Conversational Agent
Description
Use the Conversational Agent activity to build AI-powered conversational experiences in IB-X. This activity enables your agent to receive user messages, process them using the configured AI model and instructions, optionally invoke tools, and generate contextual responses.
The Conversational Agent activity is commonly used for:
- AI chatbots
- Knowledge assistants
- Support agents
- Voice and chat experiences
- Workflow-driven AI conversations
- AI agents with tool execution capabilities
The activity must always be connected to a supported trigger activity such as When Chat Message Received.
Prerequisites
Before using the Conversational Agent activity:
- Configure an AI model connection for the activity.
- You can either:
- Use your own provider API keys and model connections, or
- Use Integration Gateway managed AI services, which require and consume IB-X Currency based on usage.
- Ensure the required AI model is available and properly configured.
- Add a supported trigger activity before the Conversational Agent activity.
- Optionally configure tools that the agent can invoke.
Workflow Structure
A Conversational Agent workflow typically contains:
- A trigger activity to receive incoming user messages.
- A Conversational Agent activity to process the request.
- Optional tool activities connected through the Tools connector.
- Optional post-processing activities such as logging, notifications, or workflow actions.
Example:
Trigger Requirement
The Conversational Agent activity requires a trigger activity.
The trigger activity is responsible for:
- Receiving the incoming conversation request
- Capturing the user message
- Creating the conversation context
- Passing the request to the Conversational Agent activity
Without a trigger activity, the Conversational Agent activity cannot start a conversation.
Supported Trigger Activities
Currently, the Conversational Agent activity supports the following trigger activity:
Additional conversational channels such as voice and telephony-based integrations are planned for future releases.
The availability of conversational channels depends on the installed IB-X components, configured integrations, and supported platform capabilities.
Properties
Input
Display Name
Specify a custom display name for the activity.
Instructions
Provide the system instructions that define the behavior, capabilities, response style, and tool usage patterns of the conversational agent.
The Instructions field acts as the core behavioral definition of the agent and significantly influences how the agent understands user requests, responds to conversations, invokes tools, and handles workflow decisions.
Use this field to:
- Define the role and responsibility of the agent
- Configure conversational tone and response behavior
- Define response formatting rules
- Specify tool usage behavior and boundaries
- Configure safety, compliance, and fallback behavior
- Restrict unsupported or unsafe operations
For detailed guidance and best practices on writing effective agent instructions, refer to the Conversational Agent Instructions Guide.
Model Configuration
Model
Select the AI model that the Conversational Agent activity should use.
The list of available models depends on the configured AI model connections, which may include:
- Customer-managed provider connections using your own API keys
- Integration Gateway managed AI services provided by IB-X
Available models vary based on the selected provider, configured connection, and supported capabilities.
Temperature
Specify the creativity level of the response generation.
- Lower values produce more deterministic responses.
- Higher values produce more creative responses.
Memory Settings
Use the Memory Settings section to configure how conversation history and contextual memory are maintained across interactions.
Memory management helps the conversational agent:
- Preserve conversational continuity
- Maintain contextual awareness across multiple user interactions
- Optimize token consumption
- Support long-running conversations efficiently
- Prevent conversation context from exceeding model token limits
History Limit
Specify the maximum number of recent conversation messages to load into the active conversation context.
This determines how much historical conversation data is available to the agent during response generation.
If not specified, a default value of 50 messages is used.
Higher values improve contextual continuity but may increase token consumption and response latency.
Context Summarization
Enable this option to automatically summarize older conversation history when the conversation context grows beyond configured limits.
Context summarization helps:
- Reduce token usage
- Prevent context window exhaustion
- Maintain long-running conversations efficiently
- Preserve important historical context in condensed form
When enabled, the system replaces older conversation segments with a summarized representation while retaining recent messages in full detail. The summarization is triggered based on the configured summarization thresholds listed below.
Max Context Tokens
Specify the approximate token threshold for the conversation context.
When the estimated context size exceeds this value, the system automatically triggers context summarization.
This helps ensure that the conversation remains within the supported token limits of the selected AI model.
Max Unsummarized Messages
Specify the maximum number of new messages allowed since the last summarization operation.
When this limit is reached, summarization is triggered even if the token threshold has not yet been exceeded.
This helps maintain efficient context management during extended conversations.
Summary Target Tokens
Specify the approximate target token size for the generated summary.
During summarization, the AI model attempts to compress older conversation history within this token budget while preserving important conversational context.
Lower values produce shorter summaries with lower token usage.
Min Messages to Keep
Specify the minimum number of most recent conversation messages that should always remain in their original form.
These recent messages are excluded from summarization to ensure that the AI model retains full visibility into the latest conversational context.
This helps preserve conversational accuracy and response relevance for recent interactions.
Tools Connector
The Conversational Agent activity provides a dedicated Tools connector.
Use this connector to attach tool activities that the AI agent can invoke dynamically.
Examples of tools include:
- HTTP Request
- Database operations
- Knowledge retrieval
- File operations
- Email activities
- Custom actions
- Workflow execution
The AI model determines when and how to invoke the connected tools based on the conversation context and instructions.
Example Scenario
AI Support Assistant
In this example:
- The user sends a chat message.
- The trigger activity receives the message.
- The Conversational Agent processes the request.
- The agent optionally invokes tools.
- The generated response is returned to the user.
- The response is logged using a Write Log activity.
Best Practices
- Always provide clear and specific instructions.
- Keep system prompts concise and deterministic where possible.
- Use tool calling only when required.
- Restrict unnecessary tool access.
- Configure reasonable token limits.
- Use conversation memory carefully for long-running conversations.
- Validate AI-generated outputs before performing critical operations.
Notes
- The Conversational Agent activity requires a conversational trigger activity.
- Tool execution depends on the connected tool activities.
- Available models depend on the configured AI providers.
- Response quality depends on the configured instructions, model, and conversation context.
- Conversation history may impact token consumption.