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Dialogflow Architecture

First published: April 2018  |  Last Updated: July 2020

Recently, I realized that people are not always very clear about Dialogflow’s architecture. In particular, a client was mixing up fulfillment and integrations. So this article provides a high level overview of Dialogflow’s architecture with a focus on how data flows when users interact with Dialogflow agents.

In my view, there are 4 concepts you need to understand to get a good picture of Dialogflow’s architecture.

1 Request-response

You can summarize a Dialogflow agent as a series of (request, response) pairs [1] . That is,

1 User sends a request to the Dialogflow agent

2 (some NLU magic happens)

3 Dialogflow sends a response back to the user

Why is this important?

You see a lot of questions on forums on how to send “push notifications” from Dialogflow agents to the end user. (E.g. send them a reminder via Facebook messenger based on a past conversation). The request-first and response-next paradigm means that this feature isn’t supported in Dialogflow and the only way to achieve push notifications is by sending what are called “out of band” messages.

Sending “out of band” messages requires two things:

  • knowledge of the specific API of your messaging app (such as Facebook Messenger’s APIs)
  • understanding where it fits into the existing Dialogflow integration

Once you understand the request + response concept, you will realize that out-of-band notifications cannot be natively supported by Dialogflow 1-click integrations.

2 Intent Mapping

The second concept you need to understand is intent mapping.

Once your Dialogflow agent receives the user’s request, it will try to map it to a list of pre-defined intents. Intents are created by the bot developer as a way of telling the Dialogflow service “here is a list of tasks that the user might ask the chatbot to do”.

To understand how the intent mapping actually works, you need to understand some supplementary concepts – entities, contexts, parameters etc. You can take a look at my step by step guide to help you with this.

After Dialogflow maps the user’s request to one of the pre-defined intents, it will send a response (which is also specified by the bot creator) back to the user.

3 Fulfillment

Once Dialogflow maps the user’s request to one of the pre-defined intents, it might have to perform some business logic before sending a response.

Instead of creating “business logic LEGO blocks” (which some other bot frameworks do), the Dialogflow folks decided to use a concept called Fulfillment.

Here is how it works:

1 Dialogflow understands the user’s request and extracts structured information from their message

2 The structured information is sent to some code running on some web host which is maintained by the bot creator – this code is called a webhook [2]

3 The webhook performs the necessary business logic

4 It will return a response to your Dialogflow agent in a specific format

5 Dialogflow will parse the response, extract relevant information and use it in the response sent to the user

4 Integrations

In addition to the features above, Dialogflow also exposes an API.

This API allows you to programmatically send requests to your Dialogflow agents. The request will be mapped to an intent and an appropriate response will be sent to the API caller, just as if the request message was typed into Dialogflow’s console.

This means you can “embed” a Dialogflow agent into your existing applications and services.

Putting it all together

Let us consider Dialogflow’s built-in web demo integration. If you would like to see it in action, check out this chatbot which gives advice on SEO. This is using the built-in web demo integration provided by Dialogflow. However, the web demo integration has a lot of limitations (e.g. cannot display formatted text, line breaks, images etc).

So you can integrate your website with a Dialogflow chatbot using its REST API, and get a more visually appealing chatbot. For example, my CourseBot supports images, text formatting, line breaks, buttons, list selectors etc. The coursebot is just a sample FAQ bot which showcases the rich webchat integration, but it isn’t a particularly intelligent bot. 🙂

When the user interacts with my CourseBot, this is how the data flows:

[1] There are some exceptions. For example, it is possible to trigger a response from your Dialogflow agent by using Events. Technically, this is still a “request” going to the agent’s API endpoint, but it is not in the form of spoken or written text. [2] If your business logic is quite simple, you can host your code right inside your Dialogflow agent by using “inline webhooks”. But the idea is the same – instead of you hosting the code, you are letting Google host the code for you.

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  • Hi Aravind,
    I would like to know more about an architecture in which right after the end user sends a message it goes to my server before going to DialogFlow. So I can make a previous analysis (stats, human take over…) Have you got something like this?
    Webchat –> MyServer –> DialogFlow (or any NLP) –> Server or Webchat

    If you have any info I would really appreciate it. Thanks!

    • Yes, my rich website chatbot course covers precisely such an architecture, but right now it covers only API v1. (Although, if you are willing to do the legwork, you can combine it with some other tutorials on this site and get it to work for v2 also)

  • Hi Aravind,
    In your illustration, suppose Intent 1 & 2 are related to some generic information requested from the bot (working days, rate of interest etc.) which can be shared to anon user, but Intent 3 is some user specific sensitive information (eg. account balance). Lets assume it’s a banking bot.
    Now we obviously want user to be authenticated to find out only his/her own account balance. How can we do this authetication on the fly prompting user to use his netbanking credentials?

    I didnt find something releated to this in here, so uit might be useful to create a post on it.
    Thanks in advance!!

    • I think this question is best answered only after knowing the actual system where it will be used. I will write an article on this when I get some time, but chances are it will not satisfy a given custom requirement.