Artificial intelligence has moved from a futuristic concept to an everyday necessity. Businesses rely on AI to automate tasks, personalize experiences, and deliver faster, more accurate support. Consumers expect instant answers, natural interactions, and tools that adapt to their needs.

This shift has created a massive demand for systems that can understand language, interpret intent, and respond intelligently — not just with pre‑written scripts, but with context‑aware reasoning.

As AI becomes more embedded in customer experiences, the need for Conversational Experience Automation (CXA) has grown rapidly. CXA goes beyond simple chatbots. It blends natural language understanding, workflow automation, and integrations with external tools to create seamless, intelligent interactions.

And this is exactly where Google’s conversational agent ecosystem steps in.

What Are Google’s Conversational Agents?

Google’s conversational agents are part of its broader AI platform designed to help developers build natural, interactive, and intelligent conversational experiences. These agents can:

• Understand user input through natural language processing
• Interpret intent and context
• Trigger actions or workflows
• Integrate with external APIs and tools
• Provide dynamic, personalized responses

Think of them as modular building blocks for creating smart assistants, educational tools, customer support bots, or any system that needs to “talk” with users in a human‑like way.

Google provides a visual builder, intent recognition, and integration capabilities that make it accessible even for beginners — while still powerful enough for advanced use cases.

Building a Learning Flow With Google’s Conversational Agent

The steps below will guide you through using Google’s conversational agent platform to create a flow that helps users understand English phrases and words.

1. Define the Use Case

Your goal: A user types a phrase like “break the ice”, and the agent explains its meaning, usage, and examples.

2. Create an Agent in Google’s Conversational Platform

Begin by creating a new agent in Google’s console. From the list of available options, you’ll typically choose “Build your own” to start with a blank, customizable setup—though you’re free to pick any template that fits your needs.

Figure 1 — Create an Agent in Google’s Conversational Platform

During this step, you’ll also select important configuration details such as region, language, and other project settings. Once the agent is created, you’ll have access to a dedicated workspace for building conversational flows.

Figure 2 — Configuring the Agent's setup

3. Explore the Default Playbook

Once your agent is successfully created, you’ll be taken to the main landing page. By default, Google opens a starter Playbook for you. This initial playbook acts as a blank canvas where you can define how the agent should understand user input and what actions it should take in response.

For this tutorial, we’ll continue working directly within the default playbook. It provides everything needed to start shaping the agent’s behaviour. From here, we’ll add clear instructions that define how the agent should guide conversations, interpret user phrases, and interact with any connected tools. You can add the following Goal and Instructions as‑is or adjust them to suit your specific needs.

Figure 3 — Explore the Default Playbook

4. Open the Preview and Test Your Agent

With your playbook set up, the next step is to open the built‑in preview and test the chat experience. Make sure the correct playbook is selected in the preview panel so the agent behaves according to the instructions you just added.

Figure 4 — Open the Preview and Test Your Agent

You’ll notice from the example above that the agent now responds to the conversation according to the instructions you provided, which is exactly what we want. This confirms that the playbook is active and the agent is following the behaviour you defined.

5. Extend the Agent with an External Integration

Now that the agent can follow your instructions and respond correctly within the playbook, it’s time to enhance its capabilities by connecting an external dictionary API. This integration allows the agent to fetch real definitions, meanings, and examples directly from a trusted source, rather than relying solely on its built‑in language model.

In this step, you’ll add the dictionary API as an external tool and then update your playbook instructions so the agent knows when and how to use it. Once configured, the agent will be able to acknowledge the integration and include dictionary search results in its responses whenever a user asks about a word or phrase.

Figure 5 — Extend the Agent with an External Integration

Be sure to include the following schema when configuring your OpenAPI settings. This defines how the agent will communicate with the service and ensures the integration is recognised as an external tool the agent can call when needed.

openapi: 3.0.0
info:
  title: Dictionary API - /api/v2/entries/en/{word}
  version: 1.0.0
  description: API to retrieve dictionary entries for a given word.
servers:
  - url: https://api.dictionaryapi.dev
paths:
  /api/v2/entries/en/{word}:
    get:
      summary: Get dictionary entries for a word
      operationId: getDictionaryEntries
      parameters:
        - name: word
          in: path
          description: The word to look up in the dictionary
          required: true
          schema:
            type: string
      responses:
        '200':
          description: Successful operation
          content:
            application/json:
              schema:
                type: array
                items:
                  $ref: '#/components/schemas/DictionaryEntry'
        '404':
          description: No entry found for the word.
        '500':
          description: Internal server error.
components:
  schemas:
    DictionaryEntry:
      type: object
      properties:
        word:
          type: string
          description: The word being defined
        phonetic:
          type: string
          description: Phonetic transcription of the word
        meanings:
          type: array
          items:
            $ref: '#/components/schemas/Meaning'
    Meaning:
      type: object
      properties:
        partOfSpeech:
          type: string
          description: The part of speech (e.g., noun, verb)
        definitions:
          type: array
          items:
            $ref: '#/components/schemas/Definition'
    Definition:
      type: object
      properties:
        definition:
          type: string
          description: The definition of the word
        example:
          type: string
          description: An example of the word used in a sentence

Now add the external dictionary integration to your playbook instructions so the agent knows to search for word meanings using the connected API and acknowledge the results in its responses.

Figure 6 — Dictionary Integration Configuration

Now let’s test it out and see the integration in action.

Figure 7 — Configuration playground

With the dictionary integration in place, the agent now goes beyond simple scripted behaviour and actively pulls real definitions from the external API whenever a user asks about a word or phrase. This demonstrates how easily the agent can incorporate outside data sources to enrich its responses. Although this example focuses on a basic dictionary lookup, the same approach can be expanded into far more powerful capabilities, such as connecting to translation services, knowledge bases, business systems, or any API that can enhance the agent’s understanding and output. It’s a small step that opens the door to building much more advanced, intelligent interactions.

References

This tutorial was supported by several key resources that make it possible to build intelligent, conversational experiences:

• Free Dictionary API
• Google Conversational AI Documentation
• Build a Conversational AI Agent on Google Cloud in 5 Minutes (Powered by Gemini)
• Exploring Tools in Google Conversational Agents – Extend Your AI Power