---
title: Voice AI Testing and Observability for Telnyx AI Assistants with Cekura
canonical: https://telnyx.com/release-notes/voice-ai-testing-observability-cekura
---
# Voice AI Testing and Observability for Telnyx AI Assistants with Cekura

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

# Voice AI Testing and Observability Now Available with Cekura for Telnyx Voice AI

### 15, Jul 2026

Voice AI testing and observability is now available for Telnyx AI Assistants, powered by Cekura. Teams can now run pre-production simulations across diverse caller personas, define custom evaluation metrics, and monitor production conversations to test instruction-following, tool calls, and conversational quality, all from inside Mission Control.

### What's new

* **Test Agents:** Create caller personas with defined personalities, languages, and instructions. Each test agent is assigned a real SIP endpoint and phone number, and calls your AI Assistant like a real customer, allowing you to validate behavior before production deployment.
* **Evaluators:** Define test scenarios with expected outcomes, first messages, extra instructions, max duration, and language. Run evaluators on demand or schedule them as cron jobs for continuous regression testing.
* **Custom Metrics:** Create metrics to evaluate assistant quality across three eval types: Binary Workflow Adherence (true/false, failure means the run failed), Binary Qualitative (true/false, good to have), and Continuous Qualitative (graded on a scale of 5). Metrics support simulation, observability, and sampling modes.
* **Generate and Improve Evaluators:** Auto-generate evaluator scenarios from your assistant configuration, and batch-improve existing evaluators with natural language instructions (for example, "add DTMF tool to all evaluators").
* **Scheduled Test Runs:** Create cron jobs to run evaluators on a schedule, with personality overrides and timezone support, so regression testing happens without manual triggers.
* **Results and Call Playback:** View test results with call detail cards, play back audio from test calls, and inspect transcripts to diagnose where the assistant succeeded or failed.
* **Export Evaluators:** Export evaluator configurations as CSV or Excel for sharing, version control, or external review.

### Why it matters

* Voice AI teams ship agents to production without a reliable way to test them first. Cekura closes that gap: simulate calls, define what success looks like, and catch regressions before customers do.
* Testing happens inside the same Mission Control UI where you build and deploy the assistant. No separate tool, no separate login. The Cekura integration is a proxied API layer inside the Telnyx AI Assistants service, so a Cekura project is created on demand the first time your organization uses the test framework.
* Evaluators run on schedule, not just on demand. Cron-based regression testing means every assistant change gets validated against your test suite before it touches live traffic.
* Custom metrics let you define quality on your terms. Whether it is "did the agent greet the caller" or "did the agent follow the tool-call workflow correctly," the eval framework adapts to your definition of a good call.

### Example use cases

* Pre-production validation: simulate 20 caller personas across 5 languages calling your support agent before deploying changes to production.
* Regression testing: schedule a nightly cron job that runs your evaluator suite against the latest assistant config to catch instruction-following drift.
* Quality monitoring: enable observability metrics on production calls to track tool-call accuracy, greeting adherence, and conversation quality over time.
* Team review: export evaluator results as Excel and share with product or QA leads who do not have Mission Control access.

### Getting started

1. In Mission Control, go to **AI > AI Tests > Test Agents**.
2. Click **Create New Test Agent** and configure a caller persona: name, language, personality, and phone number. Your assistant must have a phone number assigned first.
3. Navigate to **AI > AI Tests > Evaluators** and click **Create Evaluator** to define a test scenario with instructions, expected outcome, and assigned test agent.
4. Go to **AI > AI Tests > Metrics** and click **Create Metric** to define how the evaluator grades call quality (Binary Workflow, Binary Qualitative, or Continuous Qualitative).
5. Run evaluators on demand, or create a cron job under **AI > AI Tests > Scheduled Jobs** to run regression tests on a schedule.
6. Review results under **AI > AI Tests > Results** with audio playback and transcript inspection.

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

### Ask AI

* [![GPT](https://d2efw2akbx8gaf.cloudfront.net/https://dotcom-payload-assets-prod.s3.us-east-1.amazonaws.com/cms-media/chatgpt_icon-ea55e09d.jpeg?width=92&format=webp)](https://chat.openai.com/?q=What makes Telnyx's global network different, breakdown call quality for normal phone calls and voice AI calls.)
* [![Claude](https://d2efw2akbx8gaf.cloudfront.net/https://dotcom-payload-assets-prod.s3.us-east-1.amazonaws.com/cms-media/claude_icon-c7c8eef3.webp?width=92&format=webp)](https://claude.ai/new?q=What makes Telnyx's global network different, breakdown call quality for normal phone calls and voice AI calls.)
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* [![Grok](https://d2efw2akbx8gaf.cloudfront.net/https://dotcom-payload-assets-prod.s3.us-east-1.amazonaws.com/cms-media/grok_icon-e588e5bd.webp?width=92&format=webp)](https://x.com/i/grok?text=What makes Telnyx's global network different, breakdown call quality for normal phone calls and voice AI calls.)