Your voice agent passed every test you wrote, then a real caller interrupted mid-sentence, and it booked the wrong appointment. Voice AI evals exist to catch those issues before your customers do.
This guide covers the methods that work, the platforms worth using, and the practices that separate a real eval from a dashboard.
What Are Voice AI Evals?
Voice AI evals are the tests and scores that measure whether a voice agent hears, understands, and acts on what callers actually say.
Two things make up a real eval. You simulate a conversation by running the agent against a caller scenario. Then you evaluate the result by scoring it against clear criteria.
Logging is neither of those. A trace tells you what happened on one call, but it doesn't tell you whether the agent holds up across a thousand of them.
Voice adds failure modes that text evals never see. Background noise corrupts the transcript, a 250ms pause gets read as an interruption, latency climbs, and the caller hangs up before the agent finishes.
Evals run in two places. Offline evals run before launch, on synthetic calls you control. Online evals run after launch, on live traffic you score continuously.
5 Voice AI Eval Methods (and When Each One Fits)
A breakdown of the five voice AI eval methods across offline simulation, live-call scoring, deterministic checks, model-graded judging, and human review, with where each one breaks.
Method 1: Pre-Production Scenario Simulation
What it is: Running synthetic calls against your agent before it reaches a real caller, using scripted personas and multi-turn scenarios.
How it works: You define the agent endpoint, the scenario (rescheduling an appointment), and the persona (an impatient caller with a strong accent). Then you run hundreds of variations in parallel and score each one.
Real example: Daily.co's aiwf-eval benchmark simulates a 30-turn ordering call with tool calls and knowledge grounding. Six months ago, no public model cleared 95% across the full conversation. That difference only showed up because the eval ran the whole call, and a single-turn test would have missed it.
Method 2: Production Call Scoring
What it is: Scoring live calls continuously after launch to catch degradation and regressions that appear only at scale.
How it works: Every live call gets scored against the same criteria as your pre-launch suite. Failed calls feed back into that suite before the next release, and drift shows up as a score trend instead of a customer complaint.
Real example: A team running thousands of calls a day can manually review around 1% of them. Production scoring covers the other 99% and flags the failed calls a human reviewer would never reach.
Method 3: Deterministic Evals
What it is: Rule-based assertions that check exactly what the agent did, with no model judgment involved.
How it works: You assert facts. Did the agent call the booking API? Did it confirm the date back to the caller? Did it stay under the latency budget? Each assertion returns a clean pass or fail.
Real example: A refund flow can produce a friendly, natural-sounding reply and still skip the actual refund API call. A deterministic check on the tool call catches that miss when a model-graded score would wave it through.
Method 4: LLM-as-a-Judge Evals
What it is: Using a language model to score subjective qualities like naturalness, empathy, and whether a response addressed the caller's intent.
How it works: You write a rubric, give the judge examples of good and bad responses, and let it score at scale. It handles the open-ended judgments that deterministic checks can't reach. Here is how LLM-as-a-judge scoring works in practice.
The limit: A judge is strong on turn-local errors and weak on cross-turn state. A June 2026 study of a deployed ordering agent found its built-in LLM judge caught 2 of 9 systematic problem patterns, around 22%.
In one batch, its gate flagged zero of 100 rounds, where human review confirmed 23 defects. Treat the judge as a regression floor. It is cheap, always on, and good at catching known failures when they recur.
Method 5: Human-in-the-Loop Review
What it is: Expert review of full transcripts to catch the nuanced, cross-turn failures that automation skips.
How it works: A reviewer reads the whole arc instead of isolated turns. They catch the confirm-gate lockout, the cart that hallucinated an item three turns back, and the escalation that never fired.
Real example: In that same 2026 study, an exhaustive human transcript review surfaced every cross-turn failure the automated judge missed. Humans can't scale to every call, so point them at the calls your automated evals already flagged.
What to Measure in Voice AI Evals
An eval is only as useful as the metrics behind it. These catch the most production failures, and the latency reality constrains all of them.
- Word error rate under noise: Clean-audio WER tells you little. Real callers phone in from cars and mobile networks, so measure WER under the conditions they use.
- Latency at P95 and P99: Averages hide the tail. An agent averaging 380ms can still hand 5% of callers a two-second wait, and that 5% is who churns.
- Task completion rate: The metric that maps to the business. Did the caller get the appointment, the refund, or the answer they called for?
- Instruction following: Whether the agent obeys its system prompt across the whole call, including turn twenty.
- Hallucination rate: How often the agent invents a fact that isn't in its knowledge base.
- Interruption handling: Whether the agent yields when the caller barges in or talks over them.
Latency sets a hard ceiling on accuracy. The same aiwf-eval benchmark found that the models scoring 100% on a 30-turn conversation are too slow for live voice.
Natural conversation needs voice-to-voice response under 1,500ms, so the most accurate model often loses to a faster one that callers will actually wait for. For the full metric set and how to score each one, see our guide to voice AI evaluation metrics.
How to Do Voice AI Evals (Step-by-Step)
Running voice AI evals for the first time is easy to overcomplicate. Here is the process that works.
Step 1: Build a scenario library covering the 30-50 most common user intents. Pull these from real call transcripts. What do callers actually ask? Bookings, rescheduling, refunds, escalations, off-script mid-sentence corrections. Synthetic inputs miss the failures that real calls bring up.
Step 2: Run voice evals across each scenario on every release. Treat the library as a regression suite. Every prompt edit, model swap, or routing change runs the full set before anything ships. A change that improves one scenario can silently break another.
Step 3: Score each call with an LLM judge against your rubric. Your rubric defines what a good call looks like: task completion, instruction following, latency, interruption handling. The judge scores at scale but will miss cross-turn state failures, so back it up with deterministic assertions on any binary pass/fail tool calls.
Step 4: Compare scores against your baseline run. A score in isolation means nothing. A score that dropped three points from the last release means something broke. Watch P95 and P99 latency too, since averages tend to hide the tail.
Step 5: Push fixes back into prompts, tools, and routing. When a scenario fails, trace it to the root, whether that's an ambiguous prompt, a wrong routing rule, or a malformed tool output. Fix the source, rerun the scenario, confirm the score recovers, then ship.
Voice AI Eval Platforms Compared
Five platforms come up most often when teams shop for voice AI evals. Here is what each one is built for, and the tradeoff that comes with it. Before you commit, it helps to know the levers that evaluate vendors past the marketing.
Cekura
What it does: Cekura runs end-to-end simulations and scores them, then keeps scoring once you go live. It covers workflow testing, infrastructure conditions like noise and interruptions, production monitoring, and red teaming.
Best for: Voice and chat teams that want pre-launch evals and voice observability in one platform.
The tradeoff: Cekura is built specifically for conversational AI. If your only need is scoring text completions for a non-conversational task, a general LLM eval tool is lighter.
Hamming
What it does: Hamming focuses on scale. It places thousands of concurrent simulated calls, tracks completion and error rates, and generates trust and safety reports.
Best for: Large call operations where peak-load reliability and governance come first.
The tradeoff: Hamming is strong on QA and scale. You pair it with your own agent platform, since it doesn't run the agent itself.
Coval
What it does: Coval brings simulation techniques from autonomous systems into voice. Teams define workflows, run thousands of virtual interactions, then monitor live calls for drift and policy violations.
Best for: Regulated enterprises that want large-scale simulation and live monitoring under one roof.
The tradeoff: Coval leans enterprise. Smaller teams may find the setup heavier than their stage needs.
Braintrust
What it does: Braintrust is a general LLM eval platform. It captures traces, builds datasets from production, and runs offline experiments and online scoring with LLM-as-a-judge.
Best for: Teams that already evaluate text LLMs on Braintrust and want to extend coverage to voice.
The tradeoff: Braintrust is general-purpose. Voice infrastructure testing, like interruption and noise handling, sits outside its core scope.
Langfuse
What it does: Langfuse is open-source observability. It traces calls, manages prompts, and supports model-based scoring, with self-hosting for teams that need data control.
Best for: Engineers who want open-source tracing they can host themselves.
The tradeoff: Langfuse is tracing-first. End-to-end conversation simulation usually means pairing it with a separate tool.
Which Voice AI Eval Platform Should You Choose?
The right platform depends on where your agent is and what you're most afraid of breaking.
Choose Cekura if: you build voice or chat agents and want simulation, infra testing, red teaming, and production monitoring in one place.
Choose Hamming if: you run massive call volumes and peak-load reliability is your top risk.
Choose Coval if: you operate in a regulated enterprise and need large-scale simulation plus live monitoring.
Choose Braintrust if: your team already evaluates text LLMs there, and voice is an extension of that work.
Choose Langfuse if: you want open-source, self-hosted tracing, and you'll pair it with a simulation tool.
Voice AI Eval Best Practices
These practices separate an eval that catches real failures from one that gives false confidence before live traffic finds the bugs.
- Build your suite from real transcripts: The inputs that break agents are messy. Think mid-sentence corrections, partial information, and off-script asks. Pull them from real calls, since your imagination won't generate the inputs that actually break things.
- Evaluate the whole conversation: An agent can score well on every turn and still fail the call when it loses context between them. Single-turn testing hides that break.
- Treat one LLM judge as a regression floor: It catches known failures when they recur. It misses cross-turn state. Back it with deterministic checks and human review on the calls it flags.
- Report latency as a distribution: P95 and P99 reveal the tail that averages bury. A clean average can still hide the two-second wait that loses 5% of callers.
- Keep eval infrastructure independent from your agent runtime: If you run scoring on the same stack that powers the agent, one outage can take down execution and measurement at once.
- Red-team across multiple turns: Cekura's data shows single-turn attacks succeed 19.5% of the time. Multi-turn red teaming attacks hit 92.7%. The dangerous failures hide across turns, so your red-team scenarios should too.
- Turn every production failure into a regression case: Each escalated or abandoned call is a scenario your pre-launch suite missed. Feed it back through your CI/CD pipeline so the same break never ships twice. This is the basis of auto-improving evals.
How Cekura Makes Voice AI Evals Easier
Running every method above by hand takes more engineering time than a launch deadline usually allows. Cekura sits on top of your existing stack and handles the eval infrastructure, so your team builds features instead of test harnesses.
Pre-production:
- Simulation at scale: Thousands of synthetic conversations run before launch, covering edge cases that surface only when real callers push the agent off-script.
- Auto-generated test cases: Cekura builds hundreds of scenarios from your agent's description and knowledge base, with six to seven assertions per test for precise pass or fail scoring.
- Multi-turn red teaming: Adversarial scenarios run across turns, where the dangerous jailbreaks actually live.
Infrastructure:
- Interruption and noise testing: Cekura scores how the agent handles barge-ins, background noise, and the pauses that get misread as a turn ending.
- Latency tracking: It pinpoints where slowdowns start in the pipeline so you know what to fix after each release.
Observability:
- Production call scoring: Every live call gets scored on the same criteria as your pre-launch suite, with alerts when quality drops.
- Conversation replay: When a live call breaks, replay that exact exchange against your updated agent to confirm the fix held.
- CI/CD integration: Every prompt edit or model swap runs your full scenario suite automatically before anything ships.
Native integrations work out of the box for Retell, VAPI, ElevenLabs, LiveKit, Pipecat, Bland, and more. You don't rebuild anything. You add a testing and monitoring layer on top of what you already have.
Plus, it's SOC 2-, HIPAA-, and GDPR-compliant for transcript redaction, role-based access, and audit trails.
Cekura works with teams like Twin Health and Lindy to ship voice AI evals that hold up in production. Book a demo to see how it tests your agent before your customers do.
Frequently Asked Questions
What is the difference between voice AI evals and standard LLM evals?
The main difference between voice AI evals and standard LLM evals is the audio pipeline and the multi-turn structure. Voice evals have to score speech recognition, latency, and interruption handling on top of the language model's output. They also score the full conversation arc, because voice failures often hide between turns.
Can you evaluate a voice agent with LLM-as-a-judge alone?
No, LLM-as-a-judge alone misses too many real failures to be your only eval. A June 2026 study found a deployed agent's built-in judge caught around 22% of systematic problems and flagged zero of 100 rounds in one batch, where humans found 23 defects. Use it as a regression floor, backed by deterministic checks and human review.
What metrics matter most for voice AI evals?
The metrics that matter most are word error rate under real noise and end-to-end latency at the P95 and P99 percentiles. Task completion, instruction following, and hallucination rate round out the picture. Latency and WER are where most production failures show up first.
How often should you run voice AI evals?
Run simulation and regression evals before every deployment that touches a prompt, model, or voice provider. Run production scoring continuously on live calls. Run load evals before any expected jump in call volume.