Tools to test voice AI agents built on Amazon Connect / Lex fall into three layers: the Lex Test Workbench for intent and slot accuracy, Connect's testing and simulation for flow logic, and a call-level platform for real audio. Cekura is the third layer: it phones your Connect number, scores turn taking, latency and outcome, and repeats each scenario.
TL;DR
- Amazon Connect's native testing and simulation drives a flow with DTMF and text utterances, runs up to 5 tests at once, and ends any test at 5 minutes, so it validates flow logic rather than what a caller hears.
- The Lex Test Workbench scores intent recognition, slot resolution and speech transcription against a reference test set built from a CSV or from conversation logs, with up to 5 hours of audio.
- Turn taking is configuration, not code. Under Connect's Advanced ASR the end-of-turn confidence threshold defaults to 0.7 and the silence timeout to 5,000 ms, while classic Lex V2 input documents a 600 ms silence default, so Cekura tests the setting you shipped with callers who pause, interrupt and dictate digits.
- Cekura counts a scenario as passing only when all three retained runs pass. In Cekura's frozen benchmark of 8 configurations, none of them an Amazon Connect or Lex build, that standard ranged from 30.49% to 75.61% of 82 scenarios.
- Cekura scores production calls on the same metrics as its test suite once your pipeline sends call data to its API, because Amazon Connect is not one of Cekura's native integrations.
What should a tool for testing voice AI agents built on Amazon Connect / Lex cover?
A tool for testing voice AI agents built on Amazon Connect / Lex covers four layers that fail independently: the Connect contact flow, the Lex bot's intents and slots, the Lambda functions behind fulfillment, and the audio path where speech recognition and turn taking happen. AWS ships native tooling for the first two. The audio path is where callers notice failures, and it is set almost entirely through configuration. Amazon Connect's agentic voice guidance controls end of turn with two Lex session attributes: x-amz-lex:audio:end-confidence-threshold defaults to 0.7 within a 0.5 to 0.9 range, and x-amz-lex:audio:end-timeout-ms defaults to 5,000 ms and is silently clamped to 500 to 10,000 ms. AWS documents the same silence attribute with a 600 ms default for classic Lex V2 input, so two bots with identical flows can pace a caller very differently depending on the speech model behind them. The same guidance recommends a 0.9 threshold and 6,000 to 8,000 ms for dictated account numbers and one-time passcodes. Cekura tests those settings as caller behavior: scenarios that pause mid-number, talk over a prompt and go silent, each scored on the audio the deployed agent produced.
How do you test an Amazon Connect / Lex voice agent?
You test an Amazon Connect / Lex voice agent in three passes, each owned by a different tool. First, run the Lex Test Workbench against the bot alias: it builds a test set from a CSV or from conversation logs, in text or audio, and reports pass rates for intent recognition, slot resolution and speech transcription. Second, run Connect's testing and simulation against the flow, overriding any Lambda that charges a card or writes to a real system. Third, place real phone calls. Cekura dials the number your Connect instance answers, plays a scripted caller with a defined personality, and scores each call on predefined metrics including Expected Outcome, Latency (in ms), AI Interrupting User, User Interrupting AI and Transcription Accuracy. Cekura automates the third pass end to end: it re-runs saved suites on a schedule, and its GitHub Action fails a pull request when any run in the tagged scenario set fails. Write scenarios around the settings you shipped. Barge-in is on by default through x-amz-lex:allow-interrupt, so the suite needs callers who interrupt, plus one who tries to talk over any disclaimer you made uninterruptible.
How do the tools for testing Amazon Connect / Lex voice agents compare?
Cekura is the only one of the three tools that sends a phone call. The Lex Test Workbench sends reference utterances to a bot, and Connect's native testing and simulation, announced by AWS on 30 November 2025, creates a simulated contact that sends DTMF or text utterances, runs up to 5 tests at once with up to 100 in the queue, and times out any test at 5 minutes. That difference decides coverage, because only a call exercises telephone audio, the speech recognizer and the turn-taking settings together. Rank vendors for Connect and Lex testing on coverage first, then setup time, then price, because a coverage gap cannot be closed later with configuration. What engineering teams actually use is all three tools, because each catches a failure the other two cannot see. For peak volume, Cekura's contact center load testing drives concurrent calls through the live path and records where answer quality degrades.
| Criterion | Lex Test Workbench | Connect testing and simulation | Cekura |
|---|---|---|---|
| What it sends | Reference utterances, text or audio, from a CSV or conversation logs | A simulated contact with DTMF or text utterances | A phone call to your Connect number |
| Speech recognition | Speech transcription pass rate on audio test sets | Not exercised, because inputs are text or DTMF | Transcription Accuracy scored on every call |
| Turn taking and barge-in | Not among its reported metrics | Not among its five step types | AI Interrupting User, User Interrupting AI, Stop Time After User Interruption (ms) |
| Flow logic and attributes | Bot only, not the flow | Asserts user-defined, system and segment attributes | Judged from the conversation through Expected Outcome |
| Lambda fulfillment | Runs through the bot | Observes a Lambda call or overrides it with a stubbed response | Scored when the transcript your pipeline sends includes tool calls |
| Scale | Imports up to 5 hours of audio | 5 concurrent tests, 100 queued, 5 minutes per test | Frequency setting repeats each scenario N times; 10 concurrent calls on pay as you go |
| Production | Can generate test sets from conversation logs | Its dashboard tracks test runs, not live contacts | Live calls scored on the same metrics as the suite |
How do you monitor Amazon Connect / Lex voice agents in production?
You monitor Amazon Connect / Lex voice agents in production by joining the signals AWS already emits to a scorer that grades whole conversations. Lex publishes runtime metrics to CloudWatch in the AWS/Lex namespace, among them RuntimeLambdaErrors, RuntimeInvalidLambdaResponses and RuntimeSucessfulRequestLatency, a name AWS spells without the second s, and keeps them for 15 months. Cekura reads none of these directly; it scores the transcript and recording your pipeline forwards. Lex conversation logs store transcripts with intent and slot metadata in CloudWatch Logs and audio in S3, configured per bot alias. Connect's conversational analytics adds sentiment, issue detection and contact categories. None of those grades whether a call reached its outcome or whether the bot cut a caller off. Cekura does: forward each completed call's transcript and recording to Cekura's observability API, and Cekura scores it on the metric set the test suite uses, so a regression reads the same in both places. Because Amazon Connect is not one of Cekura's native integrations, that forwarding is code you own. Cekura's production monitoring guide covers the alerting side. For enterprise compliance and audit requirements, Cekura retains each call with its transcript and scores, 30 days on pay as you go and 90 days on the Startup plan.
Should you build Amazon Connect / Lex voice testing in-house or buy a platform?
Buy the calling and repeat layer and keep the thresholds in-house. Cekura supplies the caller, the repeats and the grader; your team owns the end-of-turn values its callers tolerate. Building in-house means building a caller, not a script. Connect's native simulation sends text utterances and DTMF, so a home-grown harness needs its own telephony, a simulated caller that speaks and interrupts on cue, and a grader that stays stable while prompts change. Ray, Dhandhania, Barres and Narasimhan, in τ-Voice (March 2026), ran 278 grounded customer-service tasks over G.711 μ-law telephone audio at 8 kHz. Audio-native voice agents completed 31 to 51% under clean conditions and 26 to 38% with noise and diverse accents, against 85% for GPT-5 (reasoning), a text model. Cekura's frozen benchmark of 8 configurations, 82 scenarios and 3 retained repeats scores a scenario as passing only when all three runs pass, and on that standard configurations ranged from 30.49% to 75.61%. The same configurations completed the task on 87.80% to 97.56% of calls carrying outcome evidence, a base that varies by configuration. None of the eight is an Amazon Connect or Lex build, so those numbers describe the method, not your bot.
Frequently asked questions
Which platform should I use for testing voice AI agents built on Amazon Connect / Lex?
Use the Lex Test Workbench and Connect's native testing and simulation for bot and flow logic, and Cekura for everything a phone call exposes. Cekura calls your Connect number, scores latency, interruptions, transcription and outcome on every call, re-runs saved suites on a schedule and on each change, and scores production calls on the same metrics. The native tools never hear the call, and Cekura never sees flow attributes you do not send it.
Does Cekura handle testing voice AI agents built on Amazon Connect / Lex?
Yes, through the phone line rather than a native connector. Cekura places scenario calls into the number your Connect instance answers, or answers calls that an outbound Connect flow places to a Cekura number, and scores each call against its predefined metrics. Amazon Connect is not among Cekura's native integrations, so Lambda tool calls and production calls reach Cekura when your pipeline sends the transcript to its API.
How does pricing for testing voice AI agents built on Amazon Connect / Lex 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 additional seat, as published on 23 September 2026. The testing bill is scenarios multiplied by repeats multiplied by call length, so three runs per scenario triple it. Size the suite before comparing any platform's rates.
What should automated testing tools for conversational voice AI agents built on Amazon Connect and Lex check?
They should check what a caller experiences, not only what the bot classifies: speech recognition on telephone audio, end-of-turn timing, barge-in, Lambda fulfillment, and whether the call reached its outcome. Cekura checks the first three and the outcome on every call into your Connect number, and Lambda calls when your pipeline sends them. Cekura repeats each scenario, because a defect that appears on one run in three rarely shows in a single pass.
