where we
go in and you essentially type in something or you can even
speak to it and you know, it's able to kind of understand that
context and comes back with a an answer as opposed to, you know,
set a reference points or the 10 blue links, right.
So similarly, in the application context, you have a user
interface now where the user can interact and basically say, Hey,
show me the data for a particular region for a
particular product and it and that LLM is then is able to take
that input and synthesize that and understand the context of it
as opposed to the user having to go and figure that out, right.
So that's one layer. The second layer, which is
really important layer and it's kind of in some ways sort of
almost the core of an AI agentic architecture is a reasoning
layer, right, which is the ability to take the task given
to the agent and break it down into a set of sub tasks.
So for example, if you're saying let's go build a research report
on a particular stock, right? So if that is the the, the
prompt that the user provides, the AI agent then takes it down
and says, OK, how do I do build a research report on this
particular stock? I have to go to, you know, Yahoo
Finance or one of these public websites, get all the
information about that stock, be able to kind of synthesize all
of that information, create a report and then publish a
report. So it breaks that down to
specific tasks. That's the reasoning layer,
right? The next layer where the LMS
play a role is the ability to go out and dynamically query
information. So in the past sort of
application context where things were static, the application
could only look at things where it had an API called built in.
So if it had access to a particular data store and had an
API call, it would go pick that API call.
Here we have an LLM that can reason and say, wait a minute,
this information is available to me externally.
This information is available to me through this API call
internally. And I need to get both of these
pieces of information in order for me to execute my task which
is to build this research report.
So let me launch a a web search API.
Let me also launch an API to my internal research store, put
those data points together, right?
So it's, it's the LLM has the ability to understand what
sources of data needs to go get the information from for it to
execute on the task. Now once it does that right,
they, the application software synthesizes it and then you come
to the next step. It is.
You have the output. A lot of really well designed
agents have strong evaluation loops and this is really, really
important, right? Once the agent actually comes up
with the output, you test that output against, you know what
essentially is the gold standard that you set up and say this is
how a research report looks like.
So the agent compares the output it got with the gold standard
and says, does this match it? And then it corrects itself.
Did it matches that standard, right.
So as you can see, you inserting the large language model at
different layers of the AI agent in order for it to be able to
make the entire process a lot more dynamic and interactive.
And that's basically what is different between a classic
application flow and AI agent application flow.
So we have this reasoning, we have access to this broad body
of human knowledge, and of course that is different from
traditional applications. How accurate are these agents
today? What is the state-of-the-art?
How useful are these agents in practice?
We are quite early in this journey.
You know, it feels like when you read the press you have, you
know, AI agents are everywhere, right?
Or if you look go look at a website today for any software
company, essentially AI agents are the way they are now
expressing their products. However, I think in in terms of
the maturity of the technology, both from the the important
question you talked about how reliable they are, but also in
terms of on the customer end user side, the experience of the
end users in terms of the deployments and the scalability
and reliability of these. We're still in, in the early
days now. I will say that there's a
spectrum of your agents, right? So in, in, in the spectrum
essentially, you know, at one end of the spectrum, you could
have these consumer agents which are really focused on
individuals. You know, we were talking about
this before the show started opening.
I just launched their consumer agent called Operator.
Very interesting. You know, it's for us as
individuals part of the pro plan and the other end of the
spectrum are essentially the next phase of evolution of RPA
or robotic process automation right in the enterprise.
So agents that take on complex enterprise work flows, right.
So we have tremendous amount of activity all along the spectrum
in terms of, you know, start-ups as well as in comments advancing
the state-of-the-art right experimenting the I agents.
So we are in the early days primarily because there are a
few things that we are still trying to figure out right now.
Large language models by themselves are non
deterministic. And, and I think what I mean by
that is that and we all, we again, we've all experienced
this. When you ask ChatGPT a question
a certain way and you ask the same question in a slightly
different way, you may get a different answer.
Right now that's getting better with some of the newer models,
particularly some of the the reasoning models that are able
to correct themselves. But the fact of the matter is
that the core of the large language model is this sort of
non determinism. And so a lot of the AI agentic
designers today, builders today are working on in what we call
scaffolding in order for us to essentially take the power of
these large language models and harness them.
At the same time, making sure that we're able to understand
that the non determinism exists and we have the right
architecture to be able to handle that.
So you get a reliable, consistent and most importantly,
scalable AI agent. Where are we there?
Because of course that non determinism, if you press enter,
submit again on a prompt, it's going to give you a different
answer. Is great if you're wanting help
writing an outline or some type of summary of a document because
there's different perspectives you can take.
But if you want it to run a task like book me an airline ticket,
you don't want a lot of stuff all over the map.
You just want one thing done. You want that ticket to be
booked in the right place with the most efficiency and so
forth. So it becomes a big problem I
would think. Yeah, and I think this is again,
a central design consideration, right?
When you're building AI agents. And, and I think we can maybe
break this down into like 3 parts.
So the first part is the task itself.
And you gave an example of a task, right?
So how, how important is it for you to get the task?
Absolutely. Right Now in ChatGPT, you know,
if it gives you a slightly different answer, it's like
search, right is you're getting information, you're not
essentially making a decision. So you're OK with some level of
non determinism because the human mind is able to correct
for that, right? The other end of the spectrum,
if you're essentially betting on this AI agent to execute a task
consistently, it could be, you know, a enterprise workflow and
a back end workflow, right? It could be code generation or,
you know, you're essentially a customer service interaction
agent. You cannot have that level of
non determinism. So, so that's one thing like how
do you define the task? And I think that's a, a key
question to ask. The second aspect that you want
to look at is, OK, now that you say, let's say you, you have a
task that you need to be accurate.
And it's also again, important to understand that AI agents are
not all about just LLMS. There's a lot of existing
software, there's a lot of reusing, you know, machine
learning models, predictive models, which are deterministic,
right, as well as the classic things that you as a software
engineer do to build applications that go into making
an AI agent, right? So I'll say this again, AILLMS
are a tool. They're not the product, right?
They're not the agent, they're a tool.
So you have to understand, as with any tool, what the
capabilities are. So how do you so that's, that's
the second piece. It's like when you think about
the AI agent it you have to think about leveraging the
right, the LM in the right places.
Then the third piece is OK, so now you've figured out, OK, my
large language model is going to help me with building a plant,
for example, the reasoning layer that we talked about earlier.
So most AI agents today essentially propose a plan and
you have a human in the loop that then approves the plan.
So a good example of this is a lot of the coding, you know, Co
pilots and coding agents that you have today.
They're very again, and particularly with the the the
cloud Sonnet 3.5, that was like, I think a step function jump in
the ability of large language models to accurately generate
code, right? The way they work is
essentially, you know, the programmers interacting with the
large language model. It proposes a plan which is
approved and edited by the programmer before it actually
goes out and executes it, right. So the so-called sort of user in
the loop, human in the loop is a very critical design component
today in AI agents, particularly in that planning layer right
now, there are other things you can do.
And we talked about part of this, which is, you know, what
we call like a, a reflection loop.
So once the output of the agent comes out, you tested against
another large language model which is trained with the right
output. So the model, the agent
essentially tests itself to say, did I get this right?
And it's able to kind of think on whether the output is correct
and then make those changes and go back and iterate again,
right? So these kind of reflection
loops, the way you build evaluations, which is how do you
determine the output is correct? And using that data to
continuously improve the AI agent is again part of the
design process. So I gave you like 3 different
things where you have to consider.
There's a lot more we can go into in terms of depth, but at a
broad level, you know, the take away is that you can and design
AI agents to be deterministic. Assuming that you understand the
task right, you provided the right scaffolding, the
evaluation loops, and you essentially involve the human in
the loop. Today, AI agents work well when
you have a human in the loop, and I think that's going to be
the case particularly on this end of the spectrum where output
needs to be, decisions need to be a lot more accurate.
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we have great ones. We have questions that are
stacking up on LinkedIn, So let's jump to a few questions
right now. And the first question is from
Ravi Karkara. And he says, where do you see
American universities on creating a skills workforce for
the AI driven world economy? How will they learn to work with
and deploy AI agents? I consider AI large language
models to be a tool, right? Just as you know, we had cloud,
mobile, all these fundamental platform shifts, the AI and
large language models are a tool to help us accomplish our task.
So what I mean by that is it still is important for you to
deeply understand what is the problem you're trying to solve
with this particular tool. Are you trying to book an
airline ticket like Michael you had mentioned earlier?
Are you trying to execute a payroll function in in an
enterprise? Are you trying to respond to a
customer support question? So understanding the actual
task, which is what, you know, essentially good product
management is, is foundational to understanding how this tool,
this new tool, much more powerful, obviously dramatically
more impactful than anything that we've seen in the past, is
going to change how we as a we as basically educators, as
knowledge workers or as consumers leverage AI.
So in terms of, you know, how universities approach this, it
depends on sort of how you participate in this.
I mean, to one end, the spectrum, you know, the, the
education in, in sciences and math and, and good grounding in
that helps you be part of the design process of this.
On the other end of the spectrum, you know, if you're
more business oriented, you know, a deep understanding of
your problem, how and essentially how the tool works.
I mean, we all are experimenting with GBT today.
I use it differently, My daughter uses it differently.
You know, I hope hopefully when none of her teachers are
listening, but you know, she uses it sometimes to do help her
with her homework, right? And so she's learning as part of
that process just as I'm learning to use it for my use
cases. And so are all of us, right?
So I think it's a tool that we experiment with, but a deep
understanding the problem and figuring out how to understand
how to use this tool is, is foundational.
So, you know, product thinking is I think is another key
aspect, which I think is something we should emphasize.
You know, if you're not deep in the algorithms in math, you
still have a tremendous role to play by understanding sort of
how do you build products, how do you solve customer problems,
right? And I think the third aspect of
this is there's a huge human element to this.
We just talked about sort of how do AI agents function today?
And one of the things I said is human in the loop, right?
And you know, real AI agents get better at reasoning probably.
I mean, the O3 model is amazing, right?
And you've seen huge advances just in the last, you know, few
weeks last, you know, later part, later part of last year in
terms of the reasoning capability.
However, throughout the steps that you have to think about
where the role of the human is and at is it an evaluation
function? Is it a reasoning function?
And so humans are always going to be involved and being able to
sort of understand and engage with the technology is
effectively the most important thing, right?
There is a massive human side of this as much as there's a
technology side of this, right? So those are some of the areas.
And, you know, I'm not an educator.
So as I approach this like, that's the way I think about it.
This is from Suresh Babu Madala and he says do we need to train
agents or do agents interpret the question as a step by step
task? That's a great question.
You do need to train agents. Again, there's a spectrum of
what these agents can do in terms of simple tasks, which is
really complex tasks. And the level of training
obviously will depend on what you're expecting the agents to
do. You know, the first phase, as
you're inserting the agent into a particular task, there's a
certain amount of grounding that needs to happen.
And the grounding typically is, you know, connecting it to the
right data sources, connecting it to the right application
sources, connecting it and, and understanding, making sure that
understand and grounded in the policies of your particular use
case or your particular enterprise.
So that's the initial part, however training, you know, just
like we as human beings, right, we're constantly learning,
right? And if you, let's say you
onboard a new employee, you know, fresh college grad, they
are in a constant training process, right?
They learn you have somebody who watches their output and you
give them feedback, right? And you hopefully you observe
them continuing to improve. It's the same exact thing for AI
agents. That's why these evaluation
loops are so important aspect of designing an AI agent.
I can't say that can't stress that enough, right?
You have to be able to kind of constantly understand the output
of the agent, figure out where you can correct it and continue
to improve. And I think the the last part of
this is the observability of how these agents are functioning,
right, where are sort of the broader efficiencies and
inefficiencies of what they are doing and not doing is a
constant part of sort of your architecture.
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Our next question is from Greg Walters.
And you kind of address this a little bit.
But, he says, can't non determinism be prompted into
existence? The first aspect of this is to
really understand, you know, the role of the LLM in your task,
right? And what is the level of
reliability that you expect from an LLMS task?
Now there's certain things where the capability of the LLMS is
getting constantly better and that you know that there's a
reason why a lot of large language models are
fundamentally trained on math and on coding tasks, because
there's a lot of transfer learning as they get good at
coding, as they get good at math, they're also able to get
good at reasoning tasks, right, which are much more broadly
applied. That's why there's a lot of
focus on those tasks. So, so the first point I'll I'll
say is these models will progressively get better at
managing the hallucinations, whether it is through reasoning
loops, right, or whether it is through better post training of
the models in your deployments or whether it's inference time
reasoning, right, which is sort of another, you know, scaling
level that we now have. There are different techniques
right in the model itself, which allow you to decrease the
aperture of the non determinism, right?
So that is one vector. The other vector is, as I
mentioned earlier, building a scaffolding around it,
understanding that you're going to get an output from the large
language model that needs to be synthesized into something
that's a little bit more reliable, right, and gets to the
level of output that is acceptable for you.
So that's a reflection loop. That's your evaluations and
that's, you know, and sometimes you may just have a
deterministic runtime that you need, right to design to the AI
agentic workflow. And Gus Speckdash says it's
interesting that Agentic AI goes around the ridiculously
frustrating prompt user interface that is not integrated
with any workflow. This is huge.
What are your thoughts about Agentic AI?
Simply around the user interface and integration with workflows?
This is the quantum leap in my opinion, right, In terms of the
user interface and user engagement.
In a lot of ways, I think we've had some form of natural
language processing, NLP, right type interfaces.
You know, you can think of chat bots today.
It's hard to escape them. You know, if you're trying to
book a ticket or anything, like the first thing that pops up is
a chat bot that's trying to get your information right.
So that's natural language processing it.
You know, it's able to understand voice, translates it
into text, you know, does a search and responds back to you.
I think the opportunity with large language models is the
ability to infer context, right? What we had in the previous
generation of chat bots was a literal translation, right?
And a static sort of rules based interpretation of that
translation. So what large language models
have now, because they've trained on like the entire
corpus of human language data is they can infer context, they can
infer tone, they can infer, you know, the particular sort of
intent and they're able to then appropriately translate that
into into their query, right, and get you back a response.
So I think it's game changing that you would have a large
language model in a user interface perspective.
Now remember, like we're still today, still very text based,
right? Most of ChatGPT interactions,
though, they have voice mode, which is amazing, right, are
still text based. But think about the ability for
us to be multimodal, right? Our ability to do voice which
were there today, ability to input images and right, which
we're going to get to right. And over a period of time these
models will effectively get to this point where we call they
have a world understanding, understanding of the world
model. And I think that could be game
changing in terms of how we interface with this technology.
And this is from Arsalan Khan, who asks a very interesting
question. He says we want bias to be
removed from data when it comes to AI.
How do you remove human bias if humans are in the loop?
And who decides what's a bias or not?
It's a really interesting point. If our vision eventually is that
as some companies have articulated that every company
has an AI agent and that's sort of the first point of interface
for a customer interacting with the company, right?
Let's say you're an insurance company or you are a, you know,
you're even like a government service, like maybe the DMV,
right? At some point that interface is
really important. So I, I think look, they
obviously the model companies are doing a lot of great work in
making sure that we are conscious about bias.
Now it is a fact that that is not a perfect solution yet.
We're not quite there yet in in certain instances, you know,
you're not getting these perfect answers.
So part of this is the context that I talked about in terms of
how you ground the agent is really important.
And So what does context really mean?
Right? Context means examples.
And so if it's a customer agent, for example, you could train it
on the policies that you have. You know, you probably have a
lot of voice recordings of existing agent calls, right,
that are great examples of how to handle situations in bias or
confrontational situations. So the training of this agent,
grounding it in the policies, right, and the rules of the
particular use case is a particularly important task.
Now, I think look, the human in the loop is really about sort of
how you reason things. And obviously look, the way a
human interacts ideally is a positive in terms of our ability
to eliminate bias, right? By inserting human in the loop,
you're actually adding a step that improves the ability of the
agent overall to be, you know, to correct it's biases.
But I would say, you know, good training, grounding and
policies, right? And obviously, you know, having
responsible humans in the loop are the things that will help us
get there. But it's an imperfect process,
and that's why I say it's early days yet.
Arsalan Khan comes back and he says if subject matter experts
train AI to create AI agents, would we really need the subject
matter experts? What happens when AI agents come
across a scenario that they haven't encountered yet?
Training is a constant process for AI agents, right?
What you get when you initially start this process with a
subject matter expert helping you ground the model, ground the
AI agent is you're giving it a certain rubric.
You're basically saying like, hey, here's basically what is
expected, right? Here's a basic task.
Here's how you perform the task right now.
The task evolves. We are in a dynamic world,
right? Let's say you're in a company,
maybe you launch a new product, maybe you expand into a new
region. You know, maybe you have you,
you make an acquisition or you have new employees on boarded,
right? It's a, it's, it's a process of
constant change. So to some extent, I think you
can design the AI agent to say, OK, I can expand into a new
geography. This is how I understand it,
right? But they may be different
policies. I mean, as it's often the case,
if you're operating in a different country, they may have
their own, you know, regular rules and regulations that you
need to, you need to now kind of ground the model in and such
like. So I think the subject matter
expert is really, you know, as, and we all do this, right?
We're all subject matter experts.
We're not static, right? We're constantly sort of
learning, evolving, understanding, right, new
technologies, new pieces. And it's the same thing for the
model. So yes, the idea of the AI agent
is to take away these sort of mundane things, OK, go get this
document, put 10 documents together, create a research
report, right? So that yes, you don't need to
retrain that thing. But being able to say like, OK,
how do I operate in the European Union, which may have a
different set of rules, or in in Asia, in, in Japan or in China
or in India, right, which may have a different set of rules.
Those are things that there's a certain amount of requirement of
understanding those rules and regulations that need to, again,
you need to kind of train the agent on, right.
Let's start talking about business.
And Mario Garcia asks. He says it's inspiring to see
the impact of AI in Fortune 500 companies.
What insights about this stand out to you the most?
A lot of these large companies today, I mean, it's been amazing
to watch how rapidly, you know, both sort of large companies as
well as incumbents and start-ups have really embraced AI and
large language models. And it's largely, I think the
ChatGPT moment which unleashed this because it, you know, it
was so accessible. So you take a step back.
You know, AI has been around for a long time, right?
You know, I, I did a course in neural networks back in my, you
know, grad student days. What changed I think in this
generation is that today really powerful complex AI models are
available on the other end of an API call, right?
So that level of simplicity in terms of access to really
powerful technology is essentially what enabled us to
unleash large AI as you see it in in a lot of these use cases.
Now I will again caution you to say that we are still very
early. If you talk to a lot, a lot of
these large corporations, they do have large language models
integrated. Most of them have rolled out,
for example, some form of a coding copilot.
They've rolled out some form of, you know, customer support
function. They've rolled out some form of
an analytics sort of use case with large language models, but
we're still not deployed at scale, primarily because we're
kind of still tuning, tweaking, right, learning in terms of how
do you manage these AI agents? How do you manage biases?
As one of your audience members just asked, how do you make sure
that they are current, right? How do you make sure they don't
hallucinate? The most important thing of the,
and this is fundamental, right? We all know this.
It doesn't matter. There's a lot of really, you
know, fun, exciting, interesting demos on, on, on X, right?
And that's, those are like, great.
You can say, oh, wow, this agent can do this.
What's really important is can it do it consistently and can it
do it at scale? And so those are the questions
we'll answer this year hopefully, right?
And that's why we're so excited in 2025 about the trajectory of
these AI agents. Praveen, you and your team put
together what you call a market map of companies involved with
AI agents. Can you tell us about that?
And I can bring it up on the screen so everybody can see.
And there it is. Praveen, can you talk about this
market map that you've put together?
The market map is a dynamic living thing in the sense that
it will evolve constantly, primarily because we're seeing
so much activity, right, and energy around, you know, the AI
agentic space. So what we tried to do is to
basically construct this in, in layers, right?
So there's, there's like a foundational layer where there's
a lot of these kind of data sources, integrations and such.
Like there's this new sort of bucket we, you know, we call
sort of the agent computer interface, which is the ability
for AI agents to use computer, you know, tools, right?
And you know, it could be integrations, it could be, you
know, web tools, some of the stuff is integrated in models,
some of these are, you know, you have interesting kind of
platforms that are created for this.
So we try to kind of construct the model as, you know, layer by
layer. What's the bottom layer, OK,
where all the data platforms, right?
What is what is this sort of middleware layer, if you will,
which is the agentic computer interface as well as a lot of
these agent frameworks, right? You know, we are investors in a
company called Crew AI, for example, and I'm sure anybody
who's talked about AI agents probably knows about crew AI.
It's one of the most popular open source frameworks out
there. You can use crew AI to build
agents, say others like LAN chain, which are also do
something similar. And about that, then what we
tried to do is to say like, wow, let's try and kind of map out
sort of where is the energy in the AI agent space?
And it's important to understand, I think it's on the
left side of the of the of the market map.
There's a lot of AI agentic products and offerings from
incumbents. So obviously Salesforce, you
know, launch their own sort of agentic agentic workforce agent
force as they call it, right. Microsoft has Co pilots.
We just saw open AI launch operator, which is more consumer
oriented agent. And so, you know, effectively
all the incumbents have said like, hey, we've got these great
customers, we've got all these great use cases.
Is there a way for us to improve our customer experience or
productivity by creating an agentic workflow on top of our
existing software? On the rest of the market map,
you can see sort of tremendous amount of energy in in different
verticals. You know, particularly in
coding, for example, there's been huge amount of a great
Workman cursor by far seems to be the most popular among
developers today. But you also see very specific
vertical agents, right, sales, marketing, legal, right?
You, you see finance, right? So you can take almost each of
these different functions and you can see companies building
agents which are customized to that particular vertical use
case, right? This is really interesting
company called Samaya, for example, that's building doing
some amazing work focusing on building agents for the finance
workflow, right? So you, you, you see that the
idea was a market map was not to be precise, right?
And capture the entire view. And you know, I do apologize to
a lot of the the builders out there, some of whom we missed in
the market map clearly, right. The idea really was to kind of
give you a perspective of, you know, what this is landscape
look like, right? Where?
Where is the activity? Like where?
How are builders approaching the AI agentic space?
What are the opportunities and the use cases, the predominant
or the most important use cases for AI agents right now?
They're probably like 3 or 4 buckets and you know, the first
one obviously that everybody knows and understands very well.
Are these coding agents, coding Co pilots, coding platforms,
What do you want to call them? Right.
And depending on, you know, the particular style, you know,
cursor has a particular way of, of working.
It's more like a copilot. If you take something like Devon
has a different sort of way it, it engages, you know, it fires
off a bunch of agents that you know, execute your plan and such
like. But there's a lot of energy
around developer facing AI agentic work, right?
And so you can see that in the market map as well.
The second area is in the customer experience section.
So customer experience, obviously everything from like
customer support agents, which obviously is the biggest use
case. We, we, as you, as we were
talking about earlier, chat bots are already a fact of life.
Can we make that experience much more realistic, much more sort
of, you know, engaging? So, you know, like me, you're
not basically saying agent as as soon as you as soon as you get a
a chat bot, right? So there's a ton of energy in
that space, lots of lots of great companies building
interesting products. The other area of it's just kind
of interesting is in the operation space, right?
So if you think about operations, broadly speaking, it
could be IT operations, it could be security operations.
You have sort of this needle in the haystack problem.
You have a lot of data, you have a lot of like alerts that come
in and you're trying to figure out like, OK, which ones do I
pay attention to, right? So this is actually a perfect
use case for AI agents, the ability to synthesize all of
that information. And if it's grounded in your
policies and in sort of in in a company sort of particular way
of doing things, it's able to like, say, like, hey, here's
maybe the top three to five alerts you need to pay attention
to. This is the problem that they're
articulating. And here's a few ways to
remediate this. So, you know, we were talking
some interesting startups that are actually focused in this
particular space. So I think those are the three.
And, you know, there's a lot more, but I'll just, you know,
in the interest of time, maybe I'll just pause there.
We have a question from Elizabeth Shaw, and this is on
Twitter. Who asks how are organizations
using agentic AI in their business and their ecosystem?
Let's just take for example, a customer service AI agent,
right? So that's that's a use case that
we're seeing a lot of customers experimenting with.
So what is what is this customer service agent do?
So you effectively again, you think of a chatbot today, the
customer service agent is able to first of all, sort of be
grounded in all of your data, your FAQs.
You know, how do you, I mistype my password, how do I recover my
password? Or, you know, how do I, you
know, whatever, add, add my child to the, to the insurance
policy or whatever. You have these things continuous
task, you know that, that you were able to then interact with
an agent. The agent understands what
you're trying to do. Maybe you're looking for a form,
maybe you're looking for a website, maybe you're looking
for a particular sort of quote or something like that.
So I think that is a use case that is getting a lot of
traction. We're seeing a sort of a lot of
our customers looking at sort of, you know, rolling that out
into production. The other use case that I
mentioned is on the coding side, right?
And no, we're not just talking about like an IDE like like
Cursor, which obviously has a lot of broad adoption, but
things like automated testing and provisioning, right?
So you have software you need to roll this out.
Testing is a really critical part of that process of rolling
things out. You, you're able to actually use
AI agents, you know, very effectively in sort of that
testing and sort of, you know, red, red team kind of use cases
where you can like see if you can break it, right?
That's a very critical function where you're seeing some level
of, of deployments happening. The other one, as I said, is
like in IT operations, right? And this, this is very exciting
because again, if you, you know, these are mission critical,
right? You need to be constantly up,
you know, most of these IT teams, you know, you always have
a 24 by 7 coverage because you cannot have, you know, critical
systems going down, right? And so an AI agent is perfect
because it's it, it has the ability to synthesize large
amounts of data. It has the ability to basically,
you know. Needle in the haystack, right
problem now again, work in progress.
I wouldn't say all of these things are at perfection, but we
are definitely seeing these. And then I mentioned this
company Samaya, which is very interesting.
They are actively building like a finance analyst kind of agent,
which is, you know, which is pretty accurate in terms of
being able to extract context out of research and and provide
you really focused information. So you say that it's really
accurate. I'm assuming that what you also
mean is that it is consistently reliable and predictable.
Exactly. And it's much harder to do that
if just straight out-of-the-box, right.
I think a lot of times there's a lot of confusion in the market
about like, hey, wait a minute, the large language models are
just going to keep getting better and better and better.
And, you know, ultimately they'll just they'll be like one
model that solves all, right. And, and I think in it is true
in certain simple use cases. I mean, absolutely the models
are getting better, their reasoning better.
Maybe we will get to a point of artificial general intelligence,
right where, you know, these models can just use, you know,
computers like we do. And, and, and maybe that's the
bar, right? But I think when you're looking
at complex enterprise workflows, as I mentioned, the ability for
the agent to be grounded right and to be accurate and to
present data in the way the end user expects will require some
amount of post training, some amount of inference time, right
reasoning, as well as some amount of scaffolding in order
for you to build the perfect agent.
Can you talk about the economics of agents?
And then we'll jump back because we do have additional questions
that have come in, but the economics are really important.
So what are the aspects that Dr. economics and what do enterprise
buyers need to think about when it comes to the economics of
agents? You've seen sort of the whole
spectrum of conversations, right?
Everything from like with AI agents, we're just going to go
and tally to outcome based pricing to like, well, you know,
it's it's still software. So we have to kind of figure out
how do you make sure that you're able to charge for it
appropriately. The fundamental question to ask
right when you think about pricing is can you measure the
value of the AI agent output accurately, right?
So what do I mean by that? Let's say you have a customer
support agent. You can basically say, hey, the
customer support agent handled 100 calls, right?
And you'd have taken me X amount of dollars to handle those 100
calls. The customer agent handled those
calls. So I can attribute directly a
value right to the output of that agent.
It handed 100 calls. Each call is worth X.
So there's basically a hundred X is basically the value of that
particular agents task. In the other end of the
spectrum, let's say you're doing, you know, a research
workflow, right? So you've generated research
report or you're helping an analyst basically with the
research and you improve their productivity.
How do you measure the value of that?
Right? You measure it by, you know,
individually, like how, you know, asking the the analyst,
like how, how much more productive were you right?
And you know, it's it's much harder to quantify certain tasks
versus certain other tasks. So I think the first question in
the understanding economics of agencies, can you attribute
value in a reasonably accurate way to the output of the agent.
So based on that, if you can, then outcome based pricing is
essentially where we're eventually going to go to,
right. If it's a lot more nebulous,
then I think what we're going to see is some form of an evolution
of the existing SAS pricing model.
So you might pay a platform fee like for the agent thing and
then maybe you hire an agent. So you pay on the number of
times you run the agent, right? So it's some combination of
that. So we see this again as a
spectrum. There's no like absolute here,
right? There's a lot of experimentation
today in some ways, like companies are still trying to
figure out like, you know, how's the customer getting value
customers trying to figure out. And if you're ACFO, right,
you're used to paying subscription software, you know,
like, OK, I'm I'm I've got X amount of licenses for one year
and I can budget that right now. If you go to sort of this
outcome based pricing, again, if you don't have an accurate sense
of value, how would you as ACFO budget right for these AI
agents? So I think there's a lot of
these things that need to be, you know, we're kind of
experimenting and understanding eventually where this direct
attribution of value, I think we will end up in the outcome based
pricing bucket. But there's also going to be a
lot of these intermediary models where, you know, you want to
make sure that the developers are getting a fair value for the
product that they're building and the customers are paying a
fair price for it. So ultimately when we reach the
point where agents have discrete measurable output results, then
we can move towards performance based pricing and until then
it's essentially usage. Right.
Yeah, I think that's a good way to put it.
We have an important point now raised on LinkedIn by Naresh
Kumar, who is VP and General Manager of Product Management at
Z Scaler. And he raises the question, what
about security and agentic AI? And we haven't talked about
that. So I'm glad you brought this up.
Large language models help with this sort of needle in the
haystack problem, which is inherent to sort of diagnosing
security problems. I kind of grew up in the
networking world and be used to, you know, build these large
global scale Internet scale networks.
And a big part of the task was like, you know, if if there was
an outage somewhere to, to debug, that would essentially
mean like we synthesize tons of data, right, and figure out
where we need to focus our efforts, right.
And security is the same way. You have a large aperture of
exposure depending on, you know, the type of company you are,
everything from your network to your applications, your devices
to your individuals to identity, right?
There's like multiple layers, right when you think about
security and so, and it's been a tough challenge, right in the
security industry, we've had, you know, these platforms called
CMS which try to bring all this together and be able to give you
like a unified view where you're able to manage this.
But you know, a security OPS centre, essentially the nerve
centre of how most companies run their security operations.
So I would look at the role of LLMS in security in three ways.
The first I think is from a operations perspective, I think
it could be a very useful tool because he has the ability to
synthesize large amounts of data and help in that needle in the
haystack problem or prioritize it.
I think it's a great use case. The second one is, you know, LMS
integrated into the security products, right?
Will essentially, you know, you talk about again, a security
agent, existing security software, right, being able to
dynamically understand policy, right, dynamically able to
respond to, you know, you're adding more users etcetera.
I think you're able to sort of build those.
We're seeing companies starting to build large language models
into their software stack. Just as we talked about earlier,
it's a tool right, where where it's useful.
The third I'd say is look, you know, LLMS do represent a new,
you know, threat aperture, right?
Particularly if the models essentially hallucinate or for
example, in that psych OPS use case, you know, ignore or
highlight or or miss critical, critical kind of threat.
So while you're designing, while we talk about all these agents
being deployed in a customer support use case or, you know, a
finance analyst use case, if those large language models are
not sufficiently grounded, right, and they're not, you
know, the data, the training set that they have is not protected
appropriately, you risk not just hallucinations, but effectively,
you know, a hijack of the entire AI agent.
So it's early days in that I think we've had some, you know,
some interesting conversations with, with founders who are
thinking about this problem deep ways and building interesting
things. But yeah, you know, it's, it's,
it's an problem space at this point.
It's we will learn more and we will evolve the security
architecture just as revolving with the maturity of the AI
agents itself. OK.
And obviously Z Scaler is thinking about this because he
asked that question. Yeah, they're an important
player and they have a huge role to play right in in in our
overall architecture. We have another question from
Arsalan Khan. I'll ask you to answer this
really fast because we're just going to run out of time now who
says should we create a time and motion AI agent that assesses
other agents if they have saved money in an organization,
obviously referencing back to the pricing discussion we had
earlier? If you can listen to some of the
industry luminaries talk about like we all have some of this
army of agents, or we will have, you know, human employees and
agentic employees. There is a requirement to one
like train all these agents, ground all these agents, right,
as well as to evaluate all these agents.
So we talked about reflection loops in, in, in terms of like
the specific sort of output governing these output.
So similarly at a higher level of abstraction, which is the
value, right? Yeah, it is.
You know, you could, it's a, it's an interesting idea to be
able to say you have an agent that's constantly measuring the
value of the output of other agents to ensure that they are
meeting a particular mark. For example, if you're a
customer support agent, right? If it's not deflecting whatever,
you know, 100 calls a day or something like that, right?
And the metric may vary, then maybe it's not performing
appropriately. So and you could have an agent
that's it's, it's more like an operational function.
So there are ways to, I think in this sort of agentic future,
they are the worker AI agents and they're these sort of, you
know, evaluation agents and you potentially will have manager
agents at some point, right? You can, you can think of a
future where there is some level of sort of different levels of
hierarchy where you are actively evaluating and governing these
agents. But again, we're we're early
days yet. We're a little bit sort of
hypothesizing how this looks like.
Gus Beck Dash comes back and he says is it really better to have
the agency and the knowledge in one model or have them as
separate, loosely integrated systems?
It'll come back to the type of problems space that you're
addressing, right? You know, in general, we know
from just, you know, the things around us that there's no such
thing as one unified body of knowledge, right?
We as human beings, there's so much complexity in sort of our
world, in our workplaces, in our sort of consumer oriented lives,
that there's no such thing as like 1 mega intelligence that
essentially does everything for you.
So, you know, I think we always solve problems by breaking down,
breaking them down into smaller problems, right?
And then using different tools to solve those problems and put
these things back together. That's sort of the, the way, you
know, human workflow happens, you know, irrespective of
whether it's a, you're building a chair right, as a project or
you're building a complex set of application in an enterprise.
So I will again hypothesize here and say that I don't believe in
this sort of single unified agent.
I do think agents will continue to get better.
Maybe we will get to this threshold of artificial general
intelligence. How will you define it?
That's another at worst conversation.
But I think you will always have this notion of taking a problem,
breaking it down, using different tools to solve that
problem, to put it together. And you can think of that same
architecture applying in an agentic world as well.
OK, And with that, this has been an action-packed hour.
Praveen Akiraju, thank you so much for taking time to share
your expertise and knowledge with us today.
I really, really do appreciate you.
Thank you. Thank you for having me.
And a huge thank you to everybody who watch.
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