---
title: Post-Conversation Processing Now Available for Telnyx Voice AI Assistants
canonical: https://telnyx.com/release-notes/post-conversation-processing-voice-ai-assistants
---
# Post-Conversation Processing Now Available for Telnyx Voice AI Assistants

[Release notes](https://telnyx.com/release-notes)

# Post-Conversation Processing Now Available for Voice AI Assistants

### 21, Apr 2026

Voice AI Assistants now support Post-Conversation Processing, enabling actions triggered after a voice call ends. Unlike Insights, which provide read-only analysis, Post-Conversation Processing lets your agent execute any action it could perform during a live call, with full conversation context and no real-time latency constraints.

### What's new

* **Post-call actions:** Configure your assistant to execute tool calls, API requests, and other actions after the conversation ends, using the full context of the call that just happened.
* **Full conversation context:** Post-conversation processing runs with the same LLM and data from the call, not a separate model working from metadata alone.
* **No latency ceiling:** Without real-time voice constraints, post-conversation steps can use reasoning models and run complex multi-step workflows that would be too slow during a live conversation.
* **Mission Control configuration:** Set up post-conversation processing directly in your assistant's instructions in the Mission Control Portal.

{
  "post_conversation_settings": {
    "enabled": true
  }
}

### Why it matters

* Voice AI agents that only talk and then stop are incomplete. The real work, updating a CRM, sending a summary email, filing a ticket, booking an appointment, happens after the caller hangs up. Until now, you had to build and maintain a separate pipeline to handle it.
* Insights give you analysis. Post-Conversation Processing gives you action. A summary tells you what happened; a triggered workflow does something about it.
* The same LLM that handled the call processes the post-call steps, so it carries full conversational context rather than working from a transcript summary. No information loss between the call and the follow-up.
* No other voice AI provider offers this. The standard pattern is a separate post-call service that works from metadata, not an integrated processing layer with full call context.

### Example use cases

* A healthcare scheduling agent that confirms the appointment in the EHR and sends the patient a confirmation text after the call.
* A sales qualification bot that creates a lead in the CRM, logs the conversation summary, and triggers a follow-up email sequence based on the prospect's stated interests.
* A support agent that files a ticket with the relevant details, sets priority based on the caller's sentiment, and pings the on-call engineer if the issue is critical.
* A collections agent that updates the account status, schedules a callback based on the promised payment date, and logs the payment arrangement.

### Getting started

1. In Mission Control, navigate to AI, then Assistants, and select your assistant.
2. In the assistant's instructions section, write the post-conversation processing steps you want your agent to perform after each call.
3. Specify which actions to trigger, including tool calls and API requests. Telephony-control tools (hangup, transfer, invite) are not available during post-conversation processing.
4. Test with a live call and verify that post-call actions fire correctly in your logs.

**Learn more** in the [Telnyx AI Assistants API reference](https://developers.telnyx.com/docs/inference/ai-assistants) or the [Voice AI Agents product page](https://telnyx.com/products/voice-ai-agents).

[Start building](https://telnyx.com/sign-up)

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