Scaling AI Agents Without Breaking Reliability — Preeti Somal, Temporal

Channel: aiDotEngineer

Published at: 2025-07-28

YouTube video id: 1izYWsokr9s

Source: https://www.youtube.com/watch?v=1izYWsokr9s

[Music]
Uh my name is Prii and I am part of the
engineering team at Temporal. How many
people here have heard of Temporal?
Perfect. Great. So Temporal is the
company that takes reliability
incredibly seriously. so seriously that
our mascot is a tardigrade. Does anybody
know what a tardigrade is? Yes, some
folks. Well, it is what is also called a
water bear and is the most resilient
animal known to humankind. And so that's
how seriously we take reliability.
Definitely stop by our booth for some
stickers and some pins to just show how
much you care about reliability.
All right. So, my goal in the next 17
minutes or so is to convince all of you
that temporal is the right choice of
platform as you go out to build agentic
AI applications. So, let's dive right
in. We've heard a lot about how uh you
know agents are just software. However,
they are complex distributed systems.
They need to cope with LLMs
and they must scale and provide
durability and reliability. Otherwise,
no one's going to trust your agent. Uh,
you know, we can build like really cool
stuff, but if it doesn't work, nobody's
using it.
We also know that complex systems need
orchestration. uh Dex used the word
workflow a few times and essentially at
the core of Agentic AI applications is a
complicated workflow uh that
orchestrates multiple processes
uh needs to handle state potentially
over long periods of time. There needs
to be human interaction for approvals or
other reasons and of course they need to
be able to be uh able to run in parallel
for efficiency call tools. There's just
a lot of things going on. You know how
how many of you feel like building
agents is really simple. You're just
calling one switch statement, right?
Yeah. I mean there's a lot of things
that are interacting here and how can
you actually keep track of that? make
sure it's running reliably as well as
tracing and looking at the visibility of
all of these pieces.
And again, you know, these systems are
inherently unreliable. How many of you
have called an LLM and it succeeded 100%
of the time? Yeah, nobody's raising
their hands. So, we've dealt with this
as you're building these applications.
You are seeing inherently how unreliable
some of the tool chain here is.
and then uh you know difficult to debug
and test and Alex has been talking about
agent ops being kind of built around
this but clearly the insight into what's
happening has been incredibly hard to
get. It's been incredibly difficult to
test this in pre-production.
Well, the interesting thing here is that
these problems have existed for some
time in building complex distributed
systems and temporal is a company that
was founded around solving these
problems. Our mission really is to
outsource the reliability and
scalability parts of building a complex
distributed
uh uh application seamlessly. So again
you can focus on the hard parts of
writing your business logic. The way
that temporal works is that we we've
built uh language idiomatic SDKs. We've
the languages are available there and in
in fact one of the fun facts here is
that Python has overtaken all the other
languages in the month of January and
and that sort of is showing how much of
Python is being used in building these
applications.
We handle all the plumbing code for you,
making sure that every process executes
reliably and providing you guardrails.
And this is a battle tested product.
Temporal has been in production for over
a decade. Temporal is used in mission
critical applications. These are just a
few examples of our customers using
temporal in production today. So we feel
that using temporal for running these
for building your agentic uh AI
applications gives you reliability
out of box. But you know I can stand
here and talk all day about it. You you
probably don't believe me. Uh so we have
some customer quotes here that will help
you understand this. Uh, one of them is
from a customer called Dust that is
building their agents on top of Temporal
and the other is a company you may have
heard of that recently talked about the
tech stack they use and Temporal is very
clearly featured there as well.
We also have a number of use cases
published. So, Gorgeous for instance is
using AI agents today in production
built on temporal. This is the company
that does customer service for brands
like Reebok or Timbuktu or Glossier.
These are all household names. And and
the reason I'm bringing this up is just
to help you understand that customers
are today running agents on Temporal at
scale in production. And what's this
what what temporal is bringing to them
is incredible agility and speed because
they can focus on writing their business
logic and don't need to worry about
reliability. The reason I put up the
payments example here is to show you how
missionritical some of the workloads
that are running on temporal are. And
finally, you know, some more quotes here
from customers, developers using
temporal around building agent
applications. I hope some of this is
resonating in terms of the issues that
you are seeing as well as you're going
out and building these applications.
All right, let's talk a little bit about
architecture and code because no talk
here can be complete if we don't talk
architecture and code, right? All right,
I was hoping I would get some clap. So
let's get this going.
All right.
So this is this is an example of an
architecture before temporal. What what
we're seeing here is there is a lot of
interaction and error handling that
developers are being forced to code. And
when you use code using temporal
essentially we can abstract all of that
out into this concept of a workflow. A
workflow is something that you write.
This is this is very much a developer
focused tool. This is not a tool that a
business user or a non-technical per
person would use. This is a developer
going in and coding and building their
applications using these SDKs around the
concept of a workflow abstraction. And
at the end of the day when you think
about agents you essentially are just
orchestrating
a number of pieces around the
interaction the large language models
the chat history database and the tools
right these are are the key abstractions
that are in play and you're
orchestrating that using temporal.
What's the impact? You know, everybody's
going to tell you they've got the best
platform here, but what what is the
impact for engineers? What we really are
able to do is accelerate development.
And you what you can do is take temporal
and you can put applications out in
production in weeks. We've had customers
with case studies where we've sped up
their ve feature delivery velocity by
over 6x once they've started using
temporal. You can reach greater scale.
Um, one of our customers is a consumer
application that is scaling with events
and and they don't need to worry about
handling any of that scale logic at all.
So we we our cloud will handle the scale
for you. Uh, of course, you know, once
you've got reliability nailed, you can
sleep better at night, and that's always
important. Uh, and with reliable
applications, customers are happier.
Now, I've talked a lot. I wanted to walk
through a example. I I'm not doing a
demo because of all the various issues.
Uh, and it also seems like for some
reason travel is like the the classic
use case everybody seems to be demoing.
So, let's dive right in.
So, we've got a demo of a ticket booking
agent. This demo is live at our booth as
well. So, if you're interested, you can
go look at that at the booth. Um, and
what we'll do is I'll quickly walk you
through a little bit of the architecture
and how temporal would work here. And so
clearly you know some of the key pieces
here are around the user the system
which is temporal and AI your language
models goals and tools and the way that
temporal works here is um essentially
going to be able to take this flow and
in wrap pieces of this in temporal
concepts. So for instance, the workflow
defines the flow of the application and
it's written as code. So this is where
you would orchestrate the interactive
loops. You would receive we have this
notion of a signal which is how the
workflow gets input. We have a notion of
a query. So there's a a rich set of
abstractions that you program against to
build that workflow that will
essentially take kind of all of the
pieces of this model that I'm showing
you and translate that into code. And
nowhere in there will you have any
statements or code that we call plumbing
code. That is you you nowhere in there
will there be statements like if
something fails you know keep retrying
it. All of those pieces are handled by
temporal.
We also store all of the workflow
history uh so that you can go in and you
can look at the visibility of what is
happening as your agent is navigating
this complex set of interactions.
Temporal has the notion of activities
and so the tools that you use can be
wrapped into activities
and again this is just code
and of course with LLMs you can use
whatever provider you need. We are able
to help you validate the inputs and
drive towards the goal. Um and again
failures are handled transparently by
temporal
Um, and then interactions are managed
through temporal signals and queries.
Um, and they're stored in the workflow
history. So there's a very clear sort of
record of how your agent is executing
and you can go in and look at that. You
can also export that. One of the things
we are hearing is customers want access
to that history for compliance reasons
or for the for kind of being able to go
and debug in their testdev environment.
So we allow the capability to export
that entire history and you can use it
for whatever sort of purpose you might
need.
Uh and finally here you know you can
kind of look at the fact that we've got
the ability to have loops. we can, you
know, it's a it's a very rich
programming model where you can take the
various uh sort of uh use case patterns
for your agent and you can build them as
a workflow pretty quickly and get up and
running. Uh and then temporal cloud of
course is where we do all of the heavy
lifting around the reliability and
scalability pieces for you. So your
agent, your workflow, the code actually
runs in your environment. And temporal
cloud is where all of the execution
state, the call stack, the you know
looking at all of the failures and
retries, all of that is happening within
temporal cloud.
I know I'm speeding through a lot here,
but um definitely come by our booth as
well. Uh the worker is what I was just
talking about. This is your code. It
runs in your environment. It is
essentially fitting into any of your own
CI/CD practices. A big part of the
temporal focus has been on meeting
developers where they are. We don't want
you to change how you write code. We
just want you to get more efficient and
help you focus on writing your business
logic and not having having to worry
about all of the reliability and
scalability issues here.
And this for instance is uh the the
worker code for the um uh use case that
I was just showing. I know this is a
screenshot. I what I wanted to show here
is we've got this concept of co code
exchange temporal if you weren't aware
is an open-source product as well. So
you can go in and I know this conference
loves QR codes for some reason. So you
can go in and you can actually look at
the code at the code exchange and see
how temporal operates there.
Finally, temporal cloud is available uh
you can go sign up. We are giving away
credits. So getting started and kicking
the tires on using temporal is fairly
easy. You can go to code exchange. You
can look at any example you want. You
can run that in your local dev
environment. You can run it against
cloud and you can be up and running
pretty quickly here. And we are like I
said we are on the uh on the expo floor.
Come by and chat with us. We are booth
G3.
Perfect. Thank you.
[Music]