Top Tools for Evaluating Voice Agents in 2025

TLDR: Voice agent evaluation is no longer manual. In 2025, teams test voice agents using simulation-based conversations, audio-native metrics, and CI/CD workflows. Tools like LangWatch let you evaluate voice AI at scale without calling your agent dozens of times after every change.

Voice AI is no longer experimental. Companies now rely on voice agents to schedule appointments, resolve customer support calls, qualify sales leads, and handle regulated workflows. The hard part isn’t building voice agents anymore — it’s testing and validating them at scale.

Many teams still evaluate voice agents by manually calling them after each prompt or logic change. That approach works when an agent handles a handful of calls per day. It completely breaks down in production, where agents process thousands of calls daily, each with different accents, background noise, speaking styles, and emotional states.

Voice agents fail differently than text-based AI. A 200ms delay that’s unnoticeable in chat can derail a live phone call. An unfamiliar accent can cascade into repeated misunderstandings. Background noise can turn a simple task into a frustrating loop. These issues rarely show up in transcripts alone, you need audio-native testing and realistic caller behavior to catch them.

That’s why a new category of voice agent evaluation tools has emerged.

What is voice Agent Evaluation?

Voice agent evaluation is the structured process of testing, monitoring, and improving conversational AI systems that interact with users through spoken audio. It includes both:

• Offline evaluations: testing against curated datasets and simulated conversations before deployment.

• Online evaluations: measuring live performance and quality trends in production.

Voice adds complexity that text doesn’t have. Conversations happen in real time. Users interrupt, change topics mid-sentence, express frustration through tone, and speak over the agent. Background noise, accents, and connection issues all affect comprehension. Traditional transcript-based evaluation only tells part of the story.

A key practice in modern voice evaluation is simulation-based testing: instead of calling your voice agent 50 times manually every time you change a prompt or logic layer, you define realistic conversational scenarios and run them automatically with virtual users that speak, interrupt, ask follow-ups, and behave like real people. This approach lets you uncover edge cases and regressions at scale, without the cost and tedium of human testing. Frameworks like LangWatch Scenario let you simulate full multimodal conversations (audio-in and audio-out) across thousands of user behaviors before anything hits production.

Voice evaluation isn’t just about recognition accuracy, it’s about whether the agent handles timing, interruptions, goal completion, emotional tone, and environmental noise the way real callers expect.

Voice-only platforms vs. General AI Evaluation Platforms

Two main categories of tools now exist: voice-specialized platforms and general AI evaluation platforms with voice support. The real difference comes down to depth versus breadth.

Voice-Only Platforms

Tools like Roark, Hamming, Coval, and Evalion focus exclusively on voice.

They typically offer:

• Built-in simulation of accents, interruptions, and background noise

• Deep integrations with voice stacks like Vapi, Retell, LiveKit, and Pipecat

Tradeoff: If you’re also testing text agents, multimodal systems, or AI workflows beyond voice, you’ll need additional tooling.

General AI Evaluation Platforms

LangWatch began as a platform for LLM evaluation, observability, and agent testing — and has expanded to support voice, text, and multimodal AI in one system.

Prompt management, dataset versioning, experiment tracking, regression detection, and production monitoring all live in the same place. Voice simulation works via LangWatch' own framework, called: LangWatch Scenario

How we evaluated Voice Agent testing tools

Each platform was assessed using six weighted criteria:

1. Simulation capabilities (25%)
   Realistic multi-turn conversations, accent variation, interruptions, emotional behavior

2. Evaluation metrics (25%)
   Voice-specific metrics, custom scoring logic, audio attachment support

3. Production monitoring (20%)
   Live call tracking, alerting, trend analysis

4. Integration and workflow (15%)
   CI/CD compatibility, voice platform integrations, setup complexity

5. Scale and performance (10%)
   Scenario volume, execution speed, concurrency

6. Innovation (5%)
   Novel approaches to voice-specific testing challenges

The 6 Best Voice Agent Evaluation Tools in 2025

1. LangWatch

Best for: Teams that want 1 platform for evaluation, simulations and observability layer across voice, text, and multimodal AI

LangWatch is an AI evaluation and agent testing platform designed to connect voice agent testing directly to the rest of your AI development workflow. For voice systems, LangWatch acts as the evaluation backbone: managing datasets, running scorers, tracking regressions, and linking failures back to development decisions. Voice simulations are executed via Evalion, while LangWatch handles everything after the call.

Voice-specific capabilities:

• Debugging with real audio
  Attach raw audio files to traces and replay exactly what the agent heard during failures.

• Direct evaluation of audio models
  Works with real-time audio APIs to test tasks like intent detection, language classification, and instruction compliance on spoken input.

• Automated conversation simulation
  Evalion runs realistic calls where users interrupt, express frustration, or change goals mid-conversation. Results flow directly into LangWatch evaluations.

• Custom voice metrics
  Define scorers for latency thresholds, goal completion, CSAT, conversation flow, and compliance. Slice results by metadata to detect regressions..

Most voice testing platforms focus primarily on simulation. LangWatch also focusses on what happens next: Did the agent succeed? Is performance improving over time? Which scenarios are breaking after recent changes?

Pros

• Audio attachments make debugging concrete, not speculative

• Synthetic dataset generation supports multilingual testing from day one

• Flexible custom scorers for voice-specific quality signals

• Evalion integration delivers realistic simulation without custom tooling

• One workflow for voice, text, and multimodal agents

Pricing: Free tier / Pro $249 per month / Enterprise (custom)

2. Evalion

Best for: High-fidelity caller simulation with emotional personas

Evalion creates autonomous testing agents that behave like real callers — interrupting, hesitating, repeating themselves, and expressing frustration. Scenarios are normalized for comparison across runs and integrate natively with LangWatch.

Pros

• Emotionally realistic caller personas

• Autonomous multi-turn conversations

• Tight LangWatch integration

Cons

• Requires pairing with an evaluation platform like LangWatch

Pricing: Contact sales
Integrations: LangWatch

3. Hamming

Best for: Sress testing with regulatory requirements

Hamming runs thousands of concurrent test calls using AI-generated personas with varied accents, speaking speed, and patience levels. It excels at compliance-focused testing, simulating scenarios that could trigger PCI DSS or HIPAA violations.

Pros

• 500+ conversation paths tested simultaneously

• Strong multilingual and accent support

• Compliance-driven scenario design

Cons

• Best suited for well-defined, structured call flows

• Often paired with observability tool

Pricing: Contact sales

4. Coval

Best for: CI/CD-driven regression testing for voice agents

Coval applies testing techniques inspired by autonomous vehicle validation. Every prompt or workflow change triggers automated tests against large scenario sets. Failed production calls are automatically converted into new test cases.

Pros

• Scenario generation from transcripts and workflows

• CI/CD integration catches regressions pre-deploy

• Production failures feed back into testing

Cons

• Often paired with a separate observability platform

Pricing: Contact sales
Integrations: Retell, Pipecat

5. Roark

Best for: Production call analytics and replay-based debugging

Roark captures real production calls and lets teams replay them against updated agent logic. You hear the background noise, the hesitation, the tone — not just the transcript. Emotional signals are detected via Hume integration.

Pros

• Real call replay for post-mortem debugging

• 40+ built-in metrics including sentiment analysis

• One-click integrations with major voice stacks

• SOC 2 and HIPAA compliant

Cons

• Stronger on monitoring than pre-deployment testing

Pricing: $500 per month for 5,000 call minutes

Voice AI Evaluations Summary

Why LangWatch Works for Voice Agent Evaluation

LangWatch closes the loop between simulation, evaluation, and production monitoring. Audio attachments ensure debugging is grounded in reality. Evalion provides realistic callers without internal tooling. Custom scorers track the metrics that actually matter. And production failures continuously improve your test datasets.

The same workflow used to evaluate text agents and AI agents applies seamlessly to voice.

FAQs

What is voice agent evaluation?

Voice agent evaluation measures how well conversational AI handles spoken interactions. This includes simulating callers with different accents and emotional states, measuring response latency, tracking task success, and monitoring live performance. Unlike text evaluation, voice testing must account for audio quality, interruptions, and timing. LangWatch supports this with audio attachments, custom scorers, and simulation integrations.

How do I choose the right voice agent evaluation tool?

Three factors matter most:

1. Simulation quality: Accents, interruptions, emotional behavior

2. Metrics: Latency, goal completion, CSAT, compliance

3. Workflow integration: CI/CD, production feedback loops

LangWatch covers all three by combining evaluation, observability, and simulation in one workflow.

Is LangWatch better than Coval for voice agent testing?

They solve different problems. Coval focuses on automated regression testing triggered by code changes. LangWatch provides broader evaluation, observability, and scoring across voice, text, and multimodal systems. Many teams use Coval for simulation and LangWatch as the evaluation and tracking layer.

What’s the difference between voice evaluation and LLM observability?

Observability shows what happened — inputs, outputs, latency, cost. Evaluation determines whether the agent actually did the right thing. Voice evaluation answers questions like: Was the response timely? Did it resolve the caller’s issue? Did it follow instructions? LangWatch combines both perspectives.

How quickly can I see results?

Most teams run their first voice evaluation in under an hour. Define scenarios, connect audio input, add scorers, and run experiments. Results are immediately comparable across runs, with failures easy to inspect and replay.