Agents

A Dialogflow CX agent is a virtual agent that handles concurrent conversations with your end-users. It is a natural language understanding module that understands the nuances of human language. Dialogflow translates end-user text or audio during a conversation to structured data that your apps and services can understand. You design and build a Dialogflow agent to handle the types of conversations required for your system.

A Dialogflow agent is similar to a human call center agent. You train them both to handle expected conversation scenarios, and your training does not need to be overly explicit.

Create an agent

Note: You can create multiple CX agents for one Google Cloud project.

To create an agent:

Console

1. Open the Dialogflow CX Console.
2. Create or choose a Google Cloud project.
3. Click Create agent.
4. Select Auto-generate to create a data store agent or select Build your own to create other kinds of agents.
5. Complete the form for basic agent settings:
   1. You can choose any display name.
   2. Select your preferred location. Click the Edit button if you want to change advanced location settings.
   3. Select your preferred time zone.
   4. Select the default language for your agent. You cannot change the default language for an agent once it is created.
6. Click Save.

API

If you have not already configured location settings for your project, you must configure these settings with the console before creating agents with the API. Currently, you cannot configure location settings with the API.

To create an agent, see the create method for the Agent type. Go to the Agent API reference

Select a protocol and version for the Agent reference:

Protocol	V3	V3beta1
REST	Agent resource	Agent resource
RPC	Agent interface	Agent interface
C++	AgentsClient	Not available
C#	AgentsClient	Not available
Go	AgentsClient	Not available
Java	AgentsClient	AgentsClient
Node.js	AgentsClient	AgentsClient
PHP	Not available	Not available
Python	AgentsClient	AgentsClient
Ruby	Not available	Not available

Close

Agent data

Dialogflow agents serve as top-level containers for settings and data for virtual agents.

To access an agent's data:

Console

1. Open the Dialogflow CX Console.
2. Choose the Google Cloud project for the agent.
3. Find the agent in the list.
4. Click the agent display name.
5. Update flows, pages, etc. as described in other guides.

API

See the guides for the data you want to update.

The following data is associated with agents:

• Intents
• Entity types
• Webhooks
• Flows
• Pages
• Route groups

For more information about how data is applied at varying levels, see the data application levels.

Agent settings

To access agent settings:

Console

1. Open the Dialogflow CX Console.
2. Choose your Google Cloud project.
3. Select your agent.
4. Click Agent Settings.
5. Update the settings as desired.
6. Click Save.

API

See the get and patch/update methods for the Agent type. Go to the Agent API reference

Select a protocol and version for the Agent reference:

Protocol	V3	V3beta1
REST	Agent resource	Agent resource
RPC	Agent interface	Agent interface
C++	AgentsClient	Not available
C#	AgentsClient	Not available
Go	AgentsClient	Not available
Java	AgentsClient	AgentsClient
Node.js	AgentsClient	AgentsClient
PHP	Not available	Not available
Python	AgentsClient	AgentsClient
Ruby	Not available	Not available

Close

The following subsections describe the different categories of agent settings.

General settings

The following general settings are available for agents:

• Display name

  A human-readable name for your agent.

• Time zone

  The default time zone for your agent.

• Default language

  The default language supported by your agent. Once an agent is created, the default language cannot be changed. However, you can perform the following:

  1. Export your agent to the JSON format.
  2. Unzip the downloaded file.
  3. Find the agent.json file.
  4. Update the defaultLanguageCode and supportedLanguageCodes fields to the desired values.
  5. Restore the agent to the same or different agent from step 1.
  6. Update language-specific training phrases and entity values as needed.

• Agent lock

  • Lock the agent

    Indicates whether the agent is locked. A locked agent cannot be edited.

• Logging settings

  • Enable Cloud Logging

    Indicates whether Cloud logging is enabled for the agent.

  • Enable interaction logging

    Indicates whether you would like Google to collect and store redacted end-user queries for quality improvement.

    Note: Some features, such as Experiments, Conversation history, BigQuery export, Analytics, and NLU model improvement require this setting to be enabled, as they rely on traffic logs.

  • Enable consent-based end-user input redaction

    If this setting is enabled, it lets you use a special session parameter to control whether end-user input and parameters are redacted from conversation history and Cloud logging, by default the session parameter is true. If this setting is disabled, no redaction occurs.

    User consent is collected using a boolean session parameter: $session.params.conversation-redaction. If this setting is enabled, and the session parameter is set to false, no redaction occurs (other redaction strategies still apply). If this setting is enabled, and the session parameter is set to true, redaction occurs.

    An example consent requesting flow could be: first ask the user if they would like to keep end-user input, and match the response with two intents, one is "yes intent" and the other is "no intent". Then, set the session parameter to false (no redaction) in the parameter presets of the "yes intent" route in fulfillment, and to true (redaction occurs) in the parameter preset of the "no intent" route.

• BigQuery export

  • Enable BigQuery export

    Indicates whether BigQuery export is enabled.

  • BigQuery dataset

    The BigQuery dataset name.

  • BigQuery table

    The BigQuery table name.

• Intent Suggestions

  You can enable intent suggestions.

• Custom payload template

  In this section, you can create descriptions and payloads for custom payload templates.

ML settings

Dialogflow uses machine learning (ML) algorithms to understand end-user inputs, match them to intents, and extract structured data. Dialogflow learns from training phrases that you provide and the language models built into Dialogflow. Based on this data, it builds a model for making decisions about which intent should be matched to an end-user input. You can apply unique ML settings for each flow of an agent, and the model created by Dialogflow is unique for each flow.

The following agent-wide ML settings are available:

• Allow ML to correct spelling

  If this is enabled and end-user input has a spelling or grammar mistake, an intent will be matched as though it was written correctly. The detect intent response will contain the corrected end-user input. For example, if an end-user enters "I want an applle", it will be processed as though the end-user entered "I want an apple". This also applies to matches involving both system and custom entities.

  Spell correction is available in English, French, German, Spanish, and Italian. It is available in all Dialogflow CX regions.

  Note: If original and corrected end-user input match different intents, the intent matching the original end-user input is selected.

  Warnings and best practices:

  • Spell correction can't correct ASR (automatic speech recognition) errors, so we don't recommend enabling it for agents using ASR inputs.
  • It is possible for corrected input to match the wrong intent. You can fix this by adding commonly mismatched phrases to negative examples.
  • Spell correction increases the agent's response time slightly.
  • If an agent is defined using domain-specific jargon, the corrections may be undesired.

The following flow-specific ML settings are available:

• NLU type

  This can be one of:

  • Advanced NLU (default): Advanced NLU technology. This NLU type works better than standard, especially for large agents and flows.
  • Standard NLU: Standard NLU technology. Will no longer receive quality improvements or new features.

• Auto train

  If enabled, the flow is trained whenever it is updated with the console. For large flows, this may cause console UI delays, so you should disable this setting and manually train as-needed for large flows.

• Classification threshold

  To filter out false positive results and still get variety in matched natural language inputs for your agent, you can tune the machine learning classification threshold. This setting controls the minimum intent detection confidence required for an intent match.

  If the confidence score for an intent match is less than the threshold value, then a no-match event will be invoked.

  Note: Before adjusting this value, you should try adding negative examples.

• Training status

  Indicates whether the flow has been trained since the latest update to the flow data.

• Train NLU

  Use this button to manually train the flow.

Generative AI settings

The following generative AI settings are available:

• General

  • Banned phrases

    List of phrases that are banned for generative AI. If a banned phrase appears in the prompt or the generated response, the generation will fail.

• Generative Agent

  Note: This setting has restricted access.

  • Generative model selection

    Select the model used by generative features. For more information, see model versions.

• Generative Fallback

  • Text prompt configuration

    See Generative fallback: define your own prompt.

• Data Store

  See Data store agent settings.

Speech and IVR settings

The following speech and IVR settings are available:

• Text-to-Speech

  • Voice selection

    You can select the language and voice used for speech synthesis.

    You may enable Custom voice for your agent by selecting the custom voice option from the voice selection dropbox and specify the custom voice name in the corresponding field. The custom voice name must follow the following pattern: projects/PROJECT_ID/locations/LOCATION/models/MODEL_NAME.

    • If you are using telephony gateway, make sure the Dialogflow Service Agent service account [e-mail] is granted with "AutoML Predictor" in your custom voice project.
    • For regular API calls, make sure the service account used to call Dialogflow is granted with "AutoML Predictor" role in your custom voice project.

• Speech-to-Text

  • Enable auto speech adaptation

    See Auto speech adaptation.

  • Advanced speech settings

    For details about advanced speech options, see the Advanced speech settings guide.

DTMF

See DTMF for telephony integrations for more information.

Multimodal

See Call companion.

Share settings

See Access control.

Languages settings

Add additional language support to your agent. For the full list of languages, see the language reference.

Language auto detection

When you configure language auto detection, your chat agent will automatically detect the end-user's language and switch to that language. See the language auto detection documentation for details.

Security settings

See Security settings.

Advanced settings

Currently, the only advanced setting is for sentiment analysis.

Export and restore an agent

Note: Dialogflow ES and Dialogflow CX agent types are incompatible. You cannot restore an incompatible agent.

You can export an agent to a file, and restore an agent with that file.

An agent export includes all agent data except the following:

• Flow versions: Only the draft flows are exported to file.
• Environments: Custom environments are not exported to file.

An agent restore overwrites all target agent data (including all flow versions) except the following:

• Environments: All custom environments remain unchanged in the target agent. Flow versions referenced by custom environments in the target agent will continue to exist, as long as the associated environments exist. However, these stale flow versions are not listed or selectable flow versions for the agent.
• Vertex AI Agents Apps: The association to a Vertex AI Agents App remains unchanged in the target agent. (In other words, the value of engine in GenAppBuilderSettings) This means that data store agents can only be restored into other existing data store agents, because the resulting agent also needs to have an association to a Vertex AI Agents App.

• Vertex AI Agents Data Stores: All references to data stores will be overwritten in the target agent according to the following rules:

  • If the target agent isn't associated with an App, then it's not possible to restore an agent with data store references into it. Trying to do so results in an error message. To fix that, you can either create a new data store agent from scratch. (Alternatively, you can turn your existing agent into a data store agent by adding a data store state handler to it. In this case you'll be guided through adding an associated App to your agent.)
  • If the target agent is associated with an App, then all the data store references will be updated upon restore: their Google Cloud project ID and location will be updated to match the App of the target agent. The collection ID and data store ID will remain unchanged. This means that you need to add data stores for all the IDs with matching types into the App of the target agent prior to the restore operation.

  Example: if the source agent refers to a data store named projects/123/locations/eu-west2/collections/default_collection/dataStores/myDataStore1 and the App of the target agent is named projects/321/locations/us-east1/collections/default_collections/engines/app123, then the resulting data store reference in the target agent will become: projects/321/locations/us-east1/collections/default_collection/dataStores/myDataStore1

Note: in the API and in the contents of the exported data, a Vertex AI Agents App is called a GenAppBuilder Engine. For example in an exported JSON Package you can find the name of the engine in the agent.json file under the key genAppBuilderSettings.engine.

When exporting, you can select the export file format. If you are using source control versioning for your agent data, you should export in the JSON format. When you restore an agent, Dialogflow automatically determines the file format.

To export or restore an agent:

Console

1. Open the Dialogflow CX Console.
2. Choose the Google Cloud project for the agent.
3. Click the option menu for an agent in the list.
4. Click the Export or Restore button.
5. Follow instructions to complete.

Note: To restore a data store agent, make sure that the target agent was created as a data store agent.

API

See the export and restore methods for the Agent type. Go to the Agent API reference

Select a protocol and version for the Agent reference:

Protocol	V3	V3beta1
REST	Agent resource	Agent resource
RPC	Agent interface	Agent interface
C++	AgentsClient	Not available
C#	AgentsClient	Not available
Go	AgentsClient	Not available
Java	AgentsClient	AgentsClient
Node.js	AgentsClient	AgentsClient
PHP	Not available	Not available
Python	AgentsClient	AgentsClient
Ruby	Not available	Not available

Close

If the agent size exceeds the maximum limit, use the Cloud Storage option for agent export and restore.

If you use GitHub, also see the GitHub export/restore guide.

Delete an agent

Caution: Deleting an agent cannot be undone. Export your agent to keep a backup if necessary.

In order to delete an agent, you need a role that provides full access or edit access. See the access control guide for more information.

To delete an agent:

Console

1. Open the Dialogflow CX Console.
2. Choose the Google Cloud project for the agent.
3. Click the option menu for an agent in the list.
4. Click the delete button.
5. Confirm deletion in the dialog.

API

See the delete method for the Agent type. Go to the Agent API reference

Select a protocol and version for the Agent reference:

Protocol	V3	V3beta1
REST	Agent resource	Agent resource
RPC	Agent interface	Agent interface
C++	AgentsClient	Not available
C#	AgentsClient	Not available
Go	AgentsClient	Not available
Java	AgentsClient	AgentsClient
Node.js	AgentsClient	AgentsClient
PHP	Not available	Not available
Python	AgentsClient	AgentsClient
Ruby	Not available	Not available

Close

If you delete your project, all Dialogflow CX agents and data associated with the project are deleted immediately.