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.
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.
Why voice breaks agents that pass in text
Text agents already clear the bar. The same agent, on a real call, drops sharply — and the gap widens under real-world conditions.
Source: Sierra AI, τ-voice benchmark, 2026 · 278 grounded tasks across retail, airline, and telecom
Same logic, new failure modes
An agent with identical prompts and tools behaves differently on a call. Tone, interruptions, and accents surface breaks that text testing never catches.
Charming call, failed task
A voice agent can hold a polite, natural conversation while quietly failing the underlying task. Each turn sounds fine — the account never gets updated.
Every caller is an edge case
Non-standard accents, noisy environments, spotty connections. The callers most affected by regressions are the ones a quiet-room demo never represents.
Watch a voice agent get fully tested
From connecting the agent to the final executive report — the complete run in one video.
From connection to confidence in five steps
Connect your agent
Amazon Connect, WebRTC, or a plain phone number — no SDK or code access required.
Upload brief & KB
Drop in your agent brief and knowledge base so tests reflect what the agent should know.
Personas & test cases
Personas and use cases are generated from your brief, then test cases for each — with expected outputs and follow-ups.
Run & judge
Real calls are placed and scored by AI and deterministic judges against your pass threshold.
Review reports
Full call transcripts plus an executive summary and a developer deep-dive.
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?
Functional testing
Does it do the right thing, every time?
Two judges on every call
Subjective quality and hard proof — you get a confidence score backed by evidence, not a vibe.
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%)
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
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 or mobile.
SIP
Direct trunk into your telephony stack for enterprise contact-center testing at scale.
Works across audio-native models, ASR + LLM + TTS pipelines, and contact-center platforms — vendor-neutral by design.
Pre-deployment validation, not runtime tooling
Runtime infrastructure improves the call while it happens. ContextQA answers the question that comes before: is this agent ready to take real calls at all?
Improves the call, live
Runs while the call is happening, shaping the experience in real time.
- Voice isolation & noise suppression
- Turn-taking models
- Real-time call routing
Proves it's ready, before
Validates the agent the way callers will experience it, before it ever takes a real call.
- Produces evidence a release owner can sign off on
- Re-runs on every change, so fixes don't quietly regress elsewhere
- Vendor-neutral — works on top of any runtime layer
One layer makes live calls better; the other proves the agent is ready for them. Teams run both.
One run, two reports
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.
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.
Built for how enterprises deploy
Security and deployment flexibility for teams validating voice agents that touch real customer data.
SOC 2 Type II
Certified. Security and integration documentation available for review.
Runs in your environment
SaaS or fully self-hosted inside your own cloud account, under your IAM.
Isolation by default
Per-project data separation, built for teams validating multiple agents.
Voice agent testing, answered
What is AI voice agent testing?
Which voice platforms can I connect?
How are voice test cases created?
What does ContextQA check on each call?
How does scoring work?
Can it catch hallucinations on voice calls?
What reports do I get?
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.