Webex AI Agent Studio Administration guide

Get started with Webex AI Agent Studio

Webex AI Agent Studio is a sophisticated platform that is designed to create, manage, and deploy automated AI agents to fulfill customer service and support needs. Using artificial intelligence, AI agents provide automated assistance to customers before they interact with human agents. These agents support voice interactions with intonation, language understanding, and contextual awareness within conversations. Also, AI agents seamlessly and informatively handle digital channel interactions through text and online chat. Customers benefit from a concierge-like experience, receiving assistance with questions, information retrieval, and minimizing wait times.

Access to the autonomous AI agent for the voice calls is currently limited to specific customers. For more information, please reach out to Cisco support.

Key benefits for businesses

Efficiency & productivity
- Automates repetitive tasks, freeing human employees for strategic work.
- Provides 24/7 availability, handling higher volumes and improving response times.
- Improves accuracy in data processing, analysis, and reporting.
Cost savings
- Reduces labor costs through automation.
- Optimizes resource allocation for greater efficiency.
- Lowers operational costs by streamlining processes and preventing errors.
Enhanced decision-making
- Provides data-driven insights by analyzing large datasets.
- Enables predictive analytics for anticipating future outcomes.
- Improves risk management through identification and assessment.
Improved customer experience
- Personalizes interactions based on customer data.
- Offers faster response times through AI-powered chatbots and virtual assistants.
- Provides 24/7 customer support.
Scalability & flexibility
- Easily scales up or down to meet changing business needs.
- Learns and adapts to new situations and information.
Competitive advantage
- Drives innovation and development of new products/services.
- Increases efficiency for a competitive edge.
Employee Empowerment
- Frees human potential for more creative and strategic work.
- Enhances collaboration by providing data, insights, and support.

Understand AI agent types and examples

The following table provides a glimpse of AI agent types and their capabilities:

Table 1. AI agent types and their capabilities
AI agent type	Purpose	How to set up?
Autonomous	Autonomous agents work independently to fulfill the defined goals, reducing the need for continuous human intervention. They make decisions based on available information and predefined actions. They automate time-consuming or repetitive tasks. They access and use a knowledge base to deliver informative and accurate responses to user questions.	Set up autonomous AI agent
Scripted	Scripted AI agents are programmed to follow a set of predefined rules and instructions. They perform specific tasks that are clearly defined and structured. They respond to questions based on a user-created training corpus, which is a collection of examples and answers.	Set up scripted AI agent

Examples

Both autonomous and scripted AI agents apply to various use cases, depending on the specific requirements and desired capabilities. Some examples include:

Customer service—Both autonomous and scripted agents can provide customer support, with autonomous agents offering more flexibility and understanding of natural language.
Virtual assistants—Autonomous agents are well suited for virtual assistant roles as they can manage a wide range of tasks and offer more personalized interactions.

The choice between autonomous and scripted AI agents depend on the complexity of the tasks, the required level of autonomy, and the availability of training data.

Prerequisites

If an organization doesn't have an active contact center trial or subscription, partners can set up a contact center trial with the Webex AI agent feature. For more information, see Webex Contact Center Self-Service Trials article.
To sign up for subscription, customers need to purchase the AI Agent add-on for the Flex-3.0 Contact Center license. Based on entitlements, Webex Contact Center provisions AI Agent as one of its services for an organization. For more information about provisioning the Webex Contact Center, see Get Started with Webex Contact Center.

User roles and permissions

This section provides the details of the user roles and required permissions within an organization for accessing the AI Agent Studio application:

Role	Customer organization	Partner organization	Any other organization
Full Administrator	Full access to AI Agent Studio except sensitive info such as Transcripts	Full access to AI Agent Studio except sensitive info such as Transcripts	Full access to AI Agent Studio except sensitive info such as Transcripts
Contact Center Service Administrator	Full access to AI Agent Studio except sensitive info such as Transcripts	Not Applicable	Full access to AI Agent Studio except sensitive info such as Transcripts
Provisioning Administrator	Not Applicable	Full access to AI Agent Studio except sensitive info such as Transcripts	Not Applicable
Read-only Administrator	Read-only access to AI Agent Studio except sensitive info such as Transcripts	Read-only access to AI Agent Studio except sensitive info such as Transcripts	Read-only access to AI Agent Studio except sensitive info such as Transcripts
Supervisor	Read-only access to AI Agent Studio except sensitive info such as Transcripts	Not Applicable	Not Applicable

Access Webex AI Agent Studio

To create your AI agents, you must sign in to the Webex AI Agent Studio application. You can sign in using the following ways:

Sign-in from Control Hub

If you don’t see Webex AI Agent, contact Cisco Support to enable the corresponding feature flag.

Sign in to Control Hub.
Select Services > Contact Center.
From the Contact Center navigation pane, select Customer experience > AI Agent.
Click Webex AI Agent to access the application.

This opens the Webex AI Agent Studio application in another browser tab, and you’re ready to configure your AI Agents.

Sign-in from Webex Connect

To access the Webex AI Agent Studio application, you should have access to Webex Connect.

Sign in to Webex Connect application using the tenant URL provided for your enterprise and credentials.
By default, the Services page appears as a home page.
From the App Tray menu of the left navigation pane, click Webex AI Agent to access the application.
The system opens the Webex AI Agent Studio application in another browser tab and you’re automatically signed-in to the application.

Home page layout

Welcome to the Webex AI Agent Studio application. When you sign in, the home page displays the following layout:

Navigation bar

The navigation bar that appears on the left provides access to the following menus:
- Dashboard—Displays a list of AI agents the user has access to, as granted by the enterprise administrator.
- Knowledge—Shows the central knowledge repository or knowledge base, which serves as the brain for autonomous AI agents to respond to customer queries.
- Help—Provides access to the Webex AI Agent Studio user guide on the Webex Help Center.
- Sign out—Allows you to sign out of the Webex AI Agent Studio application.

Know your Dashboard

On the dashboard, the AI agents are represented by cards. Each card displays basic information, including the AI agent name, last updated by, last updated on, and the engine used for training the agent.

Tasks on AI agent card

Hover over an AI agent card to view the following options:

Preview—Click Preview to open the AI agent preview widget.
Ellipsis icon—Click this icon to perform the following tasks:
- Copy Preview link—Copy the preview link to paste in a new tab and preview the AI agent on the chat widget.
- Copy Access token—Copy the AI agent's access token for invoking the agent through APIs.
- Export—Export the AI agent details (in JSON format) to your local folder.
- Delete—Permanently delete the AI agent from the system.
- Pin—Pin the AI agent to the first position on the dashboard, or unpin to move it back to its previous position.

Create a new AI agent

You can create a new AI agent by using the + Create agent option on the top-right corner of the dashboard. You can choose to use a predefined template or create an agent from scratch.

To know how to create scripted and autonomous AI agents, see the following sections:

Create an autonomous AI agent
Create a scripted AI agent

You can create up to 100 AI agents, which includes both scripted and autonomous types.

Import AI agent

You can import an AI agent in JSON format from a list of available AI agents. First, ensure you’ve exported the AI agent in JSON format to your local folder. Follow these steps to import it:

Click Import agent.
Click Upload to upload the AI agent file (in JSON format) exported from the platform.
In the Agent name field, enter the AI agent name.
(Optional) In the System ID, edit the system-generated unique identifier.
Click Import.

Your AI agent is now successfully imported to the Webex AI Agent Studio platform and is available on the dashboard.

Keyword search

The platform provides robust search capabilities to help you easily locate and manage AI agents. You can perform keyword search using the agent name. Enter the agent name or a portion of the name in the search bar. The system displays a list of AI agents that match your search criteria.

Filter by agent type

In addition to keyword search, you can refine your search results by filtering based on the type of AI agent. Choose one of the agent type filters from the drop-down list—Scripted, Autonomous, and All.

Manage knowledge bases

A knowledge base is a central repository of information for the Large Language Model (LLM)-powered AI agents. The AI agents use advanced AI and machine learning technologies to understand, process, and generate human-like text. The AI engine trains the AI agents using vast amount of data, enabling them to provide detailed and contextually relevant responses. Knowledge bases store the data necessary for the functioning of autonomous AI agents.

Create the required knowledge base for the AI agent that you're configuring.

Create knowledge base for AI agent

1

Log in to the Webex AI Agent Studio platform.

2

Click the Knowledge icon on the left navigation pane.

3

On the Knowledge bases page, click +Create Knowledge base on the upper right corner.

4

On the Create knowledge base page, enter the following details:

Knowledge base name—Provide a name for the knowledge base.
Description—Provide a brief description about the knowledge base.

5

Click Create. The system creates a knowledge base with the specified name.

6

You can either upload files to the knowledge base or create documents from scratch.

To upload files, perform the following steps:
1. Click the Files tab.
2. Drag and drop the files to the knowledge base. Alternatively, click Add File to add a file.
  Important considerations:
  You can upload a maximum of 100 files.
  The system supports the following file formats: PDF, .docx, data over cable, .txt, .xlsx, .xls, and .CSV.
  The size of each file must not exceed 10 MB. Text files have a size limit of 2 MB.
  PDF and CSV files don't support hyperlinks. However, .docx, .xlsx, and .xls support hyperlinks.
  When uploading excel files, ensure the following:
  The headers must be in the first row for the system to process the header row accurately.
  The system consolidates all tables from a single excel sheet into one unified table. Clean up the file before uploading.
  Empty cells in Excel sheets are replaced with NA.
3. Click Process Files to process the files. When done, the system lists the files under the Files tab.

Important considerations:

The maximum wait time for processing a file before it gets timed out is 2 hours.
Image-processing support is available only for PDF files.

To delete all the uploaded files, click the Delete icon next to the Processed files section. To delete a single file, click the Delete file icon next to the file name.

To create documents, perform the following steps:
1. Click the Documents tab.
2. Click +Create Document to create your own knowledge base documents.
  
  You can place the documents under different categories. Click + Add Category to create categories before creating documents.
3. On the Create Document page, enter the Document name.
4. Check Add to existing category to add the document to the existing category. You can also choose to Add to new category and create a new category.
  Important considerations:
  If you don't add the documents to any category, the documents get listed in the Unassigned category by default.
  Documents don't support hyperlinks. To support hyperlinks in documents, use the link text as the hyperlink URL or simply place the URL in documents.
  You can't add tables in documents.
5. Click Save.
6. Add the required information in the text editor. Format the text using the format options, as needed.
7. To preview the document, choose the required document in the left pane and click Web Preview. The document appears in a new browser.
To delete the document, click Delete from the text editor. Also, you can delete the document by clicking the Delete file icon next to the document name in the left pane.

7

Navigate to the Information tab to view the following details:

Field	Description
Knowledge base details	The knowledge base displays the following details: Knowledge base ID. Available storage space in the knowledge base. Total number of files uploaded and documents created.
Admin user details	Name of the admin user who created the knowledge base with the time of creation.
Mapped agent	Mapped agent details. Click the Settings icon to navigate to the mapped agent page. Click the Preview icon to preview the agent.
Last updated at	Timestamp of when the knowledge base was last updated.
Last updated by	Name of the admin user who updated it last.
Activity History	History of activities on the knowledge base.

Click the Edit icon to edit the name of the knowledge base.
Click the Delete icon to delete the knowledge base completely. If a knowledge base is mapped to an AI agent, you can't delete the knowledge base.
You can map a knowledge base only to a single AI agent.

What to do next

Configure the knowledge base for the AI agents.

Access knowledge base

Before you begin

Create the knowledge base required for the AI agent.

1	Log in to the Webex AI Agent Studio platform.
2	Click the Knowledge icon on the left navigation pane. The knowledge bases appear as cards on the Knowledge bases page. The cards display the number of files uploaded and the number of documents created in the knowledge base.
3	Click on a card to navigate to the specific knowledge base. You can also access the knowledge base from the Knowledge tab of the AI agent configuration page. For more information, see Configure knowledge base section. You can search for the required knowledge base using the following criteria: Name of the knowledge base. Type of the knowledge base. Knowledge bases updated between specified dates. Knowledge bases created between specified dates. Click Reset all to reset the search criteria.

Set up autonomous AI agent

Autonomous AI agents operate independently without direct human intervention. Autonomous agents can access and use a knowledge repository to provide informative and accurate answers to user queries. These agents use advanced algorithms and machine learning techniques to analyze data, learn from their environment, and adapt their actions to achieve specific goals.

You can use the autonomous AI agents in various scenarios, including:

Provide customer support—Answer FAQs, troubleshoot issues, and guide customers through processes.
Offer technical assistance—Provide expert advice on specific topics or domains.
Natural Language Processing (NLP)—Understand and respond to human language in a natural and conversational manner.
Decision making—Make informed choices based on available information and predefined rules.
Automation—Automate repetitive or time-consuming tasks.

Guardrails configured in the autonomous AI agents ensure that the AI agent doesn't respond with unethical and harmful content.

Create an autonomous AI agent

Before you begin

Ensure to create the knowledge base for the autonomous AI agent. For more information, see Create knowledge base for AI agent.

1	Log in to the Webex AI Agent Studio platform.
2	On the Dashboard, click +Create agent.
3	On the Create an AI agent screen, choose Start from scratch and click Next. You can also choose a predefined template to create your AI agent quickly. You can filter the AI agent type as Autonomous. In this case, the fields on the Profile page autopopulate.
4	Choose Autonomous agent type.
5	Specify the following required details: Agent name—Enter the name of the AI agent. System ID—A system-generated unique identifier. This field is editable. AI engine—Webex AI Pro 1.0 is the only available AI engine. Agent's goal—Provide a brief description of the AI agent's goal. To insert an example, click Insert example. See Do's and Don'ts when writing goals for best practices.
6	Click Create. You've now successfully created the autonomous AI agent which is now available on the Dashboard. You can create up to 100 AI agents, which includes both scripted and autonomous types. On the AI agent header, you can perform the following tasks: Preview Copy Preview Link Copy Access Token Delete Export For more information, see Tasks on AI agent card. You can also import the prebuilt AI agents. For more information, see Import AI agent.

What to do next

Configure the autonomous AI agent.

Configure autonomous AI agent

The following sections guide you on how to configure autonomous AI agent for your specific needs:

Update autonomous AI agent profile
Add actions to autonomous AI agent
- Configure slot filling
- Configure fulfillment
Configure knowledge base
Configure language and voice

Update autonomous AI agent profile

Before you begin

Create an autonomous AI agent.

1	On the Dashboard, click the AI agent that you've created.
2	Navigate to the Configurations > Profile tab and configure the following details: Agent name—Edit the name of the AI agent, if needed. System ID—Edit the system id of the AI agent, if needed. Agent's goal—Edit the goal of the AI agent, if needed. See Do's and Don'ts when writing goals for best practices. Instructions—Enter the instructions for the AI agent. See Prompt engineering tips when writing instructions. URL for agent profile image—The default URL from where the system fetches the AI agent's logo or image. Change this if needed. AI engine—Webex AI Pro 1.0 is the only available AI engine. Welcome message—The default welcome message that the AI agent uses to start the interaction. Update this, if required.
3	Click Save changes.
4	Click Publish to make the AI agent live. For more information, see the Publish your autonomous AI agent section.

What to do next

Add the required actions to the AI agent.

Add actions to autonomous AI agent

Autonomous AI agents are designed to comprehend user intents and act accordingly. For example, consider a restaurant with the need to automate online food order intake. To accomplish the task, create an autonomous AI agent that performs the following actions:

Get the required information from the customer.
Transfer the information to the required flow.
Deliver the customer requirement.

The autonomous AI agent works on the following three building blocks:

Action—A task that an AI agent performs by understanding user intents and completes by connecting to external systems.
Entity or slot—Represents a step in fulfilling the user's intent. Slot filling involves asking specific questions to the customer to fulfill the customer's intent based on utterances. It's the trigger for an AI agent to start performing an action.
Fulfillment—Determines how the AI agent completes the action by connecting with external systems.

1	On the Dashboard, click the AI agent that you've created.
2	Navigate to Configurations > Actions tab. The Actions page displays the Agent handover action. The Agent handover action is enabled by default allowing the AI agent to escalate the conversation to a human agent. Use the toggle option to disable it.
3	Click +New Action to add a new action for an AI agent.
4	On the Add a new action page, specify the following details: Action name—Enter the name of the action. For example, `Book tickets`. Action description—Provide a brief description about the expected action. Ensure that the action description is clear and accurate. Action scope—Choose Slot filling and fulfillment to configure the required input entities for slot filling and also define the details for fulfillment of the action. For more information, see the Configure slot filling and Configure fulfillment sections.

What to do next

Configure slots and configure fulfillment details.

Configure slot filling

Slot filling involves adding the required input entities for the AI engine. In the Slot filling section of the Actions page, add the input entities:

You can add the entities one by one in table format. For more information, see Add input entities in table format.
You can also use the JSON file and define the entities. See A Tour of JSON Schema for details.

Add input entities in table format

1	To add an input entity, click +New input entity.
2	On the Add a new input entity page, specify the following details: Entity name—Enter the name of the input entity. Entity type—Choose the data type of the entity. Entity description—Provide a brief description about the entity. Ensure that the entity description is clear and accurate. Entity examples—Enter an example for the entity. Click +Add to add more examples. Check the Required check box to make this entity mandatory.
3	Click Add to add the input entity. You can add as many input entities as you need.
4	Click Add to add the action to the AI agent.
5	Click Publish to make the AI Agent live. For more information, see the Publish your autonomous AI agent. After adding the action, use the Controls option to edit or delete the entities.

Add entities using a JSON editor

To add an input entity in JSON, click Use JSON instead.
Enter the input parameter schema in JSON format.
Click Add.

For more information, see A Tour of JSON Schema.

Input parameter structure

The input parameters must adhere to the following structure:

type—Data type of the parameters object. This is always 'object' to denote that the parameters are structured as an object.

properties—An object where each key represents a parameter and its associated metadata.

required—An array of strings listing the names of parameters that are mandatory.

properties Object

Each key in the properties object represents an input entity/parameter and contains another object with metadata about that parameter. The metadata should always include the following keywords:

type—Data type of the parameter. The allowed types are:
- string—Textual data.
- integer—Numeric data without decimals.
- number—Numeric data that can include decimals.
- boolean—True/false values.
- array—A list of items, all of which are typically of the same type.
- object—A complex data structure with nested properties.
description—A brief explanation of what the entity represents. This helps the AI engine understand the purpose and usage of the parameter. A description that's concise and consistent with the agent's instructions and action description is good for better accuracy.
Validation is enforced by the platform for ‘type’ only. ‘Description’ isn't enforced for all entities but it's highly recommended that it’s added. Other useful keywords for entity metadata are:
- enum—The enum field lists the possible values for a parameter. This is useful for parameters that should only accept a limited set of values. Developers can define custom lists of values that a parameter should accept to use this.
- pattern—Use the pattern field with string types to specify a regular expression that the string must match. This is useful for validating specific formats, such as phone numbers, postal codes, or custom identifiers.
- examples—The examples field provides one or more examples of valid values for the parameter. This helps the AI engine understand the kind of data it needs and can be especially useful for interpretation and validation purposes.

There are other keywords that can make the entity definition more accurate and robust. For more information, see A Tour of JSON Schema.

Example

The following example includes various types of entities and keywords:

{
    "type": "object",
    "properties": {
        "username": {
            "type": "string",
            "description": "The unique username for the account.",
            "minLength": 3,
            "maxLength": 20
        },
        "password": {
            "type": "string",
            "description": "The password for the account.",
            "minLength": 8,
            "format": "password"
        },
        "email": {
            "type": "string",
            "description": "The email address for the account.",
            "pattern": "\w+([-+.']\w+)*@\w+([-.]\w+)*\.\w+([-.]\w+)*"
        },
        "birthdate": {
            "type": "string",
            "description": "The birthdate of the user.",
            "examples": ["mm/dd/YYYY"]
        },
        "preferences": {
            "type": "object",
            "description": "User preferences settings.",
            "properties": {
                "newsletter": {
                    "type": "boolean",
                    "description": "Whether the user wants to receive newsletters.",
                    "default": true
                },
                "notifications": {
                    "type": "string",
                    "description": "Preferred notification method.",
                    "enum": ["email", "sms", "push"]
                }
            }
        },
        "roles": {
            "type": "array",
            "description": "List of roles assigned to the user.",
            "items": {
                "type": "string",
                "enum": ["user", "admin", "moderator"]
            }
        }
    },
    "required": ["username", "password", "email"]
}

This example includes the following entities:

username—A string type with minimum and maximum length constraint.
password—A string type with a minimum length and a specific format (password indicates that secure handling is required).
email—A string type with a regex pattern to ensure it’s a valid email address.
birthdate—A string type with examples to prescribe the format of the date.
preferences—An object type with nested properties (newsletter and notifications), including a boolean with a default value and a string with specific allowed values (enum).
roles—An array type where each item is a string limited to specific values (enum).

The username, password, and email are mandatory as defined by the ‘required’ array.

In this example, the entities have descriptive names, clear descriptions, and follow a consistent structure and naming convention. Follow these best practices to create well-defined entities that are easy for the AI engine to interpret and enforce.

Configure fulfillment

You can configure fulfillment flows for AI agent actions on the Webex Connect Flow builder. For more information, see Configure Fulfillment Flows for AI Agent Actions.

As part of PCI compliance, autonomous AI agent currently doesn't support fulfillment that involves debit card or credit cards. All user verification or payment that involve a debit card or credit card must be done through third-party integration.

1

Go to the Actions tab on the AI agent configuration page. Choose the action for which you want to configure the fulfillment flow.

2

In the Webex Connect Flow Builder Fulfillment section, configure the following settings:

Select service—Choose the required service that is configured in the Webex Connect client workspace.

The Webex AI Agent Studio can only access service and flows configured within the client-level workspace.
Select a flow—Choose the required service that is configured in the Webex Connect client workspace.

3

Click Save changes to complete the cofiguration.

What to do next

Configure the knowledge base.

Configure knowledge base

Before you begin

Create an autonomous AI agent.

1	On the Dashboard page, click the AI agent that you've created.
2	Navigate to the Configurations > Knowledge tab.
3	Choose the required knowledge base from the drop-down list. We strongly recommend you to choose a knowledge base that uses the same language as your AI agent for optimal performance. To navigate to the chosen Knowledge base, choose Manage Knowledge base from the drop-down list. To disconnect the knowledge base from the AI agent, choose None from the drop-down list. To create a new knowledge base, choose +Add New from the drop-down list. For more information, see Create knowledge base for AI agent. Once you've mapped a knowledge base to an AI agent, you can't associate the same knowledge base to another AI agent.
4	Click Save changes.
5	Click Publish to make the AI agent live. For more information, see Publish your autonomous AI agent.

What to do next

Confixure language and voice for an autonomous AI agent. For more information, see Configure language and voice.

Configure language and voice

1	On the AI agent configuration page, navigate to the Configuration > Language tab. The default language and voice are set to English. For other supported languages and voices, see the Supported languages and voices article.
2	Choose the desired language from the drop-down list.
3	Choose the appropriate voice from the drop-down list. You can use the default voice or choose a different voice from the available list. The list of available voices appears automatically based on the chosen language. Currently, the autonomous AI agent supports only one default language and voice configuration at a time. You can change the configured language and voice but can’t delete them. However, changing the language and voice may impact the AI agent’s functionality.
4	Click Save changes.
5	Click Publish to make the AI agent live. For more information, see the Publish your autonomous AI agent section.

Preview your autonomous AI agent

You can preview the autonomous AI agents at the time of creating the AI agent, while editing, and after deploying the agent. You can open the preview from:

AI agent dashboard—On hovering over an AI agent card, the Preview option for that AI agent become visible. Click to open the preview of the AI agent.
AI agent header— Click on the AI agent card to open the AI agent. The Preview option is always visible in the header section.
Minimized widget—After you launch the preview and minimize it, a chat head widget appears at the bottom right of the page. You can use this option to easily reopen the preview mode.

Webex AI Agent Studio also provides a shareable preview option. Click the menu on the top-right corner and select the Copy Preview Link option. You can share the preview link with other users such as testers or consumers of the AI agent.

Platform preview widget

The preview widget appears on the bottom-right section of the screen. You can provide utterances (or a sequence of utterances) to check the AI agent's responses and ensure it’s functioning correctly.

Also, you can minimize the preview widget, provide consumer information, and initiate multiple rooms to test the AI agent.

Publish your autonomous AI agent

After configuring and previewing your AI agent, you can publish the agent to make it live.

Before you begin

Create the autonomous AI agent.

1	On the AI agent configuration page, click Publish.
2	On the Publish changes screen, enter the Version name and click Publish. You can view the version details on the History page. For more information, see the History section.

View autonomous AI agent sessions and history

You can view the details of sessions established with the customers and the history of the configuration changes performed on the AI agent.

Sessions

The Sessions page provides a comprehensive record of all interactions between AI agents and users. To access Sessions:

On the Dashboard, click the autonomous AI agent for which you want to view the session details.
From the left navigation pane, click Sessions.

The Sessions page appears. Each session is displayed as a record that contains all the messages of the session. This information is useful to audit, analyze, and improve the AI agent.

The sessions table shows a list of all the sessions/rooms created for that AI agent. The table gets paginated if there are more rows than can be accommodated in one screen. Any of the fields in the table can be sorted or filtered using the Refine Results section on the left-hand side. The fields which are present represent the following information about any particular session:

Session ID—The unique room id or session id for a conversation.
Consumer Id—The id of the consumer who interacted with the AI agent.
Channels—Channel where the interaction took place.
Updated At—Time of the room closure.
Room Metadata—Contains additional information about the room.
Check the required check boxes:
- Hide test sessions—To hide the test sessions and display only the list of live sessions.
- Agent handover happened—To filter the sessions that are handed over to an agent. If agent handover happens, it displays the Headphone icon indicating the handover of the chat to a human agent.
- Error occurred—To filter the sessions in which the error occurred.
- Downvoted—To filter the downvoted sessions.

View session details

To view the session details:

Click on an individual row in the sessions table for a detailed view of that session. If the session is locked, you need to have permission to decrypt the session.
Click the Decrypt content to view the session data.

This functionality is applicable only when the Advanced data protection is set to true or enabled for the tenant.
The system displays the following sessions details:
- The left panel displays details about the transactions.
- The right panel displays details about slot filling and fulfillment related to all actions. Use the Expand all button to expand the transaction. The right panel displays details about the knowledge utilization with details about document names and files uploaded.

History

The History page allows you to view the details of the configuration changes performed on the AI agent. To view the history of a specific agent:

On the Dashboard, click the autonomous AI agent for which you want to view the history.
From the left navigation pane, click History.

The History page appears with the following tabs:

Version history—Click the Version History tab to view the various versions of the autonomous AI agent.
Change logs—Click the Change Logs tab to view the changes made to the AI agents.

Version history

Whenever you publish the autonomous AI agent, a version of the autonomous AI agent is saved and is available in the Version history tab. You can view the various versions of the AI agent from the Version history tab.

Version description—A brief description about the version of the AI agent.
AI engine—The AI engine used for that version of the AI agent.
Updated at—Date and time when the version was created.
Actions—Allows you to perform the following actions on the AI agent:
- Load as draft—All changes on the AI agent is lost. You must perform the configuration again.
- Export—Use to export the AI agent.

Change logs

The Change logs tab tracks the changes made to the autonomous AI agent. You can view the details of the changes for the past 35 days. The Change logs tab displays the following details:

Users with Admin or AI agent developer roles can only access the Change logs tab. Users with custom roles that have the ‘Get Audit log’ permission can also view the audit logs.

Updated at—The data and time of the change.
Updated by—The name of the user who incorporated the change.
Change Location—The specific section of the AI agent where the change was made.
Description—Additional information about the change.

You can search for a specific audit log using the Updated by, Change Location, and Description search options. You can sort the logs based on the Updated at and Updated by fields.

View Autonomous AI agent performance using analytics

The AI agent analytics section provides a graphical representation of the key metrics to evaluate the AI agent performance and effectiveness. To generate the analytics of the Autonomous AI agent:

Choose the AI agent from the Dashboard.
On the left navigation pane, click Analytics. An overview of the AI agent performance appears in both tabular format and graphical representation.

The first section displays the following statistics about sessions and messages for the AI agent.

Total sessions and sessions handled by the AI agent without human intervention.
Total agent handovers, which is a count of number of sessions handed over to human agents.
Daily average sessions.
Total messages (human and AI agent messages) and how many of those messages came from users.
Daily average messages.

The second section displays the statistics about the users. It provides a count of total users and information about average sessions per user and daily average users.

The third section displays the AI agent responses and agent handovers.

Set up scripted AI agent

Scripted AI agents enhance the no-code agent-building capabilities of the Webex AI Agent Studio platform. They enable multiturn conversations, gathering relevant data from customers to perform specific tasks. This includes:

Running simple commands—Follow instructions to complete predefined actions.
Processing data—Manipulate and transform data according to specified rules.
Interacting with other systems—Communicate with and control other solutions.

Scripted AI agents are knowledge-driven agents whose knowledge base consists of a corpus of questions and answers. Scripted AI agent can provide answers based on a user-created training corpus, which is a collection of examples and answers. This capability is useful in scenarios where:

Specific knowledge is required—The agent needs to answer questions within a predefined domain.
Consistency is important—The agent must provide consistent responses to similar queries.
Limited flexibility is needed—The agent's responses are constrained by the information in the training corpus.

Create a scripted AI agent

1	Log in to the Webex AI Agent Studio platform.
2	On the dashboard, click + Create agent.
3	On the Create an AI Agent screen, choose Start from scratch and click Next. You can also choose a predefined template to create your AI agent quickly. You can filter the AI agent type to `Scripted`. In this case, the fields on the Profile page autopopulate.
4	Choose Scripted agent type.
5	Specify the following details: Agent name—Enter the name of the AI agent. System ID—A system-generated unique identifier. This field is editable. AI engine—Choose an AI engine from the drop-down list. The available options are: Webex AI Agent Pro 1.0 (with Swiftmatch)—this is the default option. Webex AI Agent Pro 1.0 (with RASA) Webex AI Agent Pro 1.0 (with Mindmeld)
6	Click Create. You have now successfully created the scripted AI agent which is now available on the Dashboard. You can create up to 100 AI agents, which includes both scripted and autonomous types. On the AI Agent header, you can perform the following tasks: Preview Copy Preview Link Copy Access Token Delete Export For more information, see Tasks on AI agent card. Also, you can import the AI agents. For more information, see Import AI agent.

What to do next

Configure your scripted AI agent.

Configure scripted AI agent

The following sections guide you on how to configure scripted AI agent for your specific needs:

Update scripted AI agent profile
Configure scripts
Configure agent handover
Configure language and voice

Update scripted AI agent profile

Before you begin

Create a scripted AI agent.

1	On the Dashboard, click the AI agent that you've created.
2	Navigate to the Configurations > Profile tab and configure the following details: Agent name—Edit the name of the AI agent, if needed. System ID—Edit the system id of the AI agent, if needed. Description—Enter the details about the AI agent. Timezone—Choose your time zone from the drop-down list. URL for agent profile image—The default URL from where the system fetches the AI agent's logo or image. Change this if needed. Allow feedback—Enable this toggle to let an AI agent ask for feedback in the message. Custom Error Message—Enter a custom error message for server or proxy errors. AI engine—Displays the chosen AI engine. You can update the AI engine by clicking the icon next to the drop-down list. For more information, see Update AI engine settings.
3	Click Save changes.
4	Click Publish to make the AI agent live.

Update AI engine settings

Scripted AI agents use AI engines (powered by machine learning) to understand and respond to customer queries. Here's a quick overview of the AI engines used:

Webex AI Pro 1.0 (with Swiftmatch)—A fast and lightweight training engine that supports multiple languages.
Webex AI Pro 1.0 (with RASA)—A top open-source framework for building conversational AI.
Webex AI Pro 1.0 (with Mindmeld)—An advanced framework for creating high-quality conversational flows with various natural language understanding (NLU) features.

To change the AI engine for the AI agent:

1	On the Dashboard, click the AI agent that you've created.
2	Navigate to Configurations > Profile tab.
3	Click the icon next to the AI engine.
4	In the Manage AI Engine page, configure the following fields: Training Engine—Choose the required AI engine from the drop-down list. Inference—Specify the following information: Score below which fallback is shown—The minimum confidence needed to display a response, below which a fallback response appears. Difference in scores for partial match—The minimum gap between confidence levels of responses to clearly display the best match below which a partial match template is shown. Advanced Settings Expand contractions—Check this option to convert contractions in training data and consumer queries into their full forms for better accuracy. This option is available for all three AI engines. Spellcheck in Inference—Check this option to identify and fix the spelling mistakes before processing the text. This option is available for all three AI engines. Prioritize slot filling—Check this option to prioritize slot filling over intent detection. This option is available for all three AI engines. Entity roles—Check this option to enable different roles for a single entity. This option is available only for RASA and Mindmeld AI engines. No of results to store for each message—Enter the number of transactions (for which the AI agent has calculated confidence scores) to list under Transaction information in the Sessions. This option is available for all three AI engines. Multi-lingual model—Choose the model from the drop-down list to enable the multilingual inference. Configure more than one language for an AI agent to enable this option and this option is available only for the Swiftmatch AI engine. Vector model—Choose the vector model from the drop-down list to improve the accuracy of the AI agent. This feature is only available for the Swiftmatch AI engine. Remove stopwords—Check this option to remove the stopwords during training and inference. This feature is only available for the Swiftmatch AI engine. Remove special characters—Check this option to remove special characters from customer queries for better responses. This option is available only for the Swiftmatch AI engine. Entity substitution in inference—Check this option to replace entity values in training data and inference with entity IDs. This option is available only for the Swiftmatch AI engine. Wordform expansion—Check this option to expand training data with wordforms (such as plurals, verbs, and so on), along with the synonyms present in the data. This option is available only for the Swiftmatch AI engine.
5	Click Update to change the AI engine settings for the AI agent.
6	Click Save changes to update the AI engine settings.

Configure scripts

Scripts are the building blocks that power your AI agent's understanding and responses. This section describes three key components:

Intents capture the various goals or actions users want to accomplish when interacting with your AI agent. Mapping user intents enables the AI agent to recognize and respond appropriately to user requests. To create an intent, see Create an intent.
Entities are the specific pieces of information your AI agent needs to extract from user inputs. These include dates, product names, or custom values unique to the customer use case. Entities are important variables that your AI agent must understand to fulfill the user requests effectively. To create an entity, see Create an entity.
Responses are your AI agent's carefully crafted replies to user requests. They dictate how your AI agent communicates with the users after understanding their intent and gathering the necessary entities. To create a response, see Create a response.

Together, these components work together to create fluid and purposeful conversations between your agent and its users. For more information, see Understand intents, entities, and responses in AI Agent Studio.

Create an intent

1	On the Dashboard, click the AI agent that you've created.
2	Navigate to Configurations > Scripts > Intents.
3	Click +Create intent.
4	On the Add a new intent screen, specify the following details: Intent name—Enter a name for the intent. Settings—Toggle on and off the following settings for the intent: Reset slots after completion—Toggle on to reset the slot values collected in the conversation once the intent is complete. If this toggle is in disabled status, the slot retains the old values and displays the same response. End conversation—Toggle on to close the session after this intent. Webex Connect and voice flows can use this to close a conversation with customers. Context—Provide the entry and exit contexts for the intent. For more information, see Contexts. Entry context—Enter the required input in the text box and press the Enter or Return key to add the entry context. You can configure a maximum of 5 entry contexts for a particular intent. Entry contexts control whether an intent can be matched with the customer query based on the active context of the session. Exit context—Enter the required input in the text box and press the Enter or Return key to add the exit context. You can configure a maximum of 15 exit contexts for a particular intent. Exit contexts control the active contexts for a session. Intent & utterances—Add a phrase to determine the intent and click +Add. Generate variants—For the system to generate utterances automatically based on the intent, click Generate variants. In the Generate variants dialog box, provide a description of your intent to generate relevant training data or utterances. Enter the number of variants to be generated and choose how creative you want the underlying LLM to be while generating these variants. Click Generate. Add entities as slots—If any entities are present in the added utterances, they are auto annotated in the utterances and appear in the Slots section. You can select the part of the utterance as a slot for that intent and choose an already created entity or create a new entity. The intent requires at least 3 utterances and 2 slot annotations for training with the current AI engine. Each slot displays the following: Slots (optional)—Link the entities for the slot filling. This ensures that the AI agent gathers all the required information. Click +Link to link an entity to the intent. If there’s no entity in the list, the +Link option doesn’t appear. To create an entity, see the Create an entity section. The following slot details appear and you can configure the retries and response for the required slot: The linked entity name. Required—Check the check box to determine whether it’s required for intent completion. Retries—Enter the number of retries the AI agent attempts to get this slot value from the customer. Response—Choose the response for the slot from the drop-down list. To create a new custom response, click +Create new. Configure the following Settings: Toggle the Update slot values on to update the slot value during the conversation with the customer. The AI agent considers the last value filled in the slot to process the data. If you enable this feature, the system updates values for filled slots whenever customers provide new information for the same slot type. Toggle the Provide suggestions for slots on to provide suggestions for slot filling and alternate slot values in the final response, based on customer input. Response—In the Response section, choose the response that you want to return to customers on completion of the intent. Once chosen, click View the content of selected response to view the response content. To add a new custom response, click +Create new to create a new response. For more information, see Create a response.
5	Click Add to create an intent.
6	Click Publish to make the AI agent live.

Create an entity

1	On the Dashboard, click the AI agent that you've created.
2	Navigate to Configurations > Scripts > Entities.
3	Click+Create entity.
4	On the Create entity window, specify the following fields: Entity name—Enter the entity name. Entity type—Choose an entity type from the drop-down list. Based on the chosen entity type, enter a value in the additional field, if it appears. If the AI agent has difficulty identifying a person's name with the `Person names` entity type, try using the `Free form` entity type for improved recognition.
5	Click Save changes. You can use Edit and Delete options in the Actions column to perform related actions. You can edit only the entity name and not the entity type.

Create a response

1	On the Dashboard, click the AI agent that you've created.
2	Navigate to Configurations > Scripts > Responses. The system provides default conditional responses for Web channel that you can use for customer interactions. Also, You can use the Edit icon in each response to modify the response settings. You can't delete the default responses and Web channel.
3	To create a custom response, click +Create response. Add a new response screen appears.
4	On the Add a new response screen, enter the new Response name.
5	For the configured language, you can optionally add a conditional response by clicking Add condition. Enter the condition name and click Create. Click the condition and go to the Rules section, provide the IF and OR variable conditions, as needed. In the Left variable field, choose the response variable from the drop-down list. For more information about response variables, see the List of common response variables table. This table contains the list of variables that you can use in this field to define rules. These variables can also be used to customize AI agent responses. Choose the appropriate operator to execute the rule. Choose the data type from the drop-down list. Data types are populated based on the chosen operator. In the Right variable field, specify the response variable. The type of variable value with which the syntax in the left variable must match. This field is displayed only when you set the Operator to any of these values—Equals to, Not Equals to, In, Greater than, Less than. You can also use the list of variables to define rules in the right variable of the conditional response type if you set the `Data type` to `Variable`. Click Actions to create a response for the defined rule. In the Actions section, you can configure the response to be displayed for the customer query once the rule is fulfilled. To add a response for Web (default) channel: Choose the response type from the right pane. The supported response types are Text, Carousel, Quick Reply, Image, Video, Audio, and File. For the chosen response type, configure the required settings. For information on how to configure various responses for Web (default), see the Configure response types section. To add a response for the other channels: Click + next to Web (default). Choose the desired channel from the drop-down list. The supported channels are Messenger, SMS, WhatsApp, Apple Messages for Business, RCS, and Voice. Choose the response type for the chosen channel from the right pane. For supported response types for various channels, see the Supported response types for channels section. Configure the required settings for the chosen response type. For information on how to configure various responses, see the Configure response types section.
6	Click Create to create a response.

Configure response types

In Response Designer, you can configure channel-specific responses for the intents. For more information on how to create a response, see the Create a response section.

Refer to the following section for details:

Configure text response type
Configure carousel response type
Configure quick reply response type
Configure image response type
Configure video response type
Configure audio response type
Configure file response type
Configure reply button response type
Configure list messages response type
Configure numbered list response type
Configure list picker response type
Configure time picker response type
Configure media response
Configure rich link response type
Configure form response type
Configure custom events response types

Configure text response type

You can configure text messages as responses for all channels. To add a text response type for the chosen channel (default or custom), follow these steps:

1	For the chosen channel, select Text response type.
2	Enter the text message in the Variant textbox. You can customize agent responses using variables received from the channel or collected from customers during the conversation. You can also enter `${` to choose the required variable in the text area.
3	To add multiple variants, click Add Variant and enter the text message.
4	Click Create to create the response.

Configure carousel response type

You can configure carousel responses for Web (default) and Messenger channels. Each carousel response can contain an image, a description, and up to three buttons.Follow these steps:

1	For the chosen channel, click Carousel in the right pane. The rich carousel card appears.
2	Click Configure to view or edit the image URL. By default, the system displays the image URL.
3	Enter the Title and Description (optional) for the card.
4	To send a configured payload to the AI agent and invoke the corresponding response for the intent, click + Add quick reply. Choose the Text button type and enter the text and payload/identifier. Click Done. For URL redirection, choose the URL button type (applicable only for Web channel), enter the text and URL, and click Done. Click Renew Now to change the text and URL redirection.
5	Click Create to create the response.

Configure quick reply response type

You can configure quick reply responses for Web (default), SMS, Messenger, Apple Messages for Business, and RCS channels. Follow these steps:

1	For the chosen channel, click Quick Reply on the right pane.
2	Enter the quick reply message for the intent.
3	To send a configured payload to the AI agent, click + Add quick reply. Choose the Text button type and enter the text and payload/identifier. Click Done to add a text quick reply. For URL redirection, choose the URL button type (only applicable for Web chat), enter the text and URL, and click Done.
4	Click Create to create the response. A partial match happens when there's uncertainty about incoming user queries. AI agent responds with intents that are close to the user query as options. Partial match responses are displayed for the Web.

Configure image response type

You can configure image responses for Web (default), Messenger, and WhatsApp channels. Follow these steps:

1	For the chosen channel, click Image on the right pane. The image card appears with the default configurations.
2	Update the image URL.
3	Choose the image type (jpeg or png).
4	Click Create to create the response.

Configure video response type

You can configure video responses for Web (default), Messenger, and WhatsApp channels. Follow these steps:

1	For the chosen channel, click Video on the right pane. The video card appears with the default configuration.
2	Update the video URL.
3	Choose the required video type (mp4).
4	Click Create to create the response.

Configure audio response type

You can configure audio responses for Web (default) and WhatsApp. Follow these steps:

1	For the chosen channel, click Audio in the right pane. The audio card appears with the default configuration.
2	Update the audio URL.
3	Choose the audio type (mp3 or aac).
4	Click Create to create the response.

Configure file response type

You can configure the file as a response for Web (default) and WhatsApp channels. Follow these steps:

1	For the chosen channel, click File on the right pane. The File card appears.
2	Enter the file URL.
3	Choose the file type. Supported file types are .html, .pdf, plaintext, .jpeg, .png, .mp4, .mp3, and .aac.
4	Click Create to create the response.

Configure reply button response type

You can configure reply button responses for the WhatsApp channel. Follow these steps:

1	Click Reply Button in the right pane.
2	Configure the header details (Optional). Choose the type of header—Text (can be 20 characters long), Video, Image, Document. Enter the value depending on the header type selected.
3	Enter the body text. This field can contain up to 1024 characters.
4	Enter the footer text (optional). This field can contain up to 60 characters.
5	To add a new reply button, click Add button, enter the text and payload, and click Done You can configure up to three reply buttons (with a 20-character limit).
6	Click Create to create the response.

Configure list messages response type

You can configure the list messages as responses for the WhatsApp channel. Follow these steps:

1	Click List Messages in the right pane.
2	Navigate to the Configuration tab and configure the following settings: (Optional) Configure the header details (can be 20 characters long). Enter the body text (can be 1024 characters long). (Optional) Enter the footer text (can be 60 characters long). Enter the List title.
3	Navigate to the List Sections tab and configure the following settings: (Optional) Enter the section name (can be 24 characters long). Enter the row title (can be 24 characters long). Enter the row ID, which is a unique identifier for each row that will help you identify the users’ choice. Enter the row description (can be 20 characters long). To add a new section, click Add section.
4	Click Create to create the response.

Configure numbered list response type

You can configure the numbered list as a response for the WhatsApp channel. Follow these steps:

1	Click Numbered List in the right pane.
2	Edit the default text provided.
3	Click +Add list item.
4	Under the Text button type, configure the following: Enter the text and payload/identifier to send the payload to the AI agent when the user clicks the button. Click Done.
5	Click Create to create the response.

Configure list picker response type

You can configure list picker responses for the Apple Business Messages channel. Follow these steps:

1	Click List Picker in the right pane.
2	Navigate to the Configuration tab and configure the following settings: Enter the list picker title. (Optional) Enter the preview image URL and choose the image style (Icon, Small, Large). Enter the description for the list picker. Enter the title and description for the reply message based on the selected list picker option.
3	Navigate to the List sections tab and configure the following settings: Enter the title for items in the list. Enter the title for items in the list. In the List Item field, configure the following settings: Enter the image URL for the list item. Enter a title. Enter the identifier for an item. When a user selects an item, the identifier is sent to the AI agent, which displays the next steps. Enter the description for the list item. To add another item, click the + icon. To delete an item, click the Delete icon.
4	To add a new list section, click Add List section.
5	Click Create to create the response.

Configure time picker response type

You can configure time picker responses for the Apple Business Messages channel. Follow these steps:

1

Click Timer Picker in the right pane.

Navigate to the Configuration tab and configure the following:

Enter a title for the time picker.
(Optional) Enter the preview image URL and choose the image style (Icon, Small, Large).
Enter the description.
Enter the title and description for the reply message based on the selected time picker option.

2

Navigate to the Event details tab and configure:

Enter the event title.
Enter the timezone for the event.
Configure slots:
1. Enter the date and time for slot1.
2. Enter the duration for slot1.
3. Enter the identifier for the slot. When a user selects a slot, the identifier is sent to the AI agent, which displays the next step.
4. To add a new slot, click Add slot.

3

Click Create to create the response.

Configure media response

You can configure media responses for the Apple Business Messages channel. Follow these steps:

1	Click Media in the right pane.
2	Enter the text.
3	Choose the media type and enter the URL.
4	To add more attachments, click Add attachments.
5	Click Create to create the response.

Configure rich link response type

You can configure rich link responses for the Apple Business Messages channel. Follow these steps:

1	Click Rich Link in the right pane.
2	Enter the image URL and choose the image type (jpeg or png).
3	Enter the website URL for redirection.
4	Enter the URL title for the video or image.
5	Enter the video URL supporting MIME protocol and choose the type (video/mp4).
6	Click Create to create the response.

Configure form response type

You can configure form responses for the Apple Business Messages channel. Follow these steps:

1

Click Form in the right pane.

2

Enter the form in JSON format. Each JSON contains information about the pages that go as a part of the form with customizations. The pages can be of different types:

Splash: An introduction page with a title, description, action button, and image.
Select: A page for either a single "True/False" selection or multiple selections, supporting image assets.
Picker: A page for a single selection from multiple options.
Date Picker: Provides Apple's standard Date Picker with customizable date format and limits.
Input: A page featuring various input fields to collect information.

The structured content metadata allows the user to specify the form's pages and visual layout. This template comprises two sections:

BusinessFormEvent: Configures page identifiers, summary, pages, and images.
BusinessChatMessage: Configures received and reply message information.

Click Download sample JSON to download the sample JSON file and understand the form structure.

Refer to Apple's official documentation for more information.

Configure custom events response types

You can configure custom events as responses for the voice channel. Follow these steps:

1	Click Custom Events in the right pane.
2	Enter the incoming event name that the AI agent receives.
3	In the Voice settings, configure the following settings to be added to each agent response: Timeout (s): Specify the duration, in seconds, that the agent waits for a user response. If there's no response, you can set it to either prompt the user again or end the conversation. The default timeout is 5 seconds. Timeout response: Specify the response message to be sent to the user if a timeout occurs. Allow barge-in: Check this option to let the user interrupt the agent's response with new input. If unchecked, the user must wait until the agent has finished its response before responding. DTMF input: Check this option to let users enter specific details through their keypad. This is helpful for gathering information expressed as long numbers. Enabling DTMF input allows you to configure the following settings: Input mode: Choose the required input mode to let the user respond with DTMF only or using both – DTMF and voice. Timeout between digits (s): Set the maximum idle time allowed between entering each digit. If the user exceeds this time, the digits entered so far are sent to the flow as their input. Termination character: Specify the termination character, like # or , to mark the end of user input. When the user presses this key, the preceding keypad input is sent to the flow as user input. Max length*: Specify the maximum length of characters allowed for user input.
4	Click Create to create the response.

Configure agent handover

Before you begin

Create the scripted AI agent.

1

On the Dashboard, click the scripted AI agent that you've created.

2

Navigate to Configuration > Handover and toggle on or off the required settings:

Handover on partial match—A number of consecutive partial matches in a session can initiate a handover. This number is configurable.
Handover on fallback—A number of consecutive fallback messages in a session can initiate an agent handover. This number is configurable in the provided text box.
Handover after repeated agent responses—Consecutive repeated messages in a session can initiate agent handover. The number of repetitions needed for this trigger is configurable.
Handover when slot filling retry limit reached—When the retry attempts for the slot filling is reached to the maximum limit, it initiates an agent handover.

3

Click Save changes.

What to do next

Navigate to the preview your Scripted AI Agent.

Configure language and voice

You can configure multiple languages and language-specific voices for the scripted AI agent to handle customer interactions.

Before you begin

Create the Scripted AI Agent.

1	On the AI agent configuration page, navigate to the Configuration > Language tab. The default language and voice are set to English. For other supported languages and voices, see the Supported languages and voices article.
2	To add more languages to the AI agent, click +Add language.
3	Choose the desired language and locale from the drop-down list and click Add.
4	Click Add languages. The newly added language appears in the Language tab with the Enabled toggle set to 'on' by default. Adding more than one language enables the Polymatch multilingual model in the AI Engine Advanced settings.
5	Choose the appropriate voice from the Voice Name drop-down list. The available voices automatically appear based on the chosen language.
6	To set the desired language and voice as defaults for the AI agent, click Set as default under Controls column. You can’t remove the default language and voice, but you can change them as needed. Changing the default language might impact responses, intents, curation, testing, and preview experiences.

Preview your scripted AI agent

Webex AI Agent Studio allows you to preview your AI agents during and after development. This way, you can test the AI agent's functioning and determine if it generates the desired responses for the respective input queries. You can preview your scripted AI agent in the following ways:

AI Agent dashboard—Hover over an AI Agent card to view the Preview option for that AI agent. Click Preview to open the AI Agent preview widget.
AI Agent header—After entering the Edit mode for any AI Agent by clicking on the AI Agent card or the Edit button on the AI Agent card, the Preview option is always visible on the header section.
Minimized widget—After launching and minimizing a preview, a chat head widget appears at the bottom right of the page. This lets you easily reopen the preview mode.

In addition to this, you can copy the shareable preview link from within an AI agent. On the AI Agent card, click the Ellipses icon in the top right, and click Copy Preview Link. You can share this link with the other users of the AI agent.

Platform preview widget

The preview widget appears at the bottom right of the screen. You can provide utterances (or a sequence of utterances) to see how the AI agent responds, ensuring it performs as expected. The AI agent preview supports multiple languages and can autodetect the language of utterances to respond accordingly. You can also manually select the language in the preview by clicking the language selector and choosing from the list of available options.

You can maximize the preview widget for a better view. Also, you can provide consumer information and initiate multiple rooms to thoroughly test the AI agent.

Publish your scripted AI agent

After configuring and previewing your AI agent, you can publish the agent to make it live.

Before you begin

Create the scripted AI agent.

1	On the AI agent configuration page, click Publish.
2	Enter the version name and click Publish. You can view the version details on the History page. For more information, see the History section.

Common management sections for Scripted AI Agent

The following sections appear on the left panel of the AI Agent configuration page:

Test scripted agent

As AI agents evolve and become more complex, changes to their logic or Natural Language Understanding (NLU) can sometimes have unintended consequences. To ensure optimal performance and identify potential issues, the AI agent platform offers a convenient one-click AI agent testing framework. You can:

Easily create and run a comprehensive set of test cases.
Define test messages and expected responses for various scenarios.
Simulate complex interactions by creating test cases with multiple messages.

Define tests

You can define tests using the following steps:

Log in to the Webex AI Agent Studio platform.
On the Dashboard, click the scripted AI agent that you've created.
Click Testing in the left pane. By default, the Testcases tab appears.
Select a test case and click Execute selected tests.

Each row in the table represents a test case having the following parameters:

Table 1. Test cases
Parameter	Description
Message	A sample message that represents the types of queries and statements you can expect users to send to your AI agent.
Expected language	The language in which the you interact with the AI agent.
Expected intent	Specify the intent to be displayed in response to a particular user message. To assist you in finding the most relevant intent, this column features a Smart auto-complete function. As you enter, the system suggests matching intents based on the text entered so far.
Reset previous context	Click the check box to isolate test cases and run them independently of any existing AI agent context. When enabled, each test case is simulated in a new session, preventing interference from previous interactions, or stored data.
Include partial matches	Enable this toggle to consider test cases successful even if the expected intents only partially match the actual response.
Import from CSV	Import test cases from a comma-separated file (CSV) file. In this case, all existing test cases are overwritten.
Export to CSV	Export test cases to a comma-separated file (CSV) file.
Test callbacks	Enable this toggle to simulate incoming callbacks and test the flow behavior without requiring actual incoming calls.
Callback in flow	Click the check box in this column to indicate that an intent must trigger a callback.
Expected callback template	Specify the template key to activate when the callback occurs.
Callback timeout (s)	The maximum amount of time (in seconds) the AI agent waits for a callback response before considering the callback as timed out. The system allows a maximum of 20-second timeout.

Execute tests

On the Execution tab, click Execute selected tests to initiate a sequential execution of all selected test cases.

You can also execute test cases from the Test cases tab.

.

As each test case completes, the system displays its result next to the corresponding entry. To terminate a running test case, click Abort Run at the top-right corner of the screen.

To view test cases with specific outcomes, click the desired result (for example, Passed, Passed with partial match, Failed, Pending) in the summary ribbon. This filters the test case list to display only those matching the selected result.

The session ID associated with each test case is displayed in the results. This allows you to quickly cross-reference test cases and view transaction details. To perform this, choose the Transaction Details option in the Actions column.

Execution history

On the History tab, access all executed test cases.

Click the Download icon from the Actions column to export the executed test data as a CSV file for offline analysis or reporting.
Review the specific engine and algorithm settings used for each test case execution. This information helps developers optimize the AI agent's performance.
To view the advanced algorithm configuration settings used for a particular training engine, click the Info icon next to the training engine name. This provides insights into the parameters and settings that influenced the AI agent's behavior during testing.

View agent sessions

The Sessions section provides a comprehensive record of all interactions between AI agents and customers. Each session includes a detailed history of messages exchanged. You can export session data as a CSV file for offline analysis and auditing. Use this data to analyze user interactions, identify areas for improvement, and refine AI agent responses.

It can handle large data sets by displaying results on pages. You can use the Refine Results section to filter and sort sessions based on various criteria. Each row in the table displays essential session details, including:

Channels—The channel where the interaction occurred (for example, chat, voice).
Session ID—A unique identifier for the session.
Consumer ID—The unique identifier of the user.
Messages—The number of messages exchanged during the session.
Updated at—The last updated system time.
Metadata—Additional information about the session.
- Hide test sessions—Check this check box to hide the test sessions and display only the list of live sessions.
- Agent handover happened—Check this check box to filter sessions that we hand over to an agent. If agent handover happens, it displays the headphone icon indicating the handover of the chat to a human agent.
- Error occurred—Check this check box to filter the sessions in which an error occurred.
- Downvoted—Check this check box to filter the downvoted sessions.

Click on a row to access the detailed view of a specific session. Use check boxes to filter sessions based on agent handover, errors, and downvotes. Decrypting sessions requires user-level permission and advanced data protection settings. Click Decrypt content to view the session details.

View session details in the scripted AI agent

Click the session in each row to view the individual session details.

The Transaction Info tab provides a detailed breakdown of a specific interaction, categorizing information into four sections:

The Messages section:

Displays all messages sent by you during the session.
Shows the corresponding responses generated by the AI Agent.
Presents the chronological order of the messages, providing context for the interaction.

Intents Identified section:

Displays the intents identified for the customer's query.
Indicates the confidence level associated with each identified intent.
Lists the slots that are associated with the identified intent. Click the slot to view additional information about its value and how the system extracts it from the user's query.

Entities Identified section lists the entities that the system extracts from the customer's message and associates it with the active consumer intent. These entities represent the key pieces of information that the AI Agent identified within the user's query.

The Algorithm Results section provides insights into the underlying processes that led to the AI Agent's response. Here's a breakdown of the information displayed:

List of Intents—Shows the identified intents and their corresponding similarity scores.
Entity List—Displays the entities that were extracted from the user's message.

The Other Info displays:

Processed Query—Indicates the preprocessed version of the customer's input after the AI Agent's natural language understanding (NLU) pipeline processes it.
Language detection provider—The provider who offers technology that can automatically identify the language of a given text.
Language detected—The language detected by technology. 
Agent Handover—Indicates whether an agent handover occurred during the session. Check the Agent Handover by Rules check box if an agent handover was triggered by specific rules.
Template Key—Indicates the template key associated with the intent that triggered the AI agent's response.
Response Type—Indicates the type of response generated by the AI agent.
Response Condition—Indicates the specific condition or rule that triggered the AI agent's response.
NLU AI Engine—Identifies the NLU AI engine used to process the customer's query.
Vector model—A way of representing text as numerical vectors. 
Min threshold Scores—Displays the minimum threshold score.
Partial match score difference—The partial match score difference configured in the Handover and Inference settings. The system determines whether a query is out of scope or requires agent intervention based on these values.
Debug Logs—Provides a list of debug logs associated with the specific transaction ID. Advanced logs are typically retained for 180 days.

You can also download and view the transaction info in the JSON format using the download option.

View version history and change logs

Whenever you add or update intents or entities, the system retrains your scripted AI agent to keep it up to date. After each training session, test your AI agent thoroughly to ensure accuracy and effectiveness. Each time you publish a scripted AI agent, a version is saved and accessible in the Version History tab, where you can view all versions of the scripted AI agent.

The History page allows you to access the following updates made to your agents:

Track when you published the version history and the changes made in the form of a note left by developers when publishing. 
View the AI engine used for each published version, along with its settings. You can also see the time elapsed to get each version ready to publish. 
Monitor changes to settings, intents, entities, responses, and curation in the Change logs tab.
Publish, Preview, or Load an older version as draft if needed.
View Training History—Track when you trained a corpus and the changes made.
Compare Training Engines—Review the training engines used for different iterations and their corresponding training durations.
Track changes—Monitor changes to settings, intents, responses, and curation.
Revert to previous versions—Easily revert to an older training set if needed.

Change Logs

The Change Logs section provides a detailed record of modifications made to your scripted AI agent within the past 35 days. To access change logs:

Navigate to the Dashboard and click the AI agent that you've created.
Click the Version History tab to view the AI agent's history.
Click the Change Logs tab to see a detailed log of changes:
- Updated at—The date and time the system made the change.
- Updated by—The user who made the change.
- Change Location—The section of the AI agent where the modification occurred (for example, Corpus, Intent, and Response).
- Description—Additional details about the change.

Use the Updated by and Change Location search options to find specific change log entries.
The Version History tab displays a maximum of 10 corpora for each AI agent.

View curated agent details

The system adds messages to the Curation console based on the following criteria:

Fallback Messages—When the AI agent fails to understand your message and triggers the fallback intent.
Downvoted Messages—Messages that users have downvoted during AI agent previews.
Agent Handover—Messages that result in a human agent handover due to configured rules.
From Session—Messages flagged by users as not receiving the desired response from session or room data.
Low Confidence—Messages with a confidence score falling within the specified low-confidence threshold.
Partial Match—Messages where the AI agent couldn't figure out the right intent or response.

Resolve issues

The Issues tab allows you to review and address messages flagged for curation. You can do the following:

Choose to resolve or ignore issues based on their severity and relevance.
Examine the original user utterance, the AI Agent's response, and any attached media.

You can view the full content of encrypted sessions by using the Decrypt Content option, if you’ve decrypt access enabled.

If the Advanced Data Protection is enabled in the backend, the system grants the decrypted access at the user level.

To resolve an issue, you can:

Link to an existing intent—To connect an issue to an existing intent, select the Link option and search for the desired intent.
Add to a new intent—Use the Add to a New Intent option to create a new intent directly from the Curation Console.
Ignore issues—Resolve or ignore issues to remove them from the Curation Console.

You can't link to default intents (welcome message, fallback message, partial match).
For a scripted AI agent, select the appropriate intent from the drop-down list and tag any relevant entities.
After making changes, the system retrains your AI agent to ensure that it reflects the new knowledge in its responses.
Resolve or ignore multiple issues simultaneously for efficient management.

The Resolved tab displays all issues addressed by the system. You can view a summary of each resolved issue, including whether we linked it to an existing intent, created a new intent, or ignored it. If you see responses that you don't like that the system didn't catch, you can manually add specific examples to the Curation Console.

To add issues from sessions:

Identify the Utterance—Locate the utterance that triggered the incorrect response.
Check Curation Status—If the issue isn't already in the Curation Console, the system displays the Curation Status toggle.
Toggle the Flag—Enable the Curation Status toggle to add the utterance to the Curation Console for review and resolution.

If the issue is already in the Curation Console, the toggle's appearance changes to show its status.

View your scripted AI agent performance using Analytics

The Analytics section provides a graphical representation of key metrics to evaluate the AI agent performance and effectiveness. The key metrics are divided into four sections represented as tabs, namely Overview, Responses, Training, and Curation.

On the Analytics page, you can select the AI agent you want to see the analytics for. You can customize the analytics view by choosing the channel, date range, and data granularity. By default, the system displays analytics data for the last month for all channels, with each day as a data point.

Overview

The overview contains key metrics and graphs that provide a snapshot of overall AI agent usage and performance to the developers.

From the Dashboard, choose the AI agent that you've created.
On the left navigation pane, click Analytics. An overview of the AI agent performance appears in both tabular format and graphical representation.

Sessions and messages

The first section in the overview displays the following statistics about sessions and messages for the AI agent:

The count of the total sessions and the sessions that the AI agent handles without human intervention.
Total agent handovers, which is a count of the number of sessions handed over to human agents.
Daily average sessions
Total messages (human and AI agent messages) and how many of those messages came from users.
Daily average messages

The system follows this with a graphical representation of sessions (stacked column representing sessions handled by the AI agent and sessions handed over) and the total responses sent out by the AI agent.

Users

The second section in the overview contains stats about users for the AI agent. It provides a count of total users and information about average sessions per user and daily average users. This is followed by a graph displaying new and returning users for each unit depending on the selected granularity.

Performance

The third section provides statistics about the AI agent’s responses to users. Here one can see the total responses sent out by the AI agent and the split up between responses where the AI agent:

Identified the user’s intent.
Responded with a fallback message.
Responded with a partial match message.
Informed the user of an agent handover.

The same is aggregated in a pie chart and an area graph provides information based on selected granularity.

Training

The training section represents of the ‘health’ of an AI agent corpus. It’s recommended that developers configure 20+ training utterances for each intent in their AI Agents. This section displays all intents as rectangles, with color and size indicating the amount of training data. The closer an intent is to white color, the more training data it needs for your AI Agent’s accuracy to improve.

Responses

This section gives a detailed view of what the customers are asking about and how often they are asking it. It graphically shows the most popular intents for AI agents for answering questions and response templates for AI Agents for performing actions.

This section provides a detailed view of customer inquiries and their frequency. It visually displays the most popular intents and response types that AI agents use to solve customer queries.

Curation

This section visually summarizes the number of curation issues that arise each day and the number that AI agents resolve.

Use AI agents for customer interactions

This section outlines how to integrate AI agents with both voice and digital channels to manage customer conversations.

Use AI agents for voice and digital interactions

After you've created and configured your autonomous or scripted AI agents in the Webex AI Agent Studio platform, the next step is to integrate them with the voice and digital channels. This integration allows the AI agents to handle both voice-based and digital conversations with your customers, providing a seamless and interactive user experience.

For more information, see Use AI agents for voice and digital interactions article.

Manage custom reports for AI agents

You can use global variables to generate custom reports and analyze calls routed to the AI agent.

Currently, out-of-the-box reports for the AI agent are not available in Analyzer.

Create a global variable

1	Sign in to Control Hub.
2	From the Contact Center navigation pane, choose Flows > Global Variables.
3	Click Create a new global variable and provide the name and description for the variable. Create a variable with the name CustomAIAgentInteractionOutcome. Choose String as the variable type.
4	Toggle Make Reportable on to display the variable in Analyzer for reporting purposes.
5	Click Save.

Add the global variable to the flow

The following instructions are also available within the linked sample flow import.

1	Sign in to your customer organization using Control Hub.
2	Navigate to Contact Center > Customer Experience > Flows. The Flows page appears.
3	Click the Go to Flow Designer icon beside the flow. The Flow Designer window appears.
4	In the Global Flow Properties pane, scroll down to Variable Definition > Predefined Variables section.
5	In the Global Variables section, click Add Global Variables. Add the global variable CustomAIAgentInteractionOutcome to your flow.
6	Use the Set Variable activity to assign the value ABANDONED to the variable CustomAIAgentInteractionOutcome.
7	Configure your Virtual Agent V2 activity in the flow.
8	Connect the Handled outcome of the Virtual Agent V2 Activity and use the Set Variable activity to assign the value HANDLED to the variable CustomAIAgentInteractionOutcome.
9	Connect the Escalated outcome of the Virtual Agent V2 activity and use the Set Variable activity to assign the value ESCALATED to the variable CustomAIAgentInteractionOutcome.
10	Connect the errored path of the Virtual Agent V2 activity and use the Set Variable activity to assign the value ERRORED to the variable CustomAIAgentInteractionOutcome.
11	Complete the rest of the flows based on your business logic and publish them. Any calls going through this flow will have the value of the variable CustomAIAgentInteractionOutcome set to Abandoned, Handled, Escalated or Errored, depending on the path the call takes.

Create custom visualizations

You can create custom reports for AI agent call records and AI agent outcome distribution in Analyzer.

Create AI Agent Call Records visualization

1	Download the AI Agent Call Records.json file from AI Agent Call Records.
2	Log in to Analyzer.
3	On the Home page, click the Visualization icon in Analyzer.
4	Click Import.
5	Click Browse to select the file (.json format) to be imported.
6	Click Import to import the AI Agent Call Records.json file.
7	Click Edit to modify the imported Visualization.
8	Click on Edit Filters for CustomAIAgentInteractionOutcome.
9	Click the is in radio button and add the values ABANDONED, ESCALATED, ERRORED, HANDLED.
10	Save and initiate a few test calls.
11	Run the visualization to view the results.

Create AI Agent Outcome Distribution visualization

1	Download the AI Agent Outcome Distribution.json file from AI Agent Outcome Distribution.
2	Log in to Analyzer.
3	On the home page, click the Visualization icon in Analyzer.
4	Click Import.
5	Click Browse to choose the file (in JSON format) to be imported.
6	Click Import to import the AI Agent Outcome Distribution.json file.
7	Click Edit to modify the imported Visualization.
8	Click on Edit Filters for CustomAIAgentInteractionOutcome.
9	Click the is in radio button and add the values ABANDONED, ESCALATED, ERRORED, HANDLED.
10	Save and initiate a few test calls.
11	Run the visualization to view the results.

Understand AI Compliance

This sections helps you understand AI development, data privacy, security, and safety.

AI development, data privacy, security, and safety

AI Product Development

For every AI-powered feature, we undergo an AI Impact Assessment against our Responsible AI principles, and adhere to the Responsible AI Framework, in addition to existing Security, Privacy, and Human Rights by Design processes.

Privacy and Security

We don’t retain customer input data after the inference process, and the third-party model provider, Microsoft, doesn’t access, monitor, or store Cisco customer data. For more detail on feature-specific data retention policies, see Webex AI Agent AI transparency technical note in the Cisco Trust Portal.

Data Sources for Training and Evaluation

Our third-party model provider, Microsoft doesn't use customer content to improve Azure OpenAI models and doesn’t store or retain Cisco customer data in Azure infrastructure.

Safety and Ethical Considerations

All generative AI features are prone to errors, so we prioritize content safety for AI features by opting in to Content filtering, provided by Azure OpenAI.

Model Evaluation and Performance

We prioritize the performance and accuracy of AI Assistant by involving humans in the review, testing, and quality assurance of the underlying model.

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Webex AI Agent Studio Administration guide

Get started with Webex AI Agent Studio

Key benefits for businesses

Understand AI agent types and examples

Examples

Prerequisites

User roles and permissions

Access Webex AI Agent Studio

Sign-in from Control Hub

Sign-in from Webex Connect

Home page layout

Know your Dashboard

Tasks on AI agent card

Create a new AI agent

Import AI agent

Keyword search

Filter by agent type

Manage knowledge bases

Create knowledge base for AI agent

Access knowledge base

Set up autonomous AI agent

Create an autonomous AI agent

Configure autonomous AI agent

Update autonomous AI agent profile

Add actions to autonomous AI agent

Configure slot filling

Add input entities in table format

Add entities using a JSON editor

Input parameter structure

properties Object

Example

Configure fulfillment

Configure knowledge base

Configure language and voice

Preview your autonomous AI agent

Platform preview widget

Publish your autonomous AI agent

View autonomous AI agent sessions and history

Sessions

View session details

History

Version history

Change logs

View Autonomous AI agent performance using analytics

Set up scripted AI agent

Create a scripted AI agent

Configure scripted AI agent

Update scripted AI agent profile

Update AI engine settings

Configure scripts

Create an intent

Create an entity

Create a response

Configure response types

Configure text response type

Configure carousel response type

Configure quick reply response type

Configure image response type

Configure video response type

Configure audio response type

Configure file response type

Configure reply button response type

Configure list messages response type

Configure numbered list response type

Configure list picker response type

Configure time picker response type

Configure media response

Configure rich link response type

Configure form response type

Configure custom events response types

Configure agent handover

Configure language and voice

Preview your scripted AI agent

Platform preview widget

Publish your scripted AI agent

Common management sections for Scripted AI Agent

Test scripted agent

Define tests

Execute tests