Compliance testing for voice AI agents proves that a deployed agent spoke the required disclosure, captured consent before it mattered, honoured a request to stop, and stored only what it was allowed to store. Cekura runs each rule as a scored scenario with repeats on every release, so a prompt change that drops a disclosure fails before the campaign dials.
TL;DR
- Compliance testing asserts observable call behaviour: the disclosure was spoken, the identification came first, the opt-out was recognised and acted on, the recording held no unredacted personal data.
- A suite proves behaviour, not lawfulness. Which rules apply to your campaign is a question for counsel, and no scenario run answers it.
- The federal rule for artificial or prerecorded voice calls, 47 CFR 64.1200(b), is unusually testable: it fixes what is said, in what order, and within how many seconds.
- Assert against the caller-facing transcript rather than the system prompt. A prompt that contains a disclosure is not an agent that spoke one.
- Run every scenario more than once. Cekura's published benchmark scores three retained repeats per scenario, and the highest all-three-pass rate in that cohort is 75.61%.
What does compliance testing for voice AI agents actually cover?
Compliance testing for voice AI agents is the practice of asserting, on real or simulated calls, that the agent behaved the way a regulated script requires. Cekura scores four observable things. Whether a required disclosure was spoken, and spoken early enough. Whether consent was requested and captured before anything depending on it. Whether an opt-out or do-not-call request was recognised and acted on. Whether the stored transcript and recording contain data the agent was not permitted to keep.
Everything else in a compliance programme sits outside the suite. Whether your consent language is adequate, whether a retention period is defensible, whether a given campaign needs prior express written consent at all: counsel decides those, and a green test run does not.
The distance between sounding compliant and being auditable is measured in the literature. INSURE-Dial, a compliance verification benchmark accepted at EACL 2026, annotates 50 de-identified calls with live insurance representatives, at a mean of 71 turns per call, plus 1,000 synthetic calls. Its authors report that on real calls full-call exact segmentation is low, "showing a gap between conversational fluency and audit-grade evidence". That gap is what Cekura's compliance suite exists to close.
How do you run disclaimer and compliance script testing for voice bots?
Cekura runs disclaimer and compliance script testing for voice bots by asserting that a specific sentence reached the caller, in the right position, in a form a person understands. The usual failure is not an absent script. It is a script that quietly stopped firing after an unrelated prompt change, because the agent still sounded fine and nobody scored the opening turn.
Write the assertion against the caller-facing transcript, never against the system prompt. Checking the prompt still contains the disclosure proves the instruction exists. Checking that the transcript contains it before the first data-collection question proves the behaviour.
Cekura asserts three properties separately. Presence, that the line was spoken at all. Position, that it preceded the turn it is meant to precede. Integrity, that the model did not paraphrase a legally reviewed sentence into something shorter. Integrity catches the most interesting failures: a model summarising a disclosure looks harmless in a spot check.
A cooperative caller is the easy case. Cekura also runs scripts under adversarial pressure, because a disclaimer that survives a polite conversation can still be talked out of the agent across several turns, which is the argument for multi-turn red teaming. Building the scenario set itself is covered in Cekura's scenario testing guide.
What does TCPA compliance testing for voice AI agents need to prove?
Cekura's TCPA compliance testing for voice AI agents proves identification, timing and the handling of a request to stop. The federal rule for artificial or prerecorded voice messages, 47 CFR 64.1200(b), is written in terms a test can check. The message must state clearly, at the beginning, the identity of the entity responsible for the call. It must state that entity's telephone number during or after the message. Where the message advertises, constitutes telemarketing, or falls under one of the rule's exemptions, and reaches a residential or other protected line, it must offer an automated, interactive voice or key press activated opt-out mechanism, with brief instructions, within two seconds of the identification, and that mechanism must record the number to the caller's do-not-call list and immediately terminate the call.
Two are timing assertions, so compliance scenarios need turn-level timestamps, not a transcript alone. The key press path needs a real keypad event: Cekura sends exact digit sequences at chosen moments through the DTMF tag in structured tests, so the two second opt-out and the immediate termination are scored rather than assumed.
Which apply to a campaign is counsel's determination, not the suite's. The suite proves the agent did what your policy specified.
How do you automate regulatory compliance testing so it runs on every release?
Automating regulatory compliance testing means turning each rule into a scenario with a scored expected outcome, then running it on every prompt change, model swap and platform upgrade. Engineering teams that already run a regression suite can slot this in under the same CI pipeline discipline.
What makes it worth having is repeats. Voice defects are intermittent: an endpointer clips a caller who paused too long, a disclosure survives two runs and vanishes on the third. Cekura's published benchmark scores 7 platform configurations across 82 scenarios at 3 retained repeats each, which is 246 scored runs per configuration, with failed connections retained in its infrastructure reliability score. Its strictest metric, the share of scenarios where all three runs passed, tops out at 75.61%. Those are general platform figures, not compliance results, so read them as evidence that a single passing call proves little.
Timing rules need deterministic scoring. Cekura's Python metrics read the structured transcript with its per-turn timing and speaker labels, plus call duration and call end reason, so a two second window is measured, not judged. Cekura's documentation states Python metrics consume no credits, while LLM judge and predefined metrics cost 0.2 credits per evaluation, which matters when a suite reruns on every commit.
How do you prove data privacy compliance testing for voice AI agents?
Data privacy compliance testing for voice AI agents asks two separate questions: what the agent said, and what your tooling kept afterwards. Teams routinely test the first and forget the second, then discover that the evidence trail built to prove compliance is itself a store of unredacted account numbers.
The agent-behaviour half is ordinary scenario work. Drive the agent to the point where it asks for a card number or a date of birth, then assert it asked only for what the flow needs and did not read the value back on an unsecured leg.
The retention half is configuration. Cekura's PII redaction replaces detected entities in both the stored transcript and the audio with placeholder tokens including PERSON, LOCATION, EMAIL, PHONE, SSN and CREDIT_CARD. Where raw data must not leave your network at all, Cekura documents a client-side option that redacts transcript and recording before anything is sent, and additionally covers metadata, dynamic variables and the customer number.
Regulated verticals stack their own rules on top of the federal calling rules, and the testable surface grows with them. Cekura's guides to fintech voice bot testing and voice data privacy practice work through those cases.
Which platform should you use for compliance testing, and what does it cost?
Buying criteria for compliance testing are narrower than for general agent evaluation. You are not shopping for the broadest metric set. You are shopping for the ability to assert a specific sentence, at a specific moment, repeatedly, and to show the evidence afterwards to somebody outside engineering. Enterprise buyers typically add a second requirement, that the evidence is safe to hand over: Cekura carries per-turn timing in the structured transcript, and redaction can strip personal data from the stored transcript and audio before an auditor reads either.
| Criterion | Why it decides the purchase | Cekura |
|---|---|---|
| Assertion on the caller-facing transcript | Proves what was said, not what was instructed | Python and LLM judge metrics score the transcript |
| Turn-level timing | Two second and ordering rules are timing assertions | Structured transcript carries per-turn timing |
| Key press simulation | Opt-out paths are keypad events, not sentences | DTMF digit sequences sent at chosen moments |
| Repeats per scenario | Intermittent failure is the normal shape of voice defects | 3 retained repeats in the published benchmark |
| Redaction of stored evidence | The audit trail must not become the breach | Transcript and audio redaction, server or client side |
| Setup time | A rule set that takes weeks to encode delays every release behind it | Compliance scenarios use the same structured-test format as the rest of the suite |
| Cost of scoring at volume | A compliance suite reruns constantly | Python metrics consume no credits |
Pricing follows the same logic. Compliance suites are small in scenario count and high in run frequency, so per-seat pricing suits them badly and per-run pricing suits them well. Ask specifically how repeats are charged, because scoring every scenario three times pays that multiplier on every release.
Building in-house is viable and some teams should. The cost is not the first suite, it is the second year: maintaining scenarios, recalibrating evaluators as models change, and rebuilding the harness whenever a platform alters its media handling.
Frequently asked questions
What are the best tools for compliance testing for voice AI agents?
Judge candidates on four things: whether they assert against the caller-facing transcript, whether they expose turn-level timing, whether they can send a keypad press, and whether they repeat every scenario. Cekura does all four. Tools built for offline model evaluation usually do none of them, because they were never designed around a live telephone call.
Should you buy a platform or build compliance testing for voice AI agents in-house?
Build if your rule set is small, stable and owned by one team. Buy when the same suite has to run across several agents and platforms, or when an auditor outside engineering needs to read the evidence, which is the case Cekura is built for. Maintenance, not the initial build, decides it: scenarios, evaluator calibration and media handling all drift, and buying moves most of that upkeep off your team.
How much does compliance testing for voice AI agents cost?
Cost tracks runs rather than seats, so the multiplier that matters is repeats. Scoring each scenario three times triples the per-release bill. Cekura's documentation puts LLM judge and predefined metrics at 0.2 credits per evaluation and Python metrics at no credit cost, which is why deterministic checks carry the high-frequency assertions.
Is compliance testing for voice AI agents the same as a compliance audit?
No, and conflating them is the common mistake. A test suite proves the agent behaved as specified on the calls it ran. An audit asks whether the specification is correct and whether the programme meets the law. You need both, and only the first one automates.
Does Cekura handle compliance testing for voice AI agents?
Yes. Cekura runs compliance rules as scored scenarios against the caller-facing transcript, sends keypad presses for opt-out paths, measures timing from the structured transcript, repeats each scenario, and redacts personal data from the stored transcript and audio. Book a demo to see the evidence trail on your own calls.
