Voice Agent TestingNew

AI voice agent testing that hears what your callers hear

Most teams ship voice AI agents and hope for the best. ContextQA validates accents, interruptions, turn-taking, latency, hallucinations, task completion, and knowledge-base accuracy — all in one run, before go-live.

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The basics

What is AI voice agent testing?

AI voice agent testing validates a voice agent the way real callers experience it — on real calls, with realistic personas. ContextQA checks accents, interruptions, turn-taking, latency, hallucinations, task completion, and knowledge-base accuracy, scoring every call with AI and deterministic judges before your agent goes live.

Full demo

Watch a voice agent get fully tested

From connecting the agent to the final executive report — the complete run in one video.

▶︎ Watch the full demo · 6 min
How it works

From connection to confidence in five steps

01

Connect your agent

Amazon Connect, WebRTC, or a plain phone number — no SDK or code access required.

02

Upload brief & KB

Drop in your agent brief and knowledge base so tests reflect what the agent should know.

03

Personas & test cases

Personas and use cases are generated from your brief, then test cases for each — with expected outputs and follow-ups.

04

Run & judge

Real calls are placed and scored by AI and deterministic judges against your pass threshold.

05

Review reports

Full call transcripts plus an executive summary and a developer deep-dive.

Coverage

Everything a real call can get wrong

Voice quality and functional behavior, validated in the same run.

Voice-specific testing

Does it sound right, to every caller?

Audio quality Accents Tone Language matching Response clarity Response audibility Interruptions Turn-taking Latency

Functional testing

Does it do the right thing, every time?

Intent recognition Entity extraction Task completion Hallucination detection Knowledge-base accuracy Multi-turn flows
Scoring

Two judges on every call

Subjective quality and hard proof — you get a confidence score backed by evidence, not a vibe.

LLM-based judges

For how the call feels

AI judges score the qualities only a listener can assess, against criteria you configure.

  • Intent, entities, task completion & hallucination
  • Audio checks: quality, language matching, tone & clarity
  • Configurable criteria with pass thresholds (e.g., 80%)
Deterministic judges

For what the call proves

Hard checks that pass or fail — no judgment calls, just verifiable facts.

  • Phone numbers in exactly the right format
  • Entities extracted and tasks actually completed
  • Order IDs and emails validated exactly
Connect in minutes

If callers can reach it, we can test it

Amazon Connect

Point ContextQA at your Connect instance and start placing test calls.

WebRTC

Test browser-based voice agents over a direct WebRTC connection.

Phone number

Dial the agent like a real customer — landline, mobile, or SIP.

Dual reporting

One run, two reports

For stakeholders

Executive report

Which test cases passed, which failed, whether the agent is ready for launch, and the top failure modes — with actionable insights instead of raw metrics.

For builders

Developer report

Every test case with expected vs. actual outcome, score, and the reasoning behind each result — plus full call transcripts you can replay. Pair it with root-cause analysis to fix issues fast.

Testing chat and tool-calling agents too? See AI agent testing for the full picture.

FAQ

Voice agent testing, answered

What is AI voice agent testing?

AI voice agent testing validates a voice AI agent the way real callers experience it — checking accents, interruptions, turn-taking, latency, hallucinations, task completion, and knowledge-base accuracy on real calls, before the agent goes live.

Which voice platforms can I connect?

ContextQA connects to your voice agent over Amazon Connect, WebRTC, or a plain phone number — no SDK or code access required. If callers can reach it, ContextQA can test it.

How are voice test cases created?

Upload your agent brief and knowledge base, and ContextQA generates realistic user personas (linkable to real or mock accounts), then use cases — the buckets test cases fit into — and finally individual test cases with expected outputs, follow-ups, and outcomes.

What does ContextQA check on each call?

Voice-specific quality (audio quality, tone, language matching, response clarity, accents, interruptions, turn-taking, latency) and functional behavior (intent recognition, entity extraction, task completion, hallucination, knowledge-base accuracy) — all in one run.

How does scoring work?

Every call is scored by two kinds of judges: LLM judges for intent, entities, task completion, hallucination, and audio checks (quality, language matching, tone, clarity), and deterministic judges that validate exact formats like phone numbers, order IDs, and emails. You set the pass threshold — for example, 80%.

Can it catch hallucinations on voice calls?

Yes. ContextQA validates the agent's spoken answers against your knowledge base and known-correct facts, flagging fabricated pricing, policies, or details before a real customer ever hears them.

What reports do I get?

Two views from every run: an executive report with scores, trends, and risk areas for stakeholders, and a developer report with full call transcripts, per-turn judgments, and latency traces for fixing issues fast.

Ship voice agents with confidence

Whether it's an insurance support agent, a customer-service bot, or any platform-built voice AI — ContextQA validates it before real users do.

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