PRIYANKA VERGADIA: If you are looking
to simplify the development of complicated conversational
experiences, then you've clicked on the right video.
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I'm Priyanka Vergadia, and in this episode
of "Deconstructing Chat Bots," I will share with you
how Dialogflow CX simplifies and accelerates
the creation of enterprise-level conversational experiences.
Let's say PUggs is a huge e-commerce company that sells
shoes for our furry friends.
Customer service is critical for their business,
obviously, because furry friends bark sometimes.
When they started three years ago, the company was small.
Their product options were limited,
and their customer service did not
require too many complicated twists
and turns in the conversation.
And the one developer and Dialogflow essentials
was enough to handle the demand of a fairly
simple conversational flow.
But this year, their business has grown exponentially,
with more products, customers, and a bigger
team, which has led to an evolution of their customer
service.
Their customers now have complex conversations,
which drove them towards a platform that
can simplify the process of developing
a conversational experience.
That's where Dialogflow CX comes in.
Dialogflow CX makes it easy for not just the developers
but also the conversational designers and contact center
managers to build the conversational flows.
PUggs team is excited because with Dialogflow CX,
they have the visual flow vendor, which
makes it easy to understand, navigate, and edit
complex flows.
It is optimized for large enterprise use cases,
allowing multiple different flows
within a single virtual agent.
Think of flows as a reuseable way
to contain and control a specific part
of the conversation, such as user authentication or payment
processing.
If you're familiar with mega agents and subagent
concept in Diaglogflow essentials,
flows serve a very similar purpose.
They can be reused anywhere in the agent.
So if PUggs has different teams managing orders and returns,
they can effectively work together
by calling each other's flows while having
full control of their own flow.
They even have versioning and environment capabilities
at the flow level, which facilitates each team
to work on their flows at their own pace.
The state-based data model in Dialogflow CX
allows them to easily reuse intents, entities,
and webhooks.
They can also intuitively define transitions and handle
deviations from the main topic.
With the native IVR settings, they
can easily convert an agent into an IVR
and integrate with their telephony partners.
And access to an advanced analytics dashboard
provides easy assessment of the model performance
to better serve their users.
Now that we have a basic understanding,
let's take a little tour of the Dialogflow CS console.
Once we are in, we see the project and the agent
that we are working with.
We can also have multiple agents within one project.
The graph in the center shows a visual representation
of the selected conversational flow,
similar to a state-machine diagram.
Using this Graph Setting button, we
can zoom in and out of the flow and change
orientation as needed.
I have this very simple agent that has one flow.
We see all the transitions here.
Then I have this other agent that
has multiple flows branching out from the default start flow.
If I select one of these flows, I see the graph for that flow.
The first node of the graph, called Start,
is the Start page of the flow.
And these connecting lines from one node to another
represent possible transitions.
Each node of the graph represents a page.
We can click a node to expand it and edit the data.
Some content-specific panels open
on the right side of the console to give more room
to fill the details.
The Build tab on the far left contains the sections to manage
the flows and the pages.
When you select a flow, the pages
contained within that flow are listed in the Pages section.
The Manage tab contains resources and tools
available to the agent, such as intents,
entity types, webhooks, deployment options, and more.
We will look at all of this in detail
in the upcoming episodes.
This indicator shows the status of the tasks like training.
The Agent Settings button opens the Agent Settings panel
where we can modify the agent name, machine learning, IVR
settings, languages we want to support, and more such things.
And on the far right we have the simulator,
which helps us test our agent as we are designing it.
Notice that here we can choose the environment we want
to test in, and it even selects the specific flow we
are testing, especially useful for PUggs
where different teams manage each flow.
All right, so that was a quick introduction
of Dialogflow CX with a little tour of the console.
We saw how it helps simplify the development
of complicated conversational experiences.
Join me in the next episode where
we will follow PUggs' journey to dive deeper into the Dialogflow
CX console and to understand the main components-- the flows,
the pages, and transitions.
Until then, comment below to share with me
your Conversational AI use case.
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