How To Create a Conversational Agent with Dialog Flow?

How To Create a Conversational Agent with Dialog Flow?

In 2021, almost all businesses are available online, serving their customers 24/7. Brands are often competing with each other in enhancing their digital presence. Yes, more than a mere online presence, they want to give their customers a fantastic shopping experience. Brands know very well that if not, their customers will switch to their competitors.

In this highly competitive and uncertain market, businesses need to provide 24/7 support to their customers. Today’s customers expect more from brands, and they want to reach them whenever they want. Conversational agents/chatbots are the ideal options for businesses to engage with their customers 24/7.

That is why chatbot usages are skyrocketing in the e-commerce industry. You can find them on almost all websites these days. Yes, digitization and automation are the future, and the whole world is moving towards it.

However, building a conversational agent from scratch can be a challenging and nearly impossible task. Giants like Google and Amazon have come up with such chatbots after spending years of research and billions of dollars.

Fortunately, these tech giants including Google and Amazon, allow businesses to purchase their technology platforms to create customized chatbots for their business purposes. As these platforms have pre-trained language models and easy-to-use interfaces, it comes in handy for businesses to set up and deploy customized chatbots effortlessly.

The chat and voice bots industry is exponentially growing with the advancements in technology. Dialog Flow(previously known as is one such platform from Google which provides use-case-specific, engaging voice and text-based conversations powered by Artificial Intelligence (AI).

What is Dialog Flow?

Dialog Flow is a tool that allows you to make conversational agents, or bots, or assistants that understand human conversations. It is easy to design and integrate this conversational user interface into your mobile app, web app, devices, bots, and other interactive voice response systems. Dialog Flow can help you engage with your customers more effectively.

Natural Language Understanding (NLU) is the most challenging part while building a chatbot. Yes, how do you make sure your Bot understands what your customer says? Dialog Flow comes in handy to fill this gap. Dialog Flow is a natural language understanding platform that analyzes the conversation and responds with an appropriate reply.

Thus, it replaces the Natural Language Understanding (NLU) parsing part allowing you to focus on other areas like your business needs and logic. You can deploy this bot to any platform like Facebook Messenger, Slack, Google Assistant, Twitter, Skype, or on your mobile app or website.

Ways to Use Dialog Flow Application:

There are predominantly three ways to create a Dialog Flow application:

  • You can build a bot using Dialog Flow’s one-click integration without having any coding knowledge.
  • You can use fulfillment to make a bot that has non-conversational functions. That means getting data from some third-party APIs by integrating your own custom API call.
  • You can integrate Dialog Flow API in user-defined servers and call them when needed. That means making a proper Natural Language Understanding (NLU) service.

Fundamental Units of Dialog Flow:

Let us try to understand the basics/ building blocks of Dialog Flow first.

  • Agents
  • Intents
  • Entities
  • Context

Agents are the basic units of Dialog Flow’s NLU module. A Dialog Flow agent is a trained generative machine learning model that understands natural language flows and human conversation nuances. Dialog Flow translates input text into structured data during a conversation. Thus, it enables the apps to understand the question of the end-user.

Agents work in a series like this:

  • Users interact with the agent and send a request.
  • The Natural Language Understanding (NLU) engine processes the request.
  • The system sends back a response to the user.
(Source: code4developers)

The above image depicts the importance of the agent. Yes, agents are responsible for the conversational flow. Dialog Flow provides some pre-built agents that businesses can use to get started. These pre-built agents have the most common use-cases like hotel bookings, navigation, and online shopping.

Intents are nothing but actions that a user can perform on the agent. You can also say this way. An agent is made up of intents. It recognizes what an end-user says and what actions to take. In short, intents are the entry points that initiate a conversation.

A user may request the same thing in many different ways by restructuring their sentences. However, they all mean the same thing. Thus, they all should resolve to a single intent.

Let us see a few examples of intent. What is the weather in New York today? Or play Despactio. Thus, an intent requires several phrases that you would need to program. They decide what API to call, what parameters, and how to respond to a user’s request.

Dialog Flow smartly recognizes these intents. For instance, in a sentence like play Titanic music, though the entire phrase triggers the intent, Dialog Flow extracts the keyword Titanic. Thus, it retrieves the correct song to play.

The agent would not know what values to extract from the user’s input/intent. There comes the need for entities. Yes, entities are efficient tools that fetch parameter values from Natural Language Understanding (NLU).

Any essential data that you want to get from the user’s request will have a corresponding entity. Thus, Dialog Flow uses entities to extract parameters from phrases mentioned by the users.

Entities are of three types namely:

  1. System Entities: They are predefined entities that Dialog Flow can directly extract without any assistance from the bot creator. Date, time, number, currency, and color are some examples of system entities.
  2. Developer Entities: Developer entities are entities that the developers declare, for instance, restaurant lists, locations, etc.
  3. User Entities: As the name implies, user entities are end-user-specific. No way a developer can declare these user entities. User entities are often known as short-lived entities. These are still advanced concepts that may come up in the future.

Context makes the bot conversational. Yes, a context-aware bot can recognize things and engage with users much like human interaction. Dialog Flow uses contexts to track the conversion of the user.

Consider the following conversation:

Hey, are you coming for swimming practice tonight?
Oops!! I have got to visit a friend’s place.
That’s alright. How about tomorrow night then?
That works!!

What does the conversation have to inform us? The first query is straightforward to parse. The time is tonight, and the event is swimming practice.

But if you check the second question, How about tomorrow night then? It did not mention the actual event. That is the swimming practice. Humans naturally have this sort of understanding. But, we need to program bots explicitly to understand such context across the sentences.

Other Terminologies Involved in Dialog Flow:

Furthermore, you will come across some other basic terminologies when you create a conversational agent/chatbot using Dialog Flow.

  • Fulfillment
  • Webhook

Fulfillment is a code deployed as a webhook that enables your Dialog Flow agent to call business logic on an intent-by-intent basis. Fulfillment allows you to use the information fetched by Dialog Flow’s NLU during a conversation. Thus, using the extracted data, the chatbot generates a dynamic response or trigger actions on your back-end.

Almost all Dialog Flow agents make use of fulfillment. Here are a few examples where you can use fulfillment to extend an agent.

  • Using fulfillment, one can generate dynamic responses depending on information looked up from a database.
  • You can use fulfillment for ordering products requested by the customer.
  • People often use fulfillment to implement the rules and winning conditions for a game.

Webhook is some code that runs on a server outside the Dialog Flow, which performs your chatbot’s business logic. In Dialog Flow, you can use a webhook to fetch data from your server whenever a specific intent having webhook is enabled. The data from the intent is often passed to the webhook service to get results.

Final Words:

Conversational agents or chatbots have already entered every business industry. Yes, businesses now know the importance of digital presence and thus adopting every new technology to establish their strong online presence.

A recent survey reveals that artificial intelligence(AI) and machine learning are skyrocketing in the digital marketing field. It is because the digital doors of businesses are always open for their customers. That forces the brands to entertain their customers 24/7.

With smartphones, people can order things online anytime. It could be in the middle of the night or the early morning. It is impossible to have a person in customer support to attend to customers or visitors online. These conversational agents/chatbots are precisely for that purpose.

Chatbots can interact with people who visit your online store/website/mobile app anytime. Chatbots are gifts of Artificial Intelligence that almost replace human interaction. Thus, they save your time, energy, and money. Also, chatbots help to convert visitors into customers by answering their questions immediately.

Entrepreneur, Founder @Cypherox Technologies.