The End function is executed at the conclusion of a conversation, enabling final data processing, cleanup, and integration tasks. It runs asynchronously, meaning it does not delay the end of the call but ensures that any necessary post-conversation operations are completed.

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Key features and functionality

  1. Asynchronous execution: Allows the agent to finalize data and trigger follow-up processes without interrupting the user experience.
  2. Post-conversation data handling: Captures and processes important details from the conversation for reporting, logging, or integration.
  3. Customizable workflows: Supports integration with external systems, enabling seamless handoffs and automated follow-ups.

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Use cases

The End function:

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1. Logs conversation metadata

  • Save key conversation details, such as duration, topic, or sentiment analysis, to a database or CRM.
  • Example use case: Track customer service interactions for reporting and performance analysis.

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2. Triggers workflows

  • Start processes like creating support tickets, sending confirmation emails, or updating account records.
  • Example use case: Automatically notify the sales team about potential leads from the conversation.

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3. Schedules follow-ups

  • Prepare reminders, SMS notifications, or callbacks for unresolved queries.
  • Example use case: Send a confirmation SMS after booking an appointment or a callback request.

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Implementation example

Below is a Python implementation of the End function: 
def end_function(conv: Conversation):
    # Log metadata
    metadata = {
        "conversation_id": conv.id,
        "duration": conv.duration,
        "topic": conv.state.get("last_topic", "Unknown"),
        "sentiment": conv.state.get("sentiment", "Neutral"),
    }
    log_to_crm(metadata)

    # Trigger external API
    if conv.state.get("support_needed"):
        create_support_ticket(conv.state.user_id, metadata)

    # Schedule follow-up if necessary
    if conv.state.get("follow_up_required"):
        schedule_follow_up(conv.state.user_id, conv.state.follow_up_time)

    return "End function executed successfully."

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Best practices for End function design

  1. Efficient execution:
    • Design the function to complete quickly to avoid slowing down post-conversation processes.
  2. Error handling:
    • Ensure errors during execution do not disrupt downstream workflows.
    • Implement fallback mechanisms for failed API calls or missing data.
  3. Data consistency:
    • Validate and sanitize data collected during the conversation before processing or logging it.
  4. Relevance:
    • Include only necessary post-conversation tasks to maintain efficiency and focus.

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Examples: Enhancing post-conversation workflows

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📊 Data logging for analytics

Capture details like customer sentiment, topics discussed, and the resolution status for reporting and analytics. Example:
  • “Logged: Customer expressed interest in our premium plan and showed positive sentiment.”

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📞 Automatic follow-ups

Send reminders, confirmation messages, or escalation notices to keep the customer informed. Example:
  • “An email has been sent confirming your booking for January 10th.”

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🛠 Task automation

Streamline processes by triggering external workflows or integrations. Example:
  • “Support ticket created: Issue with account login noted during the conversation.”

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🧾 CRM updates

Ensure customer records are up to date with the latest interaction details.