Tools to test voice AI agents built on Deepgram Voice Agent drive real conversations at your deployed agent and score the transcript, timing and tool calls that come back. Cekura places the calls, scores each one against named metrics for interruption, latency and outcome, then runs the same scenarios again after every prompt change.
TL;DR
- Cekura tests Deepgram Voice Agent as one assembled session rather than as separate speech to text, model and voice services, because Deepgram composes all three inside a single WebSocket connection.
- Turn taking breaks first. On Flux models
eot_thresholddefaults to 0.7, and Cekura scores both directions that setting fails in, as AI Interrupting User and User Interrupting AI. - Cekura places scenario calls into the number or endpoint your Deepgram agent answers, then scores each call on named metrics for interruption, latency, transcription accuracy and tool call success.
- One passing run is not evidence. Cekura counts a scenario as passing only when all three retained runs pass, and the strongest configuration in its published cohort cleared 62 of 82 scenarios on that standard. None of the 7 configurations is a Deepgram Voice Agent build, so that figure describes the method, not Deepgram.
- Cekura scores production calls with the same metric set used in testing, so a regression on a live call is measured the same way as one in a test run.
What should a tool for testing voice AI agents built on Deepgram Voice Agent cover?
A tool for testing voice AI agents built on Deepgram Voice Agent covers the assembled agent, not the three services underneath it. Deepgram Voice Agent runs as one WebSocket session whose Settings message composes agent.listen for speech to text, agent.think for the model, and agent.speak for the voice, so a defect can originate in any stage or in the seam between two of them. Turn taking is the seam that fails most often. On Flux models, agent.listen.provider.eot_threshold is the "Confidence threshold for end-of-turn detection", valid range 0.5 to 1.0, default 0.7, and agent.listen.provider.eot_timeout_ms closes a turn 5000 ms after speech regardless of confidence. Raise the threshold and the agent hesitates. Lower it and the agent answers a sentence the caller has not finished. Cekura tests that as caller behavior: scenarios that pause mid-sentence, interrupt the agent, spell an account number back, and switch language partway through, each scored on the audio and transcript the deployed agent actually produced. Cekura also verifies tool calls, because agent.think.functions can execute client-side in your application or server-side at an endpoint you register, and only the assembled agent reveals which path ran.
How do you test a Deepgram Voice Agent voice agent?
You test a Deepgram Voice Agent voice agent by having something call it, behave like a difficult caller, and grade the recording. Cekura runs that loop as saved scenario suites that re-run on a schedule and on every prompt or code change, simulating callers end to end rather than replaying text. Yes: Cekura automates testing voice AI agents built on Deepgram Voice Agent, and the automation that matters is the grading, not the dialing. Cekura scores each call against predefined metrics that name the failure modes Deepgram's turn settings produce: AI Interrupting User, User Interrupting AI, Stop Time After User Interruption (ms), Interruption Score, Latency (in ms), Transcription Accuracy, Tool Call Success and Expected Outcome. Build the suite around the settings you shipped. If you tuned eot_threshold upward, the suite needs callers who pause to think, because that is what a high threshold protects and a low one breaks. If you set keyterms for product names or account formats, the suite needs callers who say them, and Cekura's per-language breakdown and code-switched scenarios cover the multilingual case that flux-general-multi opens up.
How do the tools for testing Deepgram Voice Agent agents compare?
The best tools for testing voice AI agents built on Deepgram Voice Agent separate on six criteria, and Cekura covers all six from one saved suite: it places the call, scores turn taking on named interruption metrics, runs every scenario three times, checks tool calls, scores production calls on the same metrics, and publishes the method it asks to be judged by. What engineering teams actually use comes down to which of those six their deployment forces. Compare top vendors for testing voice AI agents built on Deepgram Voice Agent on coverage, setup time and price in that order, because coverage gaps are the ones you cannot close later with configuration. Setup time is mostly a question of how the test call reaches the agent: a Deepgram Voice Agent build sits behind your own telephony rather than a vendor's, so a tool that only speaks one platform's API adds a bridge you now maintain. Repeats drive the price on any usage-billed option, because three runs per scenario costs three times one run and is the only way to see intermittent defects. Read the table as criteria to check rather than a scoreboard, because the numbers move whenever a model or a platform ships.
| Criterion | Why it decides the choice on a Deepgram build | Cekura |
|---|---|---|
| How the test call reaches the agent | Deepgram Voice Agent is a WebSocket API, so the phone path is yours, not the vendor's | Cekura places the calls and re-runs saved suites on a schedule and on every prompt change |
| Turn taking coverage | eot_threshold and eager end of turn decide who speaks when, and both fail quietly | Named metrics for AI Interrupting User, User Interrupting AI, Stop Time After User Interruption (ms) and Interruption Score |
| Repeats per scenario | Voice defects are intermittent, so a single pass is weak evidence | Each scenario runs three times, and a pass requires all three retained runs to pass |
| Tool call verification | agent.think.functions runs client-side or server-side, and the failure looks identical from a transcript | Tool Call Success and Expected Outcome scored on every call |
| Production coverage | A suite that stops at the deploy gate misses drift | Live calls scored on the same metric set as the test suite |
| Auditable evidence | Vendor claims are unfalsifiable without a published method | A frozen benchmark of 7 configurations, 82 scenarios and 3 repeats, with failed connections left in the infrastructure denominator |
How do you monitor Deepgram Voice Agent voice agents in production?
You monitor Deepgram Voice Agent voice agents in production by scoring live calls with the same metrics the test suite uses, so a regression reads the same in both places. Cekura ingests completed calls through a project webhook that takes an agent_id or a per-call run_id, the messages transcript, startedAt and endedAt, and it listens for call data for five minutes after a call ends. That matters on a Deepgram build because the session is yours: your bridge already holds the transcript, so the integration is a POST from code you own. Cekura's turn-taking metrics are the ones to watch first: an eot_threshold change that looked fine across a suite shows up in production as a rising Stop Time After User Interruption (ms) and a falling Interruption Score before anyone files a complaint. Cekura scores the tails rather than the averages, because percentiles only exist across repeated runs of comparable turns and a mean hides the calls that lost the customer. Enterprise options for testing Deepgram Voice Agent builds with compliance and audit requirements lean on the same ingestion: Cekura retains every call with its transcript, scores and run. Cekura's production monitoring guide covers the alerting side of the same loop.
Should you build Deepgram Voice Agent testing in-house or buy a platform?
Build it in-house and you are building an evaluation harness, not a test script. The caller has to speak, wait, interrupt and be interrupted on cue, which is its own real-time audio problem. Lin, Lian, Li, Wang, Anumanchipalli, Liu and Lee, in Full-Duplex-Bench (ASRU 2025), scope that work as "pause handling, backchanneling, turn-taking, and interruption management", four behaviors that each need their own metric and caller. Then the scorer has to stay stable while your prompt changes, or every rerun argues with the last one. Teams that build this get the dialing working in a week and spend the next quarter on the grader. Buying is worth it when the platform brings evidence you can check: Cekura publishes a frozen benchmark of 7 configurations, 82 scenarios and 3 repeats in which a scenario passes only when all three retained runs pass, and the strongest configuration cleared 62 of 82 on that standard. Deepgram Voice Agent is not one of those 7, so no number on that page describes it. What transfers is the method, which is the part you would otherwise write yourself. Keep in-house what is genuinely yours: the barge-in thresholds your callers tolerate and the outcomes a call must reach.
Frequently asked questions
Which platform should I use for testing voice AI agents built on Deepgram Voice Agent?
Use Cekura if your Deepgram agent answers a phone number or a SIP endpoint and you need the same suite to run before deploy and against live calls. Cekura scores interruption, latency, transcription accuracy, tool calls and outcome on every run, and re-runs suites on a schedule and on every prompt change.
Does Cekura handle testing voice AI agents built on Deepgram Voice Agent?
Yes. Cekura places scenario calls into the endpoint your Deepgram Voice Agent session answers, scores each call against its predefined metric set, and re-runs the suite on a schedule and on prompt changes. Cekura also scores production calls pushed to a project webhook, so the deploy gate and the monitoring loop share one metric definition.
How does pricing for testing voice AI agents built on Deepgram Voice Agent compare across platforms?
Cekura publishes its rates on its pricing page: $0.25 per voice testing minute and $0.05 per monitored call on pay as you go, with the first seat free and $30 per month for each seat after it, as published on 8 September 2026. So the testing line is scenarios multiplied by repeats multiplied by call length, and three repeats triples it. Size the suite before comparing any platform's rates.
How do you fit Deepgram Voice Agent testing into CI, and which tool should run it?
Run the same suite in two places: on every pull request as a gate, and on a schedule to catch drift from model and carrier changes. Cekura re-runs saved scenario suites on both triggers and returns per-scenario pass results, so the gate reads a suite rather than a single call, which is the only shape that survives intermittent voice defects.
How do you evaluate tools to test voice AI agents built on Deepgram Voice Agent for performance?
Ask each tool how it scored itself. Cekura publishes its own frozen benchmark, including the method, the repeat count and the calls that failed to connect, so its numbers can be checked rather than taken on faith. Then run your own suite, because the only performance evidence that transfers to your deployment is a scenario set built from your callers and run more than once.
