Output

Overview

The Output Node in UPTIQ Workbench allows workflows to display messages within a conversation, either for internal processing or user-facing communication. This node helps control what information is presented, to whom, and in what format, ensuring clarity in interactions and internal workflow execution.

By leveraging the Output Node, developers can: ✅ Guide user interactions by displaying status updates, confirmations, or responses. ✅ Provide internal reasoning context without exposing information to users. ✅ Format and structure responses dynamically within AI workflows.

Configurations

Field	Description
Type	Determines who sees the message. Options: External (visible to users) or Internal (for reasoning engine only).
Text	The actual message that will be displayed.
Formatting Instructions	Determines how the message should be structured. Example: Preserve formatting as provided by the user.

1. Type

Internal Messages

The Internal Message Type in the Output Node is a powerful feature that allows developers to create a chain effect of user queries. When an Internal Message is generated, the output of this node is automatically passed back to the Intent Classification system, triggering a new round of sub-agent classification, intent identification, and workflow execution—just as if the user had manually entered the message.

How It Works

1. The Output Node generates an Internal Message instead of displaying it to the user.

2. This message is fed back into the AI agent, acting as a new user query.

3. The Intent Classification system processes the message and routes it to the appropriate sub-agent and workflow.

4. The AI executes the next set of actions proactively, reducing the need for additional user input.

Why This Matters for AI Workflow Design

✅ Creates a Proactive AI Agent

• AI agents don’t always need to wait for a user’s next query.

• Instead, they can predict logical next steps and execute them automatically.

✅ Reduces User Input for Multi-Step Processes

• Users don’t need to enter repetitive queries.

• AI can generate follow-up queries dynamically, making interactions more efficient.

✅ Enables Smarter Flow Execution

• AI auto-generates queries based on user intent and retrieved data.

• This makes workflows more intelligent and context-aware.

External Messages

• Displayed to the user as part of the conversation.

• Used for providing responses, confirmations, or next steps.

• Example: "Thank you for your patience. Your report is now ready."

1. Text Enter the message that should be displayed to the user or forwarded to the intent classification module of the reasoning engine

2. Formatting Instructions Specify your formatting guidelines in this field. Workbench will use these instructions to be applied during output generation to customize the format and include any additional information needed.

Output Format

After execution, the message is displayed based on its type. For External Messages, the user will see:

Thank you for your patience. Your report is now ready.

For Internal Messages, the AI will receive the string output based on what is set in Text field.

Example Use-Cases

1. Notifying Users About Report Generation

A workflow generates a financial report and needs to notify the user when it's ready.

• Configuration:

  • Type: External

  • Text: "Thank you for your patience. Your report is now ready."

  • Formatting Instructions: Preserve formatting provided by user.

• Outcome: The user receives a message confirming report availability.

2. Providing Internal Instructions to AI Reasoning Engine

A workflow processes a loan application and requires an internal update for reasoning.

• Configuration:

  • Type: Internal

  • Text: "Processing loan application with ID: LA123456."

• Outcome:

  • The AI receives contextual guidance without exposing this message to the user.

3. Auto-Follow-Up on a Loan Application Status

Scenario: A user asks: "What is the status of my loan application?" The workflow fetches the loan status and, if it’s pending, generates a follow-up query to suggest additional actions.

• Configuration:

  • Type: Internal

  • Text: "The user’s loan application is still pending. Ask them if they want to connect with a loan officer for assistance."

• Outcome:

  • Instead of requiring the user to ask "Can I connect with a loan officer?", the AI proactively generates this question.

  • The new query goes through intent classification, triggering a workflow to offer an appointment booking option.

4. Intelligent Next-Step Execution in Financial Reports

Scenario: A user requests: "Show me my last 3 transactions." Once transactions are retrieved, the AI automatically asks if the user wants further insights, such as categorizing spending trends.

• Configuration:

  • Type: Internal

  • Text: "User requested their last 3 transactions. Generate a query to analyze spending patterns and show category-wise breakdown."

• Outcome:

  • Instead of waiting for the user to ask for a spending analysis, the AI triggers the next logical step proactively.

  • The Intent Classification system processes the new query and runs the appropriate workflow.

Key Takeaways for Developers

✅ Control Information Visibility – Decide whether messages should be visible to users (External) or restricted to AI reasoning (Internal).

✅ Enhance User Experience – Use External Messages to provide real-time updates, confirmations, or guided interactions.

✅ Improve Workflow Debugging & Context Awareness – Use Internal Messages to log key workflow steps and guide AI behavior.

✅ Customizable Formatting – Messages can preserve user-provided formatting, ensuring structured communication.

By integrating the Output Node, developers can improve the clarity, control, and effectiveness of AI-driven conversations, enhancing both user experience and internal process efficiency. 🚀