Programmatic Control of Google’s AI Agents: A Developer’s Guide

Mon 26 Jan 2026

Photo credits to

In the previous

, you walked through the process of setting up an instance of conversational agent. With that in place, this guide shifts the spotlight to the next step: how to programmatically interact with Google’s conversational agents through a simple API endpoint. Instead of clicking through interfaces, you’ll learn how to programmatically query your deployed instance and integrate conversational agents into your applications.

1. Install and Verify the Google Cloud CLI

Before interacting with Google’s conversational agents through an API endpoint, make sure the Google Cloud CLI is installed on your machine. This command‑line tool handles authentication, configuration, and service management, so it’s essential for the steps that follow.

Once installed, confirm that it’s working by checking the version:

bash

gc --version

In my setup, I’ve created a shorter alias to reduce typing during development. If you notice commands in this guide that look slightly different from the standard gcloud syntax, that’s why. You can stick with the full command or create your own alias if you prefer a more streamlined workflow.

2. List Your Available Google Cloud Projects

With the CLI ready, the next step is to confirm which Google Cloud projects are accessible under your account. This helps you verify that you’re working in the correct environment before making any API calls.

Run the following command:

bash

gc projects list

This will return a table of all projects linked to your account

3. Set Your Active Project and Authenticate

Before making any API calls, you need to tell the Google Cloud CLI which project you intend to work with. This ensures every command and request is scoped to the correct environment.

bash

gc config set project <project-id>

After that, authenticate your user account so the CLI can generate the credentials required for API access:

bash

gc auth login

This will open a browser window prompting you to sign in. Once authenticated, the CLI stores your credentials locally and you’re ready to interact with Google’s conversational agents programmatically.

4. Create and Verify a Service Account

To allow programmatic access to your conversational agent, you’ll need a dedicated service account. This account will later be assigned the appropriate roles and used to generate credentials for API calls.

bash

gc iam service-accounts create my-agent-cxa \
  --display-name="NAME ACCORDINGLY"

Once created, verify that it exists within your project:

bash

gc iam service-accounts list --project=<project-id>

5. Generate a Service Account Key and (Optionally) Test Authentication

With your service account created, the next step is to generate a key file. This key will later be used by your application to authenticate programmatic requests to Google’s conversational agent API.

bash

gc iam service-accounts keys create service-account.json \
  --iam-account=my-agent-cxa@project-id.iam.gserviceaccount.com

This command downloads a JSON key file named service-account.json to your current directory. Keep this file secure, it grants access to your project.

If you want to quickly confirm that authentication is working, you can print an access token.

bash

gc auth print-access-token

6. Make Your First Programmatic Request to the Conversational Agent

With authentication in place and your service account ready, you can now send a direct API request to your conversational agent. This is the core of programmatic access, issuing a query and receiving the agent’s response without using the UI.

bash

curl -X POST \
  -H "Authorization: Bearer <ACCESS-TOKEN>" \
  -H "x-goog-user-project: project-id" \
  -H "Content-Type: application/json; charset=utf-8" \
  -d '{
    "queryInput": {
      "text": {
        "text": "Hello!"
      },
      "languageCode": "en"
    }
  }' \
  "https://<region>-dialogflow.googleapis.com/v3/projects/<project-id>/locations/<region>/agents/<agent-id>/sessions/SESSION_ID:detectIntent"

Replace the placeholders with your actual values:

ACCESS-TOKEN: The token printed earlier or generated via your service account
project-id: Your Google Cloud project ID
region: The region where your agent is deployed
agent-id: The ID of your Dialogflow CX agent
SESSION_ID: Any unique string to represent the conversation session

If everything is configured correctly, the API will return a JSON response containing the agent’s detected intent and generated reply. This confirms that your setup is working and that you can now integrate conversational queries directly into your applications.

In practical scenarios, developers rarely rely on raw curl commands for long. Instead, you wrap these API calls inside a scripting or programming language. From there, you build services around them: helper functions, reusable modules, microservices, event‑driven workflows, or full backend integrations. That’s where the real power shows up, turning simple API requests into scalable, production‑ready conversational experiences.