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Building a Conversational Agent

Overview​

In IB-X, a Conversational Agent is implemented as a standard Agent workflow.

Unlike platforms that provide separate chatbot or assistant artifacts, IB-X uses the same Agent model for conversational, automation, and AI-driven workloads. A conversational experience is created by combining a conversational trigger with the Conversational Agent activity and any additional workflow activities required to perform business operations.

This approach enables conversational experiences to seamlessly integrate with workflow automation, enterprise systems, approvals, and human-in-the-loop processes.

Creating an Agent​

To create a new Agent:

  1. Open AI Command Center.
  2. Click New Agent.
  3. Enter the Agent details.
  4. Click Create.

The Agent Designer opens with a blank workflow canvas.

Designing the Workflow​

A Conversational Agent workflow typically consists of:

  1. A conversational trigger activity.
  2. A Conversational Agent activity.
  3. Optional tool and workflow activities.
  4. Optional response, logging, notification, or integration activities.

Typical workflow structure:

When Chat Message Received
            │
            ▼
   Conversational Agent
            │
            ├── Tool Activities
            │
            ├── Workflow Activities
            │
            └── Integrations

Adding a Conversational Trigger​

Every conversational workflow begins with a conversational trigger.

The trigger is responsible for:

  • Receiving incoming conversation requests
  • Creating or resuming conversation sessions
  • Initializing conversation context
  • Launching workflow execution

Currently, IB-X provides the following conversational trigger:

  • When Chat Message Received

Additional conversational channels may be supported through future trigger activities without changing the overall workflow design model.

Add the trigger activity to the workflow canvas from the Triggers category.

Configuring the Conversational Experience​

The conversational experience is configured through the trigger activity.

The trigger provides configuration options that control:

  • Agent identity
  • Welcome experience
  • Greeting behavior
  • Chat capabilities
  • Voice capabilities
  • Speech services
  • User interface appearance

The configuration is organized into the following sections:

  • AI Persona
  • Welcome
  • Greeting
  • Features
  • Voice
  • Transcriber

These settings determine how users interact with the conversational experience across supported channels.

For detailed information, see: When Chat Message Received Trigger

Adding the Conversational Agent Activity​

After adding the conversational trigger, add the Conversational Agent activity from the AI category.

The Conversational Agent activity is responsible for:

  • Understanding user requests
  • Processing conversational context
  • Generating responses
  • Maintaining conversation memory
  • Invoking tools and actions
  • Interacting with enterprise knowledge

The activity acts as the intelligence layer of the conversational workflow.

Configuring Agent Behavior​

Agent behavior is primarily controlled through instructions configured in the Conversational Agent activity.

Instructions can be used to define:

  • Agent responsibilities
  • Communication style
  • Response formatting
  • Business rules
  • Tool usage guidance
  • Compliance requirements
  • Safety restrictions

Well-defined instructions help ensure consistent and predictable agent behavior.

For detailed guidance, see:

Selecting an AI Model​

The Conversational Agent activity requires an AI model.

Models may be configured using:

  • Customer-managed AI provider connections
  • Integration Gateway managed AI services

The selected model determines the capabilities, performance characteristics, and response quality of the conversational experience.

Configuring Memory​

Conversation memory enables the agent to maintain context across multiple interactions.

Memory settings can be used to:

  • Preserve conversation history
  • Maintain conversational continuity
  • Manage token consumption
  • Support long-running conversations

Proper memory configuration helps improve user experience while maintaining efficient model usage.

Connecting Enterprise Knowledge​

Conversational Agents can retrieve information from enterprise knowledge sources through the Knowledge Ingestion Service.

Knowledge grounding enables the agent to answer questions using:

  • Product documentation
  • Websites
  • Knowledge bases
  • Policies and procedures
  • Internal business content

This helps improve response accuracy and reduces unsupported responses.

For more information, see:

Adding Tools and Actions​

Conversational Agents can invoke workflow activities as tools.

Tools enable the agent to perform actions in addition to answering questions.

Examples include:

  • Creating tickets
  • Retrieving customer information
  • Sending emails
  • Updating records
  • Generating reports
  • Executing workflows

Tool execution allows Conversational Agents to become active participants in business processes rather than acting solely as information assistants.

Testing the Agent​

IB-X provides built-in testing capabilities that allow you to validate conversational behavior directly from the Agent Designer.

To test an Agent:

  1. Open the Agent in the Agent Designer.
  2. Click Run from the bottom of the designer canvas.
  3. The conversational interface opens using the currently configured chat experience.
  4. Interact with the Agent using text chat or voice, depending on the configured capabilities.

The testing experience uses the configured conversational settings, including:

  • AI Persona
  • Welcome configuration
  • Greeting behavior
  • Knowledge grounding
  • Tool execution
  • Voice configuration

Reviewing Execution History​

All test executions can be reviewed for troubleshooting and analysis.

You can access execution details through:

  • Automation → Show Debug Runs
  • Audit History

These views provide visibility into Agent execution details, workflow behavior, conversation processing, tool execution, and troubleshooting information.

Before publishing an Agent, validate:

  • Conversational behavior
  • Knowledge retrieval accuracy
  • Tool execution scenarios
  • Voice interactions
  • Error handling paths
  • Response quality and consistency

Testing and reviewing execution history helps ensure that the Agent behaves as expected before being exposed to end users.

Publishing and Sharing​

Once the workflow is complete, the Agent can be published.

Depending on the configured trigger and channel capabilities, users can interact with the Agent through supported conversational interfaces.

Testing and production URLs are available through the trigger configuration.

The conversational experience can also be embedded into external applications and websites when supported by the selected channel.

Best Practices​

  • Keep instructions focused and specific.
  • Connect only the tools required by the Agent.
  • Ground responses using trusted enterprise knowledge.
  • Configure conversation memory appropriately.
  • Validate tool execution scenarios thoroughly.
  • Test both normal and exceptional conversation paths.
  • Continuously monitor and improve the conversational experience after deployment.

Next Steps​

After building your first Conversational Agent, explore:

  • Architecture
  • AI Persona
  • Knowledge Grounding
  • Tools and Actions
  • Voice Capabilities
  • Operations